def test_nirspec_nrs1_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(*self.test_dir,
'jw00023001001_01101_00001_NRS1_ramp_fit.fits')
ref_file = self.get_data(*self.ref_loc,
'jw00023001001_01101_00001_NRS1_ramp_fit_assign_wcs.fits')
result = AssignWcsStep.call(input_file, save_results=True, suffix='assign_wcs')
result.close()
im = ImageModel(result.meta.filename)
imref = ImageModel(ref_file)
for slit in ['S200A1', 'S200A2', 'S400A1', 'S1600A1']:
w = nirspec.nrs_wcs_set_input(im, slit)
grid = grid_from_bounding_box(w.bounding_box)
ra, dec, lam = w(*grid)
wref = nirspec.nrs_wcs_set_input(imref, slit)
raref, decref, lamref = wref(*grid)
assert_allclose(ra, raref, equal_nan=True)
assert_allclose(dec, decref, equal_nan=True)
assert_allclose(lam, lamref, equal_nan=True)
def test_nirspec_ifu_wcs(envopt, _jail, test_id, input_file, truth_file):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
del test_id
input_file = get_bigdata('jwst-pipeline', envopt,
'nirspec', 'test_wcs', 'nrs1-ifu', input_file)
truth_file = get_bigdata('jwst-pipeline', envopt,
'nirspec', 'test_wcs', 'nrs1-ifu', 'truth', truth_file)
result = AssignWcsStep.call(input_file, save_results=True, suffix='assign_wcs')
result.close()
im = ImageModel(result.meta.filename)
imref = ImageModel(truth_file)
w = nirspec.nrs_wcs_set_input(im, 0)
grid = grid_from_bounding_box(w.bounding_box)
ra, dec, lam = w(*grid)
wref = nirspec.nrs_wcs_set_input(imref, 0)
raref, decref, lamref = wref(*grid)
# equal_nan is used here as many of the entries are nan.
# The domain is defined but it is only a few entries in there that are valid
# as it is a curved narrow slit.
assert_allclose(ra, raref, equal_nan=True)
assert_allclose(dec, decref, equal_nan=True)
assert_allclose(lam, lamref, equal_nan=True)
def test_master_background_userbg(_jail, user_background):
"""Verify data can run through the step with a user-supplied background"""
image = datamodels.ImageModel((10, 10))
image.meta.instrument.name = 'MIRI'
image.meta.instrument.detector = 'MIRIMAGE'
image.meta.exposure.type = 'MIR_LRS-FIXEDSLIT'
image.meta.observation.date = '2018-01-01'
image.meta.observation.time = '00:00:00'
image.meta.subarray.xstart = 1
image.meta.subarray.ystart = 1
image.meta.wcsinfo.v2_ref = 0
image.meta.wcsinfo.v3_ref = 0
image.meta.wcsinfo.roll_ref = 0
image.meta.wcsinfo.ra_ref = 0
image.meta.wcsinfo.dec_ref = 0
image = AssignWcsStep.call(image)
# Run with a user-supplied background and verify this is recorded in header
result = MasterBackgroundStep.call(image, user_background=user_background)
collect_pipeline_cfgs('./config')
result = MasterBackgroundStep.call(
image,
config_file='config/master_background.cfg',
user_background=user_background,
)
# For inputs that are not files, the following should be true
assert type(image) is type(result)
assert result is not image
assert result.meta.cal_step.master_background == 'COMPLETE'
assert result.meta.background.master_background_file == 'user_background.fits'
def test_miri_ifu_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(self.test_dir,
'jw00024001001_01101_00001_MIRIFUSHORT_uncal_MiriSloperPipeline.fits')
ref_file = self.get_data(os.path.join(*self.ref_loc),
'jw00024001001_01101_00001_MIRIFUSHORT_assign_wcs.fits')
result = AssignWcsStep.call(input_file)
output_file = result.meta.filename
result.save(output_file)
result.close()
im = ImageModel(output_file)
imref = ImageModel(ref_file)
# Get the region file
region = RegionsModel(crds_client.get_reference_file(im, 'regions'))
# inputs
shape = region.regions.shape
y, x = np.mgrid[ : shape[0], : shape[1]]
