def img_with_wcs(input):
"""
Open a JWST exposure and apply the distortion model.
Parameters
----------
input : type
Anything `jwst.datamodels.util.open` can accept for initialization.
Returns
-------
with_wcs : `jwst.datamodels.ImageModel`
Image model with full `~gwcs` in `with_wcs.meta.wcs`.
"""
from jwst.datamodels import util
from jwst.stpipe import crds_client
from jwst.assign_wcs import assign_wcs
img = util.open(input)
dist_file = crds_client.get_reference_file(img, 'distortion')
reference_files = {'distortion': dist_file}
with_wcs = assign_wcs.load_wcs(img, reference_files=reference_files)
return with_wcs
def test_miri_ifu_wcs(_bigdata):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
try:
os.remove("miri_ifu_wcs_output.fits")
except:
pass
input_file = os.path.join(
_bigdata, 'miri', 'test_wcs', 'ifu',
'jw00024001001_01101_00001_MIRIFUSHORT_uncal_MiriSloperPipeline.fits')
ref_file = os.path.join(
_bigdata, 'miri', 'test_wcs', 'ifu',
'jw00024001001_01101_00001_MIRIFUSHORT_assign_wcs.fits')
AssignWcsStep.call(input_file, output_file='miri_ifu_wcs', suffix='output')
im = ImageModel('miri_ifu_wcs_output.fits')
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'))
# Choose the same plane as in the miri.py file (hardcoded for now).
regions = region.regions[7, :, :]
# 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 = 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_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 plot_ramps_pre_post_correction(self, pixel=[500, 500]):
"""
We want to plot a test ramp pre and post dark correction and the dark itself.
Also print the properties of the dark file to check that they match.
Parameters
----------
pixel : list
the pixel to plot the ramp for
"""
in_dm = datamodels.MIRIRampModel(self.pre_dark_file)
out_dm = datamodels.MIRIRampModel(self.post_dark_file)
nints, ngroups, nrows, ncols = in_dm.data.shape
group = range(1, ngroups + 1)
fig, axs = plt.subplots(2, 1, figsize=(10, 8), sharex=True)
axs[0].plot(group,
in_dm.data[0, :, pixel[1], pixel[0]],
c='r',
marker='^',
markersize=0,
linestyle='-',
linewidth=1,
label='pre-dark')
axs[0].plot(group,
out_dm.data[0, :, pixel[1], pixel[0]],
c='b',
marker='o',
markersize=0,
linestyle='-',
linewidth=1,
label='post-dark')
axs[0].set_title('dark current correction', fontsize=15)
axs[0].set_ylabel('DN', fontsize=15)
axs[0].set_xlim(-1, max(group) + 1)
axs[0].legend(prop={'size': 12}, loc=0)
# get CDP
cdp = self._get_dark_cdp()
# get CRDS
crds_filename = crds_client.get_reference_file(in_dm, 'dark')
crds = datamodels.DarkMIRIModel(crds_filename)
axs[1].plot(group,
in_dm.data[0, :, pixel[1], pixel[0]] -
out_dm.data[0, :, pixel[1], pixel[0]],
c='g',
marker='s',
markersize=0,
linestyle='-',
linewidth=1,
label='difference')
axs[1].plot(group,
cdp.data[0, 0:ngroups, pixel[1], pixel[0]],
c='orange',
marker='o',
markersize=5,
linestyle='-',
linewidth=0,
label='CDP')
axs[1].plot(group,
crds.data[0, 0:ngroups, pixel[1], pixel[0]],
c='red',
marker='x',
markersize=9,
linestyle='-',
linewidth=0,
label='CRDS')
axs[1].set_ylabel('DN', fontsize=15)
axs[1].set_xlabel('group', fontsize=15)
axs[1].legend(prop={'size': 12}, loc=0)
plt.tight_layout(h_pad=0)
plot_name = os.path.join(self.output_dir, 'dark_correction.pdf')
try:
os.remove(plot_name)
except:
pass
fig.savefig(plot_name, dpi=100)