def test_pixel_scale_ratio_spec(miri_rate, ratio):
im = AssignWcsStep.call(miri_rate, sip_approx=False)
result1 = ResampleSpecStep.call(im)
result2 = ResampleSpecStep.call(im, pixel_scale_ratio=ratio)
assert_allclose(np.array(result1.data.shape),
np.array(result2.data.shape) * ratio,
rtol=1,
atol=1)
def output_hdul(set_inandout_filenames, config):
set_inandout_filenames_info = core_utils.read_info4outputhdul(config, set_inandout_filenames)
step, txt_name, step_input_file, step_output_file, run_calwebb_spec2, outstep_file_suffix = set_inandout_filenames_info
skip_runing_pipe_step = config.getboolean("tests_only", "_".join((step, "tests")))
# Only run step if data is not IFU
inhdu = core_utils.read_hdrfits(step_input_file, info=False, show_hdr=False)
end_time = '0.0'
print("Is the input file IFU? = ", core_utils.check_IFU_true(inhdu))
if not core_utils.check_IFU_true(inhdu):
stp = ResampleSpecStep()
# if run_calwebb_spec2 is True calwebb_spec2 will be called, else individual steps will be ran
step_completed = False
if run_calwebb_spec2:
# read the assign wcs fits file
local_step_output_file = core_utils.read_completion_to_full_run_map("full_run_map.txt", step)
hdul = core_utils.read_hdrfits(local_step_output_file, info=False, show_hdr=False)
# move the output file into the working directory
working_directory = config.get("calwebb_spec2_input_file", "working_directory")
step_output_file = os.path.join(working_directory, local_step_output_file)
print ("Step product was saved as: ", step_output_file)
subprocess.run(["mv", local_step_output_file, step_output_file])
return hdul
else:
if config.getboolean("steps", step):
print ("*** Step "+step+" set to True")
if os.path.isfile(step_input_file):
if not skip_runing_pipe_step:
# get the right configuration files to run the step
local_pipe_cfg_path = config.get("calwebb_spec2_input_file", "local_pipe_cfg_path")
# start the timer to compute the step running time
start_time = time.time()
if local_pipe_cfg_path == "pipe_source_tree_code":
result = stp.call(step_input_file)
else:
result = stp.call(step_input_file, config_file=local_pipe_cfg_path+'/resample_spec.cfg')
result.save(step_output_file)
# end the timer to compute the step running time
end_time = repr(time.time() - start_time) # this is in seconds
print("Step "+step+" took "+end_time+" seconds to finish")
step_completed = True
core_utils.add_completed_steps(txt_name, step, outstep_file_suffix, step_completed, end_time)
hdul = core_utils.read_hdrfits(step_output_file, info=False, show_hdr=False)
return hdul
else:
core_utils.add_completed_steps(txt_name, step, outstep_file_suffix, step_completed, end_time)
pytest.skip("Skipping "+step+" because the input file does not exist.")
else:
core_utils.add_completed_steps(txt_name, step, outstep_file_suffix, step_completed, end_time)
pytest.skip("Skipping "+step+". Step set to False in configuration file.")
else:
pytest.skip("Skipping "+step+" because data is IFU data and the resample will be done in cube_build.")
def test_miri_wcs_roundtrip(miri_rate):
im = AssignWcsStep.call(miri_rate)
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_nirspec_wcs_roundtrip(nirspec_rate):
im = AssignWcsStep.call(nirspec_rate)
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)
def test_spatial_transform():
"""
Calling the backwards WCS transform gives the same results
for ``negative RA`` and ``negative RA + 360``.