# Get indices where pixels == 0. These should be NaNs in the output.
ind_zeros = region.regions == 0
ra, dec, lam = im.meta.wcs(x, y)
raref, decref, lamref = imref.meta.wcs(x, y)
utils.assert_allclose(ra, raref, equal_nan=True)
utils.assert_allclose(dec, decref, equal_nan=True)
utils.assert_allclose(lam, lamref, equal_nan=True)
# Test that we got NaNs at ind_zero
assert(np.isnan(ra).nonzero()[0] == ind_zeros.nonzero()[0]).all()
assert(np.isnan(ra).nonzero()[1] == ind_zeros.nonzero()[1]).all()
# Test the inverse transform
x1, y1 = im.meta.wcs.backward_transform(ra, dec, lam)
assert(np.isnan(x1).nonzero()[0] == ind_zeros.nonzero()[0]).all()
assert (np.isnan(x1).nonzero()[1] == ind_zeros.nonzero()[1]).all()
# Also run a smoke test with values outside the region.
dec[100][200] = -80
ra[100][200] = 7
lam[100][200] = 15
x2, y2 = im.meta.wcs.backward_transform(ra, dec, lam)
assert np.isnan(x2[100][200])
assert np.isnan(x2[100][200])
def test_miri_ifu_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(self.test_dir,
'jw00024001001_01101_00001_MIRIFUSHORT_uncal_MiriSloperPipeline.fits')
result = AssignWcsStep.call(input_file, save_results=True)
# Get the region file
region = RegionsModel(crds_client.get_reference_file(result, 'regions'))
# inputs
x, y = grid_from_bounding_box(result.meta.wcs.bounding_box)
# Get indices where pixels == 0. These should be NaNs in the output.
ind_zeros = region.regions == 0
cwd = os.path.abspath('.')
os.makedirs('truth', exist_ok=True)
os.chdir('truth')
truth_file = self.get_data(*self.ref_loc,
'jw00024001001_01101_00001_MIRIFUSHORT_assign_wcs.fits')
os.chdir(cwd)
truth = ImageModel(truth_file)
ra, dec, lam = result.meta.wcs(x, y)
raref, decref, lamref = truth.meta.wcs(x, y)
assert_allclose(ra, raref, equal_nan=True)
assert_allclose(dec, decref, equal_nan=True)
assert_allclose(lam, lamref, equal_nan=True)
# Test that we got NaNs at ind_zero
assert(np.isnan(ra).nonzero()[0] == ind_zeros.nonzero()[0]).all()
assert(np.isnan(ra).nonzero()[1] == ind_zeros.nonzero()[1]).all()
# Test the inverse transform
x1, y1 = result.meta.wcs.backward_transform(ra, dec, lam)
assert(np.isnan(x1).nonzero()[0] == ind_zeros.nonzero()[0]).all()
assert (np.isnan(x1).nonzero()[1] == ind_zeros.nonzero()[1]).all()
# Also run a smoke test with values outside the region.
dec[100][200] = -80
ra[100][200] = 7
lam[100][200] = 15
x2, y2 = result.meta.wcs.backward_transform(ra, dec, lam)
assert np.isnan(x2[100][200])
assert np.isnan(x2[100][200])
def test_miri_fixed_slit_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(self.test_dir,
'jw00035001001_01101_00001_mirimage_rate.fits')
result = AssignWcsStep.call(input_file, save_results=True)
truth_file = self.get_data(os.path.join(*self.ref_loc),
'jw00035001001_01101_00001_mirimage_assign_wcs.fits')
truth = ImageModel(truth_file)
x, y = grid_from_bounding_box(result.meta.wcs.bounding_box)
ra, dec, lam = result.meta.wcs(x, y)
raref, decref, lamref = truth.meta.wcs(x, y)
assert_allclose(ra, raref)
assert_allclose(dec, decref)
assert_allclose(lam, lamref)
def test_miri_slitless_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(self.test_dir,
"jw80600012001_02101_00003_mirimage_rateints.fits")
result = AssignWcsStep.call(input_file, save_results=True)
cwd = os.path.abspath('.')
