def test_bad_pixel_map(self):
bp_map = BadPixelMapModule(name_in="bp_map",
dark_in_tag="dark",
flat_in_tag="flat",
bp_map_out_tag="bp_map",
dark_threshold=0.99,
flat_threshold=-0.99)
self.pipeline.add_module(bp_map)
self.pipeline.run()
storage = DataStorage(self.test_dir + "/PynPoint_database.hdf5")
storage.open_connection()
data = storage.m_data_bank["bp_map"]
assert data[0, 0] == 1.
assert data[30, 30] == 1.
assert data[10, 10] == 0.
assert data[12, 12] == 0.
assert data[14, 14] == 0.
assert data[20, 20] == 0.
assert data[22, 22] == 0.
assert data[24, 24] == 0.
assert np.mean(data) == 0.9993
storage.close_connection()
def test_create_storage_without_existing_database(self):
storage = DataStorage(self.test_data)
storage.open_connection()
storage.m_data_bank["data"] = [0, 1, 2, 5, 7]
assert storage.m_data_bank["data"][2] == 2
assert storage.m_data_bank.keys() == ["data", ]
storage.close_connection()
os.remove(self.test_data)
def test_open_close_connection(self):
storage = DataStorage(self.test_data)
storage.open_connection()
assert storage.m_open is True
storage.open_connection()
assert storage.m_open is True
storage.close_connection()
assert storage.m_open is False
storage.close_connection()
assert storage.m_open is False
os.remove(self.test_data)
def test_dark_and_flat_calibration(self):
dark = DarkCalibrationModule(name_in="dark",
image_in_tag="images",
dark_in_tag="dark",
image_out_tag="dark_cal")
self.pipeline.add_module(dark)
flat = FlatCalibrationModule(name_in="flat",
image_in_tag="dark_cal",
flat_in_tag="flat",
image_out_tag="flat_cal")
self.pipeline.add_module(flat)
self.pipeline.run()
storage = DataStorage(self.test_dir + "/PynPoint_database.hdf5")
storage.open_connection()
data = storage.m_data_bank["dark"]
assert np.allclose(data[0, 10, 10],
3.528694163309295e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
7.368663496379876e-07,
rtol=limit,
atol=0.)
data = storage.m_data_bank["flat"]
assert np.allclose(data[0, 10, 10],
-0.0004053528990466237,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
-4.056978234798532e-07,
rtol=limit,
atol=0.)
storage.close_connection()
def test_bad_pixel_sigma_filter(self):
sigma = BadPixelSigmaFilterModule(name_in="sigma",
image_in_tag="images",
image_out_tag="sigma",
box=9,
sigma=5,
iterate=1)
self.pipeline.add_module(sigma)
self.pipeline.run()
storage = DataStorage(self.test_dir + "/PynPoint_database.hdf5")
storage.open_connection()
data = storage.m_data_bank["sigma"]
assert np.allclose(data[0, 0, 0],
0.00032486907273264834,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 10, 10],
0.025022559679385093,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 20, 20],
0.024962143884217046,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
6.721637736047109e-07,
rtol=limit,
atol=0.)
storage.close_connection()
def test_bad_pixel_interpolation(self):
interpolation = BadPixelInterpolationModule(
name_in="interpolation",
image_in_tag="images",
bad_pixel_map_tag="bp_map",
image_out_tag="interpolation",
iterations=100)
self.pipeline.add_module(interpolation)
self.pipeline.run()
storage = DataStorage(self.test_dir + "/PynPoint_database.hdf5")
storage.open_connection()
data = storage.m_data_bank["interpolation"]
assert np.allclose(data[0, 0, 0],
0.00032486907273264834,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 10, 10],
1.0139222106683477e-05,
rtol=limit,
atol=0.)
assert np.allclose(data[0, 20, 20],
-4.686852973820094e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
3.0499629451215465e-07,
rtol=limit,
atol=0.)
storage.close_connection()
def test_fake_planet(self):
read = FitsReadingModule(name_in="read",
image_tag="read")
self.pipeline.add_module(read)
angle = AngleInterpolationModule(name_in="angle",
data_tag="read")
self.pipeline.add_module(angle)
fake = FakePlanetModule(position=(0.5, 90.),
magnitude=5.,
psf_scaling=1.,
interpolation="spline",
name_in="fake",
image_in_tag="read",
psf_in_tag="read",
image_out_tag="fake",
verbose=True)
self.pipeline.add_module(fake)
simplex = SimplexMinimizationModule(position=(31., 49.),
magnitude=5.,
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="sum",
aperture=0.05,
sigma=0.027,
tolerance=0.1,
pca_number=2,
cent_size=None,
edge_size=None,
extra_rot=0.)
self.pipeline.add_module(simplex)
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)
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)
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()
storage = DataStorage(self.test_dir+"/PynPoint_database.hdf5")
storage.open_connection()
data = storage.m_data_bank["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.)
data = storage.m_data_bank["header_read/PARANG"]
assert data[5] == 2.7777777777777777
data = storage.m_data_bank["fake"]
assert np.allclose(data[0, 49, 31], 0.00036532633147006946, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0001012983225928772, rtol=limit, atol=0.)
