def test__overscan_schange():
ccd_data = ccd_data_func()
old_data = ccd_data.copy()
new_data = subtract_overscan(ccd_data,
overscan=ccd_data[:, 1],
overscan_axis=0)
assert not np.allclose(old_data.data, new_data.data)
np.testing.assert_array_equal(old_data.data, ccd_data.data)
def test_subtract_overscan_fails():
ccd_data = ccd_data_func()
# Do we get an error if the *image* is neither CCDData nor an array?
with pytest.raises(TypeError):
subtract_overscan(3, np.zeros((5, 5)))
# Do we get an error if the *overscan* is not an image or an array?
with pytest.raises(TypeError):
subtract_overscan(np.zeros((10, 10)), 3, median=False, model=None)
# Do we get an error if we specify both overscan and fits_section?
with pytest.raises(TypeError):
subtract_overscan(ccd_data,
overscan=ccd_data[0:10],
fits_section='[1:10]')
# Do we raise an error if we specify neither overscan nor fits_section?
with pytest.raises(TypeError):
subtract_overscan(ccd_data)
# Does a fits_section which is not a string raise an error?
with pytest.raises(TypeError):
subtract_overscan(ccd_data, fits_section=5)
def test_subtract_overscan_model(transpose):
ccd_data = ccd_data_func()
# Create the overscan region
size = ccd_data.shape[0]
oscan_region = (slice(None), slice(0, 10))
science_region = (slice(None), slice(10, None))
yscan, xscan = np.mgrid[0:size, 0:size] / 10.0 + 300.0
if transpose:
oscan_region = oscan_region[::-1]
science_region = science_region[::-1]
scan = xscan
overscan_axis = 0
else:
overscan_axis = 1
scan = yscan
original_mean = ccd_data.data[science_region].mean()
ccd_data.data[oscan_region] = 0. # Only want overscan in that region
ccd_data.data = ccd_data.data + scan
ccd_data = subtract_overscan(ccd_data,
overscan=ccd_data[oscan_region],
overscan_axis=overscan_axis,
median=False,
model=models.Polynomial1D(2))
np.testing.assert_almost_equal(ccd_data.data[science_region].mean(),
original_mean)
# Set the overscan_axis explicitly to None, and let the routine
# figure it out.
ccd_data = subtract_overscan(ccd_data,
overscan=ccd_data[oscan_region],
overscan_axis=None,
median=False,
model=models.Polynomial1D(2))
np.testing.assert_almost_equal(ccd_data.data[science_region].mean(),
original_mean)
def test_subtract_overscan(median, transpose, data_rectangle):
ccd_data = ccd_data_func()
# Make data non-square if desired
if data_rectangle:
ccd_data.data = ccd_data.data[:, :-30]
# Create the overscan region
oscan = 300.
oscan_region = (slice(None), slice(0, 10)) # Indices 0 through 9
fits_section = '[1:10, :]'
science_region = (slice(None), slice(10, None))
overscan_axis = 1
if transpose:
# Put overscan in first axis, not second, a test for #70
oscan_region = oscan_region[::-1]
fits_section = '[:, 1:10]'
science_region = science_region[::-1]
overscan_axis = 0
ccd_data.data[oscan_region] = oscan
# Add a fake sky background so the "science" part of the image has a
# different average than the "overscan" part.
sky = 10.
original_mean = ccd_data.data[science_region].mean()
ccd_data.data[science_region] += oscan + sky
# Test once using the overscan argument to specify the overscan region
ccd_data_overscan = subtract_overscan(ccd_data,
overscan=ccd_data[oscan_region],
overscan_axis=overscan_axis,
median=median,
model=None)
# Is the mean of the "science" region the sum of sky and the mean the
# "science" section had before backgrounds were added?
np.testing.assert_almost_equal(
ccd_data_overscan.data[science_region].mean(), sky + original_mean)
# Is the overscan region zero?
assert (ccd_data_overscan.data[oscan_region] == 0).all()
# Now do what should be the same subtraction, with the overscan specified
# with the fits_section
ccd_data_fits_section = subtract_overscan(ccd_data,
overscan_axis=overscan_axis,
fits_section=fits_section,
median=median,
model=None)
# Is the mean of the "science" region the sum of sky and the mean the
# "science" section had before backgrounds were added?
np.testing.assert_almost_equal(
ccd_data_fits_section.data[science_region].mean(), sky + original_mean)
# Is the overscan region zero?
assert (ccd_data_fits_section.data[oscan_region] == 0).all()
# Do both ways of subtracting overscan give exactly the same result?
np.testing.assert_array_equal(ccd_data_overscan[science_region],
ccd_data_fits_section[science_region])
# Set overscan_axis to None, and let the routine figure out the axis.
# This should lead to the same results as before.
ccd_data_overscan_auto = subtract_overscan(ccd_data,
overscan_axis=None,
overscan=ccd_data[oscan_region],
median=median,
model=None)
np.testing.assert_almost_equal(
ccd_data_overscan_auto.data[science_region].mean(),
sky + original_mean)
# Use overscan_axis=None with a FITS section
ccd_data_fits_section_overscan_auto = subtract_overscan(
ccd_data,
overscan_axis=None,
fits_section=fits_section,
median=median,
model=None)
np.testing.assert_almost_equal(
ccd_data_fits_section_overscan_auto.data[science_region].mean(),
sky + original_mean)
# Overscan_axis should be 1 for a square overscan region
# This test only works for a non-square data region, but the
# default has the wrong axis.
if data_rectangle:
ccd_data.data = ccd_data.data.T
oscan_region = (slice(None), slice(0, -30))
science_region = (slice(None), slice(-30, None))
ccd_data_square_overscan_auto = subtract_overscan(
ccd_data,
overscan_axis=None,
overscan=ccd_data[oscan_region],
median=median,
model=None)
ccd_data_square = subtract_overscan(ccd_data,
overscan_axis=1,
overscan=ccd_data[oscan_region],
median=median,
model=None)
np.testing.assert_allclose(ccd_data_square_overscan_auto,
ccd_data_square)