os.makedirs('truth', exist_ok=True)
os.chdir('truth')
truth_file = self.get_data(*self.ref_loc,
"jw80600012001_02101_00003_mirimage_assignwcsstep.fits")
os.chdir(cwd)
truth = CubeModel(truth_file)
x, y = grid_from_bounding_box(result.meta.wcs.bounding_box)
ra, dec, lam = result.meta.wcs(x, y)
raref, decref, lamref = truth.meta.wcs(x, y)
assert_allclose(ra, raref)
assert_allclose(dec, decref)
assert_allclose(lam, lamref)
def test_miri_image_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(self.test_dir,
"jw00001001001_01101_00001_MIRIMAGE_ramp_fit.fits")
result = AssignWcsStep.call(input_file, save_results=True)
cwd = os.path.abspath('.')
os.makedirs('truth', exist_ok=True)
os.chdir('truth')
truth_file = self.get_data(*self.ref_loc,
"jw00001001001_01101_00001_MIRIMAGE_assign_wcs.fits")
os.chdir(cwd)
truth = ImageModel(truth_file)
x, y = grid_from_bounding_box(result.meta.wcs.bounding_box)
ra, dec = result.meta.wcs(x, y)
raref, decref = truth.meta.wcs(x, y)
assert_allclose(ra, raref)
assert_allclose(dec, decref)
def test_miri_image_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(self.test_dir,
"jw00001001001_01101_00001_MIRIMAGE_ramp_fit.fits")
ref_file = self.get_data(*self.ref_loc,
"jw00001001001_01101_00001_MIRIMAGE_assign_wcs.fits")
result = AssignWcsStep.call(input_file)
output_file = result.meta.filename
result.save(output_file)
result.close()
im = ImageModel(output_file)
imref = ImageModel(ref_file)
x, y = np.mgrid[:1031, :1024]
ra, dec = im.meta.wcs(x, y)
raref, decref = imref.meta.wcs(x, y)
utils.assert_allclose(ra, raref)
utils.assert_allclose(dec, decref)
def test_miri_fixed_slit_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(self.test_dir,
'jw00035001001_01101_00001_mirimage_rate.fits')
ref_file = self.get_data(os.path.join(*self.ref_loc),
'jw00035001001_01101_00001_mirimage_assign_wcs.fits')
result = AssignWcsStep.call(input_file)
output_file = result.meta.filename
result.save(output_file)
result.close()
im = ImageModel(output_file)
imref = ImageModel(ref_file)
y, x = np.mgrid[:1031, :1024]
ra, dec, lam = im.meta.wcs(x, y)
raref, decref, lamref = imref.meta.wcs(x, y)
utils.assert_allclose(ra, raref)
utils.assert_allclose(dec, decref)
utils.assert_allclose(lam, lamref)
def test_miri_slitless_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(self.test_dir,
"jw80600012001_02101_00003_mirimage_rateints.fits")
ref_file = self.get_data(*self.ref_loc,
"jw80600012001_02101_00003_mirimage_assign_wcs.fits")
result = AssignWcsStep.call(input_file)
output_file = result.meta.filename
result.save(output_file)
result.close()
im = CubeModel(output_file)
imref = CubeModel(ref_file)
x, y = np.mgrid[:1031, :1024]
ra, dec, lam = im.meta.wcs(x, y)
raref, decref, lamref = imref.meta.wcs(x, y)
utils.assert_allclose(ra, raref)
utils.assert_allclose(dec, decref)
utils.assert_allclose(lam, lamref)
def test_spatial_transform_miri():
wcsinfo = {
'dec_ref': 40,
'ra_ref': 100,
'roll_ref': 0.0,
'v2_ref': -453.5134,
'v3_ref': -373.4826,
'v3yangle': 0.0,
'vparity': -1
}
instrument = {'detector': 'MIRIMAGE', 'filter': 'P750L', 'name': 'MIRI'}
observation = {'date': '2019-01-01', 'time': '17:00:00'}
subarray = {
'fastaxis': 1,
'name': 'SLITLESSPRISM',
'slowaxis': 2,
'xsize': 72,
