def test_weight_type(nircam_rate, _jail):
"""Check that weight_type of exptime and ivm work"""
im1 = AssignWcsStep.call(nircam_rate, sip_approx=False)
im1.var_rnoise[:] = 0
im2 = im1.copy()
im3 = im1.copy()
im1.data += 10
im2.data += 5
im3.data += 5
im1.var_rnoise += (1 / 10)
im2.var_rnoise += (1 / 5)
im3.var_rnoise += (1 / 5)
im2.meta.observation.sequence_id = "2"
im3.meta.observation.sequence_id = "3"
c = ModelContainer([im1, im2, im3])
assert len(c.group_names) == 3
result1 = ResampleStep.call(c, weight_type="ivm", blendheaders=False, save_results=True)
# assert_allclose(result1.data, result2.data)
# assert_allclose(result1.wht, result2.wht)
assert_allclose(result1.data[100:105, 100:105], 7.5, rtol=1e-2)
assert_allclose(result1.wht[100:105, 100:105], 20, rtol=1e-2)
result2 = ResampleStep.call(c, weight_type="exptime", blendheaders=False)
assert_allclose(result2.data[100:105, 100:105], 7.5, rtol=1e-2)
assert_allclose(result2.wht[100:105, 100:105], 20, rtol=1e-2)
def test_resample_undefined_variance(nircam_rate, shape):
"""Test that resampled variance and error arrays are computed properly"""
im = AssignWcsStep.call(nircam_rate)
im.var_rnoise = np.ones(shape, dtype=im.var_rnoise.dtype.type)
im.var_poisson = np.ones(shape, dtype=im.var_poisson.dtype.type)
im.var_flat = np.ones(shape, dtype=im.var_flat.dtype.type)
im.meta.filename = "foo.fits"
c = ModelContainer([im])
ResampleStep.call(c, blendheaders=False)
def test_pixel_scale_ratio_imaging(nircam_rate, ratio):
im = AssignWcsStep.call(nircam_rate, sip_approx=False)
im.data += 5
result1 = ResampleStep.call(im)
result2 = ResampleStep.call(im, pixel_scale_ratio=ratio)
assert_allclose(np.array(result1.data.shape), np.array(result2.data.shape) * ratio, rtol=1, atol=1)
# Avoid edge effects; make sure data values are identical for surface brightness data
assert np.mean(result1.data[10:-10, 10:-10]) == np.mean(result2.data[10:-10, 10:-10])
# Make sure the photometry keywords describing the solid angle of a pixel
# are updated
area1 = result1.meta.photometry.pixelarea_steradians
area2 = result2.meta.photometry.pixelarea_steradians
assert_allclose(area1 * ratio**2, area2, rtol=1e-6)
def test_custom_wcs_resample_imaging(nircam_rate, ratio, rotation, crpix,
crval, shape):
im = AssignWcsStep.call(nircam_rate, sip_approx=False)
im.data += 5
result = ResampleStep.call(im,
output_shape=shape,
crpix=crpix,
crval=crval,
rotation=rotation,
pixel_scale_ratio=ratio)
t = result.meta.wcs.forward_transform
# test rotation
pc = t['pc_rotation_matrix'].matrix.value
orientation = np.rad2deg(np.arctan2(pc[0, 1], pc[1, 1]))
assert np.allclose(rotation, orientation)
# test CRPIX
assert np.allclose((-t['crpix1'].offset.value, -t['crpix2'].offset.value),
crpix)
# test CRVAL
assert np.allclose(t(*crpix), crval)
# test output image shape
assert result.data.shape == shape[::-1]
def test_resample_variance(nircam_rate, n_images):
"""Test that resampled variance and error arrays are computed properly"""
err = 0.02429
var_rnoise = 0.00034
var_poisson = 0.00025
im = AssignWcsStep.call(nircam_rate)
im.var_rnoise += var_rnoise
im.var_poisson += var_poisson
im.err += err
im.meta.filename = "foo.fits"
c = ModelContainer()
for n in range(n_images):
c.append(im.copy())
result = ResampleStep.call(c, blendheaders=False)
# Verify that the combined uncertainty goes as 1 / sqrt(N)
assert_allclose(result.err[5:-5, 5:-5].mean(),
err / np.sqrt(n_images),
atol=1e-5)
assert_allclose(result.var_rnoise[5:-5, 5:-5].mean(),
var_rnoise / n_images,
atol=1e-7)
assert_allclose(result.var_poisson[5:-5, 5:-5].mean(),
var_poisson / n_images,
atol=1e-7)
def test_image(asnfile):
'''Test to single FITS input.'''
with open(asnfile) as json_data:
d = json.load(json_data)
members = d['products'][0]['members'][0]['expname']
wcs = AssignWcsStep.call(members)
resample = ResampleStep.call(wcs)
resample.save(asnfile[:5] + '_singleimage_resample.fits')
def test_kernel(asnfile, kernel):
'''Test to check kernel.'''
calib = ResampleStep.call(asnfile,
kernel=kernel,
output_file=asnfile[:5] + '_kernel' +
str(kernel) + '_resample.fits')
img3 = calwebb_image3.Image3Pipeline(config_file='calwebb_image2.cfg')
img3.tweakreg.skip = True
img3.resample.kernel = kernel
img3.output_file = asnfile[:5] + '_kernel' + str(kernel) + '_resample.fits'
img3.run(asnfile)
def test_wcs_keywords(nircam_rate):
# make sure certain wcs keywords are removed after resample
im = AssignWcsStep.call(nircam_rate)
result = ResampleStep.call(im)
assert result.meta.wcsinfo.v2_ref is None
assert result.meta.wcsinfo.v3_ref is None
assert result.meta.wcsinfo.ra_ref is None
assert result.meta.wcsinfo.dec_ref is None
assert result.meta.wcsinfo.roll_ref is None
assert result.meta.wcsinfo.v3yangle is None
assert result.meta.wcsinfo.vparity is None
def test_custom_wcs_pscale_resample_imaging(nircam_rate, ratio):
im = AssignWcsStep.call(nircam_rate, sip_approx=False)
im.data += 5
fiducial = compute_fiducial([im.meta.wcs])
input_scale = compute_scale(wcs=im.meta.wcs, fiducial=fiducial)
result = ResampleStep.call(im,
pixel_scale_ratio=ratio,
pixel_scale=3600 * input_scale * 0.75)
output_scale = compute_scale(wcs=result.meta.wcs, fiducial=fiducial)
# test scales are close
assert np.allclose(output_scale, input_scale * 0.75)
def test_sip_coeffs_do_not_propagate(nircam_rate):
im = AssignWcsStep.call(nircam_rate, sip_degree=2)
# Check some SIP keywords produced above
assert im.meta.wcsinfo.cd1_1 is not None
assert im.meta.wcsinfo.ctype1 == "RA---TAN-SIP"
# Make sure no PC matrix stuff is there
assert im.meta.wcsinfo.pc1_1 is None
result = ResampleStep.call(im)
# Verify that SIP-related keywords do not propagate to resampled output
assert result.meta.wcsinfo.cd1_1 is None
assert result.meta.wcsinfo.ctype1 == "RA---TAN"
# Make sure we have a PC matrix
assert result.meta.wcsinfo.pc1_1 is not None