"""
im = ImageModel()
im.meta.wcsinfo._instance.update(wcsinfo)
im.meta.instrument._instance.update(instrument)
im.meta.exposure._instance.update(exposure)
im.meta.observation._instance.update(observation)
im.meta.subarray._instance.update(subarray)
im = AssignWcsStep.call(im)
im.data = np.random.rand(416, 72)
im.error = np.random.rand(416, 72)
im.dq = np.random.rand(416, 72)
im = ResampleSpecStep.call(im)
x, y = grid_from_bounding_box(im.meta.wcs.bounding_box)
ra, dec, lam = im.meta.wcs(x, y)
ra1 = np.where(ra < 0, 360 + ra, ra)
assert_allclose(im.meta.wcs.invert(ra, dec, lam),
im.meta.wcs.invert(ra1, dec, lam))
def output_hdul(set_inandout_filenames, config):
set_inandout_filenames_info = core_utils.read_info4outputhdul(
config, set_inandout_filenames)
step, txt_name, step_input_file, step_output_file, run_calwebb_spec2, outstep_file_suffix = set_inandout_filenames_info
run_pipe_step = config.getboolean("run_pipe_steps", step)
# determine which tests are to be run
resample_spec_completion_tests = config.getboolean(
"run_pytest", "_".join((step, "completion", "tests")))
#resample_spec_reffile_tests = config.getboolean("run_pytest", "_".join((step, "reffile", "tests")))
#resample_spec_validation_tests = config.getboolean("run_pytest", "_".join((step, "validation", "tests")))
run_pytests = [
resample_spec_completion_tests
] #, resample_spec_reffile_tests, resample_spec_validation_tests]
end_time = '0.0'
# Only run step if data is not IFU or BOTS
mode_used = config.get("calwebb_spec2_input_file", "mode_used").lower()
working_directory = config.get("calwebb_spec2_input_file",
"working_directory")
initial_input_file = config.get("calwebb_spec2_input_file", "input_file")
initial_input_file = os.path.join(working_directory, initial_input_file)
detector = fits.getval(initial_input_file, "DETECTOR", 0)
if not os.path.isfile(initial_input_file):
pytest.skip(
"Skipping " + step +
" because the initial input file given in PTT_config.cfg does not exist."
)
if mode_used != "bots" and mode_used != "ifu":
# if run_calwebb_spec2 is True calwebb_spec2 will be called, else individual steps will be ran
step_completed = False
# check if the filter is to be changed
change_filter_opaque = config.getboolean("calwebb_spec2_input_file",
"change_filter_opaque")
if change_filter_opaque:
is_filter_opaque, step_input_filename = change_filter_opaque2science.change_filter_opaque(
step_input_file, step=step)
if is_filter_opaque:
filter_opaque_msg = "With FILTER=OPAQUE, the calwebb_spec2 will run up to the extract_2d step. Resample pytest now set to Skip."
print(filter_opaque_msg)
core_utils.add_completed_steps(txt_name, step,
outstep_file_suffix,
step_completed, end_time)
pytest.skip("Skipping " + step +
" because the input file does not exist.")
if run_calwebb_spec2:
hdul = core_utils.read_hdrfits(step_output_file,
info=False,
show_hdr=False)
return hdul, step_output_file, run_pytests
else:
if run_pipe_step:
# Create the logfile for PTT, but erase the previous one if it exists
PTTcalspec2_log = os.path.join(
working_directory,
'PTT_calspec2_' + detector + '_' + step + '.log')
if os.path.isfile(PTTcalspec2_log):
os.remove(PTTcalspec2_log)
print(
"Information outputed to screen from PTT will be logged in file: ",
PTTcalspec2_log)
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
logging.basicConfig(filename=PTTcalspec2_log,
level=logging.INFO)
# print pipeline version
import jwst
pipeline_version = "\n *** Using jwst pipeline version: " + jwst.__version__ + " *** \n"
print(pipeline_version)
logging.info(pipeline_version)
if change_filter_opaque:
logging.info(filter_opaque_msg)
if os.path.isfile(step_input_file):
msg = " *** Step " + step + " set to True"
print(msg)
logging.info(msg)
stp = ResampleSpecStep()
# check that previous pipeline steps were run up to this point
core_utils.check_completed_steps(step, step_input_file)
# get the right configuration files to run the step
local_pipe_cfg_path = config.get(
"calwebb_spec2_input_file", "local_pipe_cfg_path")
# start the timer to compute the step running time
start_time = time.time()