'xstart': 1,
'ysize': 416,
'ystart': 529
}
exposure = {
'duration': 11.805952,
'end_time': 58119.85416,
'exposure_time': 11.776,
'frame_time': 0.11776,
'group_time': 0.11776,
'groupgap': 0,
'integration_time': 11.776,
'nframes': 1,
'ngroups': 100,
'nints': 1,
'nresets_between_ints': 0,
'nsamples': 1,
'readpatt': 'FAST',
'sample_time': 10.0,
'start_time': 58119.8333,
'type': 'MIR_LRS-SLITLESS',
'zero_frame': False
}
im = ImageModel((416, 72))
im.meta.wcsinfo = wcsinfo
im.meta.instrument = instrument
im.meta.observation = observation
im.meta.exposure = exposure
im.meta.subarray = subarray
im = AssignWcsStep.call(im)
im = ResampleSpecStep.call(im)
x, y = grid_from_bounding_box(im.meta.wcs.bounding_box)
ra, dec, lam = im.meta.wcs(x, y)
xp, yp = im.meta.wcs.invert(ra, dec, lam)
assert_allclose(x, xp, atol=1e-8)
assert_allclose(y, yp, atol=1e-8)
def test_spatial_transform_miri():
wcsinfo = {
'dec_ref': -0.00601415671349804,
'ra_ref': -0.02073605215697509,
'roll_ref': -0.0,
'v2_ref': -453.5134,
'v3_ref': -373.4826,
'v3yangle': 0.0,
'vparity': -1
}
instrument = {'detector': 'MIRIMAGE', 'filter': 'P750L', 'name': 'MIRI'}
observation = {'date': '2019-01-01', 'time': '17:00:00'}
subarray = {
'fastaxis': 1,
'name': 'SLITLESSPRISM',
'slowaxis': 2,
'xsize': 72,
'xstart': 1,
'ysize': 416,
'ystart': 529
}
exposure = {
'duration': 11.805952,
'end_time': 58119.85416,
'exposure_time': 11.776,
'frame_time': 0.11776,
'group_time': 0.11776,
'groupgap': 0,
'integration_time': 11.776,
'nframes': 1,
'ngroups': 100,
'nints': 1,
'nresets_between_ints': 0,
'nsamples': 1,
'readpatt': 'FAST',
'sample_time': 10.0,
'start_time': 58119.8333,
'type': 'MIR_LRS-SLITLESS',
'zero_frame': False
}
im = ImageModel()
im.data = np.random.rand(416, 72)
im.error = np.random.rand(416, 72)
im.dq = np.random.rand(416, 72)
im.meta.wcsinfo._instance.update(wcsinfo)
im.meta.instrument._instance.update(instrument)
im.meta.observation._instance.update(observation)
im.meta.exposure._instance.update(exposure)
im.meta.subarray._instance.update(subarray)
out = AssignWcsStep.call(im)
out = ResampleSpecStep.call(out)
x, y = grid_from_bounding_box(out.meta.wcs.bounding_box)
ra, dec, lam = out.meta.wcs(x, y)
ra1 = np.where(ra < 0, 360 + ra, ra)
assert_allclose(out.meta.wcs.invert(ra, dec, lam),
out.meta.wcs.invert(ra1, dec, lam))
def test_spatial_transform_nirspec():
wcsinfo = {
'dec_ref': -0.00601415671349804,
'ra_ref': -0.02073605215697509,
'roll_ref': -0.0,
'v2_ref': -453.5134,
'v3_ref': -373.4826,
'v3yangle': 0.0,
'vparity': -1
}
instrument = {
'detector': 'NRS1',
'filter': 'CLEAR',
'grating': 'PRISM',
'name': 'NIRSPEC',
'gwa_tilt': 37.0610,
'gwa_xtilt': 0.0001,
'gwa_ytilt': 0.0001
}
subarray = {
'fastaxis': 1,
'name': 'SUBS200A1',
'slowaxis': 2,
'xsize': 72,
'xstart': 1,
'ysize': 416,
'ystart': 529
}
observation = {'date': '2016-09-05', 'time': '8:59:37'}
exposure = {
'duration': 11.805952,
'end_time': 58119.85416,
'exposure_time': 11.776,
'frame_time': 0.11776,
'group_time': 0.11776,
'groupgap': 0,
'integration_time': 11.776,
'nframes': 1,
'ngroups': 100,
'nints': 1,
'nresets_between_ints': 0,
'nsamples': 1,
'readpatt': 'NRSRAPID',
'sample_time': 10.0,
'start_time': 58119.8333,
'type': 'NRS_FIXEDSLIT',
'zero_frame': False
}
