def test_add_modules(self):
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
# --- Reading Modules ---
# default location
reading = FitsReadingModule(name_in="reading",
input_dir=None,
image_tag="im_arr")
pipeline.add_module(reading)
# no default location
reading2 = FitsReadingModule(name_in="reading2",
input_dir=self.test_dir,
image_tag="im_arr2")
pipeline.add_module(reading2)
# --- Processing Module ---
process = BadPixelSigmaFilterModule(image_in_tag="im_arr")
pipeline.add_module(process)
# --- Writing Module ---
# default location
write = FitsWritingModule(name_in="writing",
file_name="result.fits",
data_tag="im_arr")
pipeline.add_module(write)
# no default location
write2 = FitsWritingModule(name_in="writing2",
file_name="result.fits",
data_tag="im_arr",
output_dir=self.test_dir)
pipeline.add_module(write2)
with pytest.warns(UserWarning):
pipeline.add_module(write2)
pipeline.run()
os.remove(self.test_dir+"result.fits")
os.remove(self.test_data)
# --- Reading Module ---
# run_module
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
reading = FitsReadingModule(name_in="reading",
input_dir=None,
image_tag="im_arr")
pipeline.add_module(reading)
pipeline.run_module("reading")
os.remove(self.test_data)
def test_run_non_valid_module_list(self):
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
# --- Reading Modules ---
reading = FitsReadingModule(name_in="reading")
pipeline.add_module(reading)
# --- Processing Module ---
process = BadPixelSigmaFilterModule(name_in="filter", image_in_tag="im_list")
pipeline.add_module(process)
# --- Writing Module ---
write = FitsWritingModule(name_in="writing",
file_name="result.fits",
data_tag="im_list")
pipeline.add_module(write)
with pytest.raises(AttributeError):
pipeline.run()
with pytest.raises(AttributeError):
pipeline.run_module("filter")
with pytest.raises(AttributeError):
pipeline.run_module("writing")
assert pipeline.validate_pipeline_module("test") is None
os.remove(self.test_data)
def test_run_non_existing_module(self):
pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
with pytest.warns(UserWarning):
pipeline.run_module("test")
os.remove(self.test_data)
class TestEndToEnd(object):
def setup(self):
self.test_dir = os.path.dirname(__file__) + "/"
self.pipeline = Pypeline(self.test_dir, self.test_dir, self.test_dir)
def test_read(self):
read_fits = FitsReadingModule(name_in="read_fits",
image_tag="im",
overwrite=True)
self.pipeline.add_module(read_fits)
self.pipeline.run_module("read_fits")
data = self.pipeline.get_data("im")
assert np.allclose(data[0, 0, 0],
0.00032486907273264834,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.4518306864680034e-05,
rtol=limit,
atol=0.)
assert data.shape == (82, 102, 100)
def test_remove_last(self):
remove_last = RemoveLastFrameModule(name_in="remove_last",
image_in_tag="im",
image_out_tag="im_last")
self.pipeline.add_module(remove_last)
self.pipeline.run_module("remove_last")
data = self.pipeline.get_data("im_last")
assert np.allclose(data[0, 0, 0],
0.00032486907273264834,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.9365399524407205e-05,
rtol=limit,
atol=0.)
assert data.shape == (78, 102, 100)
def test_parang(self):
angle = AngleInterpolationModule(name_in="angle", data_tag="im_last")
self.pipeline.add_module(angle)
self.pipeline.run_module("angle")
files = self.pipeline.get_attribute("im_last", "FILES", static=False)
parang = self.pipeline.get_attribute("im_last", "PARANG", static=False)
assert files[0] == self.test_dir + 'adi01.fits'
assert parang[1] == 1.1904761904761905
def test_cut_lines(self):
cut_lines = RemoveLinesModule(lines=(0, 0, 0, 2),
name_in="cut_lines",
image_in_tag="im_last",
image_out_tag="im_cut")
self.pipeline.add_module(cut_lines)
self.pipeline.run_module("cut_lines")
data = self.pipeline.get_data("im_cut")
assert np.allclose(data[0, 0, 0],
0.00032486907273264834,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00010141595132969683,
rtol=limit,
atol=0.)