if local_pipe_cfg_path == "pipe_source_tree_code":
result = stp.call(step_input_file)
else:
result = stp.call(step_input_file,
config_file=local_pipe_cfg_path +
'/resample_spec.cfg')
result.save(step_output_file)
# end the timer to compute the step running time
end_time = repr(time.time() -
start_time) # this is in seconds
msg = "Step " + step + " took " + end_time + " seconds to finish"
print(msg)
logging.info(msg)
step_completed = True
hdul = core_utils.read_hdrfits(step_output_file,
info=False,
show_hdr=False)
# rename and move the pipeline log file
try:
calspec2_pilelog = "calspec2_pipeline_" + step + "_" + detector + ".log"
pytest_workdir = os.getcwd()
logfile = glob(pytest_workdir + "/pipeline.log")[0]
os.rename(
logfile,
os.path.join(working_directory, calspec2_pilelog))
except:
IndexError
# add the running time for this step
core_utils.add_completed_steps(txt_name, step,
outstep_file_suffix,
step_completed, end_time)
return hdul, step_output_file, run_pytests
else:
msg = " The input file does not exist. Skipping step."
print(msg)
logging.info(msg)
core_utils.add_completed_steps(txt_name, step,
outstep_file_suffix,
step_completed, end_time)
pytest.skip("Skipping " + step +
" because the input file does not exist.")
else:
msg = "Skipping running pipeline step " + step
print(msg)
logging.info(msg)
end_time = core_utils.get_stp_run_time_from_screenfile(
step, detector, working_directory)
if os.path.isfile(step_output_file):
hdul = core_utils.read_hdrfits(step_output_file,
info=False,
show_hdr=False)
step_completed = True
# add the running time for this step
core_utils.add_completed_steps(txt_name, step,
outstep_file_suffix,
step_completed, end_time)
return hdul, step_output_file, run_pytests
else:
step_completed = False
# add the running time for this step
core_utils.add_completed_steps(txt_name, step,
outstep_file_suffix,
step_completed, end_time)
pytest.skip()
else:
pytest.skip("Skipping " + step +
" because data is either IFU or BOTS.")
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': 'SUBPRISM',
'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_nirspec_masterbackground_mos_user1d(self):
"""
Regression test of master background subtraction for NRS MOS when a user 1-D spectrum is provided.
"""
# input file has 2-D background image added to it
input_file = self.get_data(*self.test_dir, 'nrs_mos_sci+bkg_cal.fits')
# user provide 1-D background was created from the 2-D background image
input_1dbkg_file = self.get_data(*self.test_dir,
'nrs_mos_bkg_x1d.fits')
result = MasterBackgroundStep.call(input_file,
user_background=input_1dbkg_file,
save_results=True)
# _________________________________________________________________________
# One of out tests is to compare the 1-D extracted spectra from
# the science image (no background added) and the masterbackground subtracted
# data.
# run resample_spec on results from MasterBackground step
result_2d = ResampleSpecStep.call(result)
# run 1-D extract on results from MasterBackground step
result_1d = Extract1dStep.call(result_2d)
# get input science data with background added
input_sci_cal_file = self.get_data(*self.test_dir,
'nrs_mos_sci_cal.fits')
# get 1-D extract on original science data without background
# this reference data was also run through ResampleSpec
input_sci_1d_file = self.get_data(*self.ref_loc,
'nrs_mos_sci_extract1dstep.fits')
input_sci = datamodels.open(input_sci_cal_file)
sci_cal_1d = datamodels.open(input_sci_1d_file)
num_spec = len(sci_cal_1d.spec)
# the user 1D spectrum may not cover the entire wavelength range of the
# science data. Find the wavelength range of user 1-D spectra
input_1dbkg_1d = datamodels.open(input_1dbkg_file)
user_wave = input_1dbkg_1d.spec[0].spec_table['wavelength']
user_flux = input_1dbkg_1d.spec[0].spec_table['flux']
user_wave_valid = np.where(user_flux > 0)
min_user_wave = np.amin(user_wave[user_wave_valid])
max_user_wave = np.amax(user_wave[user_wave_valid])
input_1dbkg_1d.close()
# loop over each slit and perform 2 tests on each slit
for i in range(num_spec):