im = ImageModel()
im.data = np.random.rand(2048, 2048)
im.error = np.random.rand(2048, 2048)
im.dq = np.random.rand(2048, 2048)
im.meta.wcsinfo._instance.update(wcsinfo)
im.meta.instrument._instance.update(instrument)
im.meta.observation._instance.update(observation)
im.meta.exposure._instance.update(exposure)
im.meta.subarray._instance.update(subarray)
im = AssignWcsStep.call(im)
im = Extract2dStep.call(im)
im = ResampleSpecStep.call(im)
for slit in im.products:
x, y = grid_from_bounding_box(slit.meta.wcs.bounding_box)
ra, dec, lam = slit.meta.wcs(x, y)
ra1 = np.where(ra < 0, 360 + ra, ra)
assert_allclose(slit.meta.wcs.invert(ra, dec, lam),
slit.meta.wcs.invert(ra1, dec, lam))
def test_apply_background_2d(_jail, miri_dither_ch12):
""" Test if background polynomial is set it is subtracted correctly"""
all_models = datamodels.ModelContainer(miri_dither_ch12)
# test if given a background polynomial - apply_background_2d gives the correct answer
# Polynomial information was created by running a faked full array data set to determine
# what the polynomial values should be
new_container = []
degree = (1, 1, 1,)
center = (9.99999408089876, -4.668612949274985e-06, 4.889999912818894)
poly = np.ndarray(9)
# run AssignWcsStep on first file and set wcs of other files to this
channel = '1'
for im, m in enumerate(all_models):
if im == 0:
m = AssignWcsStep.call(m)
wcs1 = m.meta.wcs
poly = [-2.50000000e+00, -6.66518331e-15, 5.67845589e-12, -1.23549218e-11,
3.26209108e-12, -5.43180357e-12, -2.54903452e-09, 6.21614553e-09]
elif im == 1:
m.meta.wcs = wcs1
poly = [-2.50000000e+00, -9.29031986e-15, 1.74437380e-12, -3.94894956e-12,
4.71729481e-13, 5.42031845e-13, -9.96151554e-10, 2.78281950e-09]
elif im == 2:
m.meta.wcs = wcs1
poly = [7.50000000e+00, 2.22921836e-14, -6.97131279e-12, 1.58336906e-11,
-5.43704212e-12, 6.79109678e-12, 2.63515372e-09, -8.00226976e-09]
else:
m.meta.wcs = wcs1
poly = [-2.50000000e+00, -6.33668043e-15, -4.51516905e-13, 4.70180795e-13,
1.70322157e-12, -1.90132506e-12, 9.10032350e-10, -9.96695272e-10]
poly = np.asarray(poly)
m.meta.background.polynomial_info.append(
{
'degree': degree,
'refpoint': center,
'coefficients': poly.ravel().tolist(),
'channel': channel
}
)
# input 10 output 12.5 for all images
apply_background_2d(m, channel, subtract=True)
new_container.append(m)
# all the data should now be the same for the science pixels
# the edge pixels are located in non-science region and no
# background subtraction is done on these pixels
data1 = new_container[0].data[:, 16:]
data2 = new_container[1].data[:, 16:]
data3 = new_container[2].data[:, 16:]
data4 = new_container[3].data[:, 16:]
assert(np.allclose(data1, data2, rtol=1e-6))
assert(np.allclose(data2, data3, rtol=1e-6))
assert(np.allclose(data3, data4, rtol=1e-6))
assert(np.allclose(data1, data4, rtol=1e-6))
def test_wavecorr_fs():
hdul = create_nirspec_fs_file(grating="PRISM", filter="CLEAR")
im = datamodels.ImageModel(hdul)
dither = {'x_offset': -0.0264, 'y_offset': 1.089798712}
im.meta.dither = dither
im.meta.instrument.fixed_slit = 'S200A1'
im.meta.instrument.lamp_state = 'NONE'
im.meta.instrument.lamp_mode = 'FIXEDSLIT'
im.meta.instrument.gwa_tilt = 698.1256999999999
im.meta.instrument.gwa_xtilt = 0.334691644