assert data.shape == (78, 100, 100)
def test_background(self):
background = MeanBackgroundSubtractionModule(shift=None,
cubes=1,
name_in="background",
image_in_tag="im_cut",
image_out_tag="im_bg")
self.pipeline.add_module(background)
self.pipeline.run_module("background")
data = self.pipeline.get_data("im_bg")
assert np.allclose(data[0, 0, 0],
0.00037132392435389595,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
2.3675404363850964e-07,
rtol=limit,
atol=0.)
assert data.shape == (78, 100, 100)
def test_bad_pixel(self):
bad_pixel = BadPixelSigmaFilterModule(name_in="bad_pixel",
image_in_tag="im_bg",
image_out_tag="im_bp",
box=9,
sigma=8,
iterate=3)
self.pipeline.add_module(bad_pixel)
self.pipeline.run_module("bad_pixel")
data = self.pipeline.get_data("im_bp")
assert np.allclose(data[0, 0, 0],
0.00037132392435389595,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
2.3675404363850964e-07,
rtol=limit,
atol=0.)
assert data.shape == (78, 100, 100)
def test_star(self):
star = StarExtractionModule(name_in="star",
image_in_tag="im_bp",
image_out_tag="im_star",
image_size=1.08,
fwhm_star=0.0108)
self.pipeline.add_module(star)
self.pipeline.run_module("star")
data = self.pipeline.get_data("im_star")
assert np.allclose(data[0, 0, 0],
0.00018025424208141221,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
0.00063151691905138636,
rtol=limit,
atol=0.)
assert data.shape == (78, 40, 40)
def test_center(self):
center = StarAlignmentModule(name_in="center",
image_in_tag="im_star",
ref_image_in_tag=None,
image_out_tag="im_center",
interpolation="spline",
accuracy=10,
resize=5)
self.pipeline.add_module(center)
self.pipeline.run_module("center")
data = self.pipeline.get_data("im_center")
assert np.allclose(data[1, 0, 0],
1.2113798549047296e-06,
rtol=limit,
atol=0.)
assert np.allclose(data[16, 0, 0],
1.0022456564129139e-05,
rtol=limit,
atol=0.)
assert np.allclose(data[50, 0, 0],
1.7024977291686637e-06,
rtol=limit,
atol=0.)
assert np.allclose(data[67, 0, 0],
7.8143774182171561e-07,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
2.5260676762055473e-05,
rtol=limit,
atol=0.)
assert data.shape == (78, 200, 200)
def test_remove_frames(self):
remove_frames = RemoveFramesModule(frames=(0, 15, 49, 66),
name_in="remove_frames",
image_in_tag="im_center",
selected_out_tag="im_remove",
removed_out_tag=None)
self.pipeline.add_module(remove_frames)
self.pipeline.run_module("remove_frames")
data = self.pipeline.get_data("im_remove")
assert np.allclose(data[0, 0, 0],
1.2113798549047296e-06,
rtol=limit,
atol=0.)
assert np.allclose(data[14, 0, 0],
1.0022456564129139e-05,
rtol=limit,
atol=0.)
assert np.allclose(data[47, 0, 0],
1.7024977291686637e-06,
rtol=limit,
atol=0.)
assert np.allclose(data[63, 0, 0],
7.8143774182171561e-07,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
2.5255308248050269e-05,
rtol=limit,
atol=0.)
assert data.shape == (74, 200, 200)
def test_subset(self):
subset = StackAndSubsetModule(name_in="subset",
image_in_tag="im_remove",
image_out_tag="im_subset",
random=37,
stacking=2)
self.pipeline.add_module(subset)
self.pipeline.run_module("subset")
data = self.pipeline.get_data("im_subset")
assert np.allclose(data[0, 0, 0],
-1.9081971570461925e-06,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
2.5255308248050275e-05,
rtol=limit,
atol=0.)
assert data.shape == (37, 200, 200)
def test_pca(self):
pca = PSFSubtractionModule(name_in="pca",
pca_number=2,
images_in_tag="im_subset",
reference_in_tag="im_subset",
res_arr_out_tag="res_arr",
res_arr_rot_out_tag="res_rot",
res_mean_tag="res_mean",
res_median_tag="res_median",
res_var_tag="res_var",
res_rot_mean_clip_tag="res_rot_mean_clip",
extra_rot=0.0,
cent_size=0.1)
self.pipeline.add_module(pca)
self.pipeline.run_module("pca")
data = self.pipeline.get_data("res_mean")
assert np.allclose(data[154, 99],
0.0004308570688425797,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
9.372451154992271e-08,
rtol=limit,
atol=0.)
assert data.shape == (200, 200)