data = storage.m_data_bank["simplex_res"]
assert np.allclose(data[46, 49, 31], 3.718481593648487e-05, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), -2.8892749617545238e-08, rtol=limit, atol=0.)
data = storage.m_data_bank["flux_position"]
assert np.allclose(data[46, 0], 31.276994533457994, rtol=limit, atol=0.)
assert np.allclose(data[46, 1], 50.10345749706295, rtol=limit, atol=0.)
assert np.allclose(data[46, 2], 0.5055288651354779, rtol=limit, atol=0.)
assert np.allclose(data[46, 3], 89.6834045889695, rtol=limit, atol=0.)
assert np.allclose(data[46, 4], 4.997674024675655, rtol=limit, atol=0.)
data = storage.m_data_bank["res_mean"]
assert np.allclose(data[0, 49, 31], 9.258255068620805e-05, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), -2.610863424405134e-08, rtol=limit, atol=0.)
data = storage.m_data_bank["snr_fpf"]
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], 11.775360946367874, rtol=limit, atol=0.)
assert np.allclose(data[0, 5], 2.9838031156970146e-08, rtol=limit, atol=0.)
data = storage.m_data_bank["photometry"]
assert np.allclose(data[0][0], 0.983374353660573, rtol=limit, atol=0.)
assert np.allclose(data[39][0], 0.9841484973083519, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.9835085649488583, rtol=limit, atol=0.)
storage.close_connection()
def test_star_alignment(self):
read = FitsReadingModule(name_in="read",
image_tag="read")
self.pipeline.add_module(read)
extraction = StarExtractionModule(name_in="extract",
image_in_tag="read",
image_out_tag="extract",
image_size=0.6,
fwhm_star=0.1,
position=None)
self.pipeline.add_module(extraction)
align = StarAlignmentModule(name_in="align",
image_in_tag="extract",
ref_image_in_tag=None,
image_out_tag="align",
accuracy=10,
resize=2)
self.pipeline.add_module(align)
shift = ShiftImagesModule((6., 4.),
name_in="shift",
image_in_tag="align",
image_out_tag="shift")
self.pipeline.add_module(shift)
center = StarCenteringModule(name_in="center",
image_in_tag="shift",
image_out_tag="center",
mask_out_tag=None,
fit_out_tag="center_fit",
method="full",
interpolation="spline",
radius=0.1,
sign="positive",
guess=(6., 4., 1., 1., 1., 0.))
self.pipeline.add_module(center)
self.pipeline.run()
storage = DataStorage(self.test_dir+"/PynPoint_database.hdf5")
storage.open_connection()
data = storage.m_data_bank["read"]
assert np.allclose(data[0, 10, 10], 0.00012958496246258364, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 9.832838021311831e-05, rtol=limit, atol=0.)
data = storage.m_data_bank["extract"]
assert np.allclose(data[0, 10, 10], 0.05304008435511765, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0020655767159466613, rtol=limit, atol=0.)
data = storage.m_data_bank["header_extract/STAR_POSITION"]
assert data[10, 0] == data[10, 1] == 75
data = storage.m_data_bank["shift"]
assert np.allclose(data[0, 10, 10], -4.341611534220891e-05, rtol=limit, atol=0.)
assert np.allclose(np.mean(data), 0.0005164420068450968, rtol=limit, atol=0.)
data = storage.m_data_bank["center"]
assert np.allclose(data[0, 10, 10], 4.128859892625027e-05, rtol=1e-4, atol=0.)
assert np.allclose(np.mean(data), 0.0005163806188663894, rtol=1e-7, atol=0.)
storage.close_connection()
def test_contrast_curve(self):
read = FitsReadingModule(name_in="read", image_tag="read")
self.pipeline.add_module(read)
angle = AngleInterpolationModule(name_in="angle", data_tag="read")
self.pipeline.add_module(angle)
contrast = ContrastCurveModule(name_in="contrast",
image_in_tag="read",
psf_in_tag="read",
pca_out_tag="pca",
contrast_out_tag="limits",
separation=(0.5, 0.6, 0.1),
angle=(0., 360., 180.),
magnitude=(7.5, 1.),
sigma=5.,
accuracy=1e-1,
psf_scaling=1.,
aperture=0.1,
ignore=True,
pca_number=15,
norm=False,
cent_size=None,
edge_size=None,
extra_rot=0.)
self.pipeline.add_module(contrast)
self.pipeline.run()
storage = DataStorage(self.test_dir + "/PynPoint_database.hdf5")
storage.open_connection()
data = storage.m_data_bank["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.)
data = storage.m_data_bank["header_read/PARANG"]
assert data[5] == 2.7777777777777777
data = storage.m_data_bank["pca"]
assert np.allclose(data[9, 68, 49],
5.707647718560735e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
-3.66890878538392e-08,
rtol=limit,
atol=0.)
data = storage.m_data_bank["pca"]
assert np.allclose(data[21, 31, 50],
5.4392925807364694e-05,
rtol=limit,
atol=0.)
assert np.allclose(np.mean(data),
-3.668908785383954e-08,
rtol=limit,
atol=0.)
storage.close_connection()