im.meta.instrument.gwa_ytilt = 0.0349255353
im.meta.exposure.type = 'NRS_FIXEDSLIT'
im.meta.wcsinfo = {
'dec_ref': -70.77497509320972,
'dispersion_direction': 1,
'ra_ref': 90.75414044948525,
'roll_ref': 38.2179067606001,
'specsys': 'BARYCENT',
'spectral_order': 0,
'v2_ref': 332.136078,
'v3_ref': -479.224213,
'v3yangle': 138.7605896,
'velosys': -1398.2,
'vparity': -1,
'waverange_end': 5.3e-06,
'waverange_start': 6e-07
}
result = AssignWcsStep.call(im)
result = Extract2dStep.call(result)
bbox = ((-.5, 428.5), (-.5, 38.5))
result.slits[0].meta.wcs.bounding_box = bbox
x, y = wcstools.grid_from_bounding_box(bbox)
ra, dec, lam_before = result.slits[0].meta.wcs(x, y)
result.slits[0].wavelength = lam_before
src_result = SourceTypeStep.call(result)
result = WavecorrStep.call(src_result)
assert_allclose(result.slits[0].source_xpos, 0.127111, atol=1e-6)
slit = result.slits[0]
source_xpos = wavecorr.get_source_xpos(slit, slit.meta.wcs, lam=2)
assert_allclose(result.slits[0].source_xpos, source_xpos, atol=1e-6)
mean_correction = np.abs(src_result.slits[0].wavelength -
result.slits[0].wavelength)
assert_allclose(np.nanmean(mean_correction), 0.003, atol=.001)
dispersion = wavecorr.compute_dispersion(slit.meta.wcs, x, y)
assert_allclose(dispersion[~np.isnan(dispersion)], 1e-8, atol=1.04e-8)
# test on both sides of the slit center
source_xpos1 = -.2
source_xpos2 = .2
ref_name = result.meta.ref_file.wavecorr.name
freference = datamodels.WaveCorrModel(
WavecorrStep.reference_uri_to_cache_path(ref_name,
im.crds_observatory))
zero_point1 = wavecorr.compute_zero_point_correction(
lam_before, freference, source_xpos1, 'S200A1', dispersion)
zero_point2 = wavecorr.compute_zero_point_correction(
lam_before, freference, source_xpos2, 'S200A1', dispersion)
diff_correction = np.abs(zero_point1[1] - zero_point2[1])
assert_allclose(np.nanmean(diff_correction), 0.45, atol=0.01)
def test_spatial_transform_nirspec():
wcsinfo = {
'dec_ref': 40,
'ra_ref': 100,
'roll_ref': 0,
'v2_ref': -453.5134,
'v3_ref': -373.4826,
'v3yangle': 0.0,
'vparity': -1
}
instrument = {
'detector': 'NRS1',
'filter': 'CLEAR',
'grating': 'PRISM',
'name': 'NIRSPEC',
'gwa_tilt': 37.0610,
'gwa_xtilt': 0.0001,
'gwa_ytilt': 0.0001
}
subarray = {
'fastaxis': 1,
'name': 'SUBS200A1',
'slowaxis': 2,
'xsize': 72,
'xstart': 1,
'ysize': 416,
'ystart': 529
}
observation = {'date': '2016-09-05', 'time': '8:59:37'}
exposure = {
'duration': 11.805952,
'end_time': 58119.85416,
'exposure_time': 11.776,
'frame_time': 0.11776,
'group_time': 0.11776,
'groupgap': 0,
'integration_time': 11.776,
'nframes': 1,
'ngroups': 100,
'nints': 1,
'nresets_between_ints': 0,
'nsamples': 1,
'readpatt': 'NRSRAPID',
'sample_time': 10.0,
'start_time': 58119.8333,
'type': 'NRS_FIXEDSLIT',
'zero_frame': False
}
im = ImageModel((2048, 2048))
im.meta.wcsinfo = wcsinfo
im.meta.instrument = instrument
im.meta.observation = observation
im.meta.exposure = exposure
im.meta.subarray = subarray
im = AssignWcsStep.call(im)
im = Extract2dStep.call(im)
im = ResampleSpecStep.call(im)
for slit in im.slits:
x, y = grid_from_bounding_box(slit.meta.wcs.bounding_box)
ra, dec, lam = slit.meta.wcs(x, y)
xp, yp = slit.meta.wcs.invert(ra, dec, lam)
assert_allclose(x, xp, atol=1e-8)
assert_allclose(y, yp, atol=1e-8)