# ______________________________________________________________________
# Test 1 compare extracted spectra data from the science data
# to extracted spectra from the output
# from MasterBackground subtraction.
sci_spec_1d = sci_cal_1d.spec[i].spec_table['flux']
sci_wave = sci_cal_1d.spec[i].spec_table['wavelength']
result_spec_1d = result_1d.spec[i].spec_table['flux']
# find the waverange covered by both user 1-D and science slit
sci_wave_valid = np.where(sci_spec_1d > 0)
min_wave = np.amin(sci_wave[sci_wave_valid])
max_wave = np.amax(sci_wave[sci_wave_valid])
if min_user_wave > min_wave:
min_wave = min_user_wave
if max_user_wave < max_wave:
max_wave = max_user_wave
sub_spec = sci_spec_1d - result_spec_1d
valid = np.where(
np.logical_and(sci_wave > min_wave, sci_wave < max_wave))
sub_spec = sub_spec[valid]
mean_sub = np.nanmean(sub_spec)
atol = 1.5
assert_allclose(mean_sub, 0, atol=atol)
# ______________________________________________________________________
# Test 2 compare the science data with no background
# to the output from the masterBackground Subtraction step
# background subtracted science image.
bb = input_sci.slits[i].meta.wcs.bounding_box
x, y = grid_from_bounding_box(bb)
ra, dec, lam = input_sci.slits[i].meta.wcs(x, y)
valid = np.isfinite(lam)
sci = input_sci.slits[i].data
result_slit = result.slits[i].data
# check for outliers in the science image that could cause test
# to fail. These could be cosmic ray hits or other yeck that
# messes up the science data - ignores these cases
sci_mean = np.nanmean(sci[valid])
sci_std = np.nanstd(sci[valid])
upper = sci_mean + sci_std * 5.0
lower = sci_mean - sci_std * 5.0
mask_clean = np.logical_and(sci[valid] < upper, sci[valid] > lower)
# for this slit subtract the background subtracted data from
# the science data with no background added
sub = result_slit - sci
# do not look at outliers - they confuse things
sub_valid = sub[valid]
mean_sub = np.mean(sub_valid[mask_clean])
atol = 0.1
assert_allclose(np.absolute(mean_sub), 0, atol=atol)
# ______________________________________________________________________
# Test 3 Compare background sutracted science data (results)
# to a truth file. This data is MultiSlit data
result_file = result.meta.filename
ref_file = self.get_data(*self.ref_loc,
'nrs_mos_sci+bkg_masterbackgroundstep.fits')
outputs = [(result_file, ref_file)]
self.compare_outputs(outputs)
input_sci.close()
result.close()
def test_nirspec_masterbackground_mos_user1d(self):
"""
Regression test of master background subtraction for NRS MOS when a user 1-D spectrum is provided.
"""
# input file has 2-D background image added to it
input_file = self.get_data(*self.test_dir,
'nrs_mos_sci+bkg_cal.fits')
# user provide 1-D background was created from the 2-D background image
input_1dbkg_file = self.get_data(*self.test_dir,
'nrs_mos_bkg_x1d.fits')
result = MasterBackgroundStep.call(input_file,
user_background=input_1dbkg_file,
save_results=True)
# _________________________________________________________________________
# One of out tests is to compare the 1-D extracted spectra from
# the science image (no background added) and the masterbackground subtracted
# data.
# run resample_spec on results from MasterBackground step
result_2d = ResampleSpecStep.call(result)
# run 1-D extract on results from MasterBackground step
result_1d = Extract1dStep.call(result_2d)
# get input science data with background added
input_sci_cal_file = self.get_data(*self.test_dir,
'nrs_mos_sci_cal.fits')
# get 1-D extract on original science data without background
# this reference data was also run through ResampleSpec
input_sci_1d_file = self.get_data(*self.ref_loc,
'nrs_mos_sci_extract1dstep.fits')
input_sci = datamodels.open(input_sci_cal_file)
sci_cal_1d = datamodels.open(input_sci_1d_file)
num_spec = len(sci_cal_1d.spec)
# the user 1D spectrum may not cover the entire wavelength range of the
# science data. Find the wavelength range of user 1-D spectra
input_1dbkg_1d = datamodels.open(input_1dbkg_file)
user_wave = input_1dbkg_1d.spec[0].spec_table['wavelength']
user_flux = input_1dbkg_1d.spec[0].spec_table['flux']
user_wave_valid = np.where(user_flux > 0)
min_user_wave = np.amin(user_wave[user_wave_valid])
max_user_wave = np.amax(user_wave[user_wave_valid])
input_1dbkg_1d.close()
# loop over each slit and perform 2 tests on each slit
for i in range(num_spec):
# ______________________________________________________________________
# Test 1 compare extracted spectra data from the science data
# to extracted spectra from the output
# from MasterBackground subtraction.
sci_spec_1d = sci_cal_1d.spec[i].spec_table['flux']
sci_wave = sci_cal_1d.spec[i].spec_table['wavelength']
result_spec_1d = result_1d.spec[i].spec_table['flux']
# find the waverange covered by both user 1-D and science slit
sci_wave_valid = np.where(sci_spec_1d > 0)
min_wave = np.amin(sci_wave[sci_wave_valid])
max_wave = np.amax(sci_wave[sci_wave_valid])
if min_user_wave > min_wave:
min_wave = min_user_wave
if max_user_wave < max_wave:
max_wave = max_user_wave
sub_spec = sci_spec_1d - result_spec_1d
valid = np.where(np.logical_and(sci_wave > min_wave, sci_wave < max_wave))
sub_spec = sub_spec[valid]
mean_sub = np.nanmean(sub_spec)
atol = 1.5
assert_allclose(mean_sub, 0, atol=atol)
# ______________________________________________________________________
# Test 2 compare the science data with no background
# to the output from the masterBackground Subtraction step
# background subtracted science image.
bb = input_sci.slits[i].meta.wcs.bounding_box
x, y = grid_from_bounding_box(bb)
ra, dec, lam = input_sci.slits[i].meta.wcs(x, y)
valid = np.isfinite(lam)
sci = input_sci.slits[i].data
result_slit = result.slits[i].data
# check for outliers in the science image that could cause test
# to fail. These could be cosmic ray hits or other yeck that
# messes up the science data - ignores these cases
sci_mean = np.nanmean(sci[valid])
sci_std = np.nanstd(sci[valid])
upper = sci_mean + sci_std*5.0
lower = sci_mean - sci_std*5.0
mask_clean = np.logical_and(sci[valid] < upper, sci[valid] > lower)
# for this slit subtract the background subtracted data from
# the science data with no background added
sub = result_slit - sci
# do not look at outliers - they confuse things
sub_valid = sub[valid]
mean_sub = np.mean(sub_valid[mask_clean])
atol = 0.1
assert_allclose(np.absolute(mean_sub), 0, atol=atol)
# ______________________________________________________________________
# Test 3 Compare background sutracted science data (results)
# to a truth file. This data is MultiSlit data
result_file = result.meta.filename
ref_file = self.get_data(*self.ref_loc,
'nrs_mos_sci+bkg_masterbackgroundstep.fits')
outputs = [(result_file, ref_file)]
self.compare_outputs(outputs)
input_sci.close()
result.close()
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)
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)
