def test_open_fits_model_s3(s3_root_dir):
path = str(s3_root_dir.join("test.fits"))
with DataModel() as dm:
dm.save(path)
model = DataModel("s3://test-s3-data/test.fits")
assert isinstance(model, DataModel)
def test_skip_serializing_null(tmpdir, filename):
"""Make sure that None is not written out to the ASDF tree"""
path = str(tmpdir.join(filename))
with DataModel() as model:
model.meta.telescope = None
model.save(path)
with DataModel(path) as model:
# Make sure that 'telescope' is not in the tree
with pytest.raises(KeyError):
assert model.meta["telescope"] is None
def test_stringify():
im = DataModel()
assert str(im) == '<DataModel>'
im.close()
im = ImageModel((10, 100))
assert str(im) == '<ImageModel(10, 100)>'
im.close()
image = ROOT_DIR + "/nircam_mask.fits"
im = MaskModel(image)
assert str(im) == '<MaskModel(2048, 2048) from nircam_mask.fits>'
im.close()
def test_open_asdf_model(init):
# Open an empty asdf file, pass extra arguments
with DataModel(init=init,
ignore_version_mismatch=False,
ignore_unrecognized_tag=True) as model:
assert not model._asdf._ignore_version_mismatch
assert model._asdf._ignore_unrecognized_tag
def _create_source():
dm = DataModel(FITS_FILE)
assert dm.meta.instrument.name == 'MIRI'
dm.meta.instrument.name = 'NIRCAM'
dm.meta.subarray.xstart = 42
return dm
def test_open_asdf_s3(s3_root_dir):
"""Test opening a model from an ASDF file on S3"""
path = str(s3_root_dir.join("test.asdf"))
with DataModel() as dm:
dm.save(path)
with datamodels.open("s3://test-s3-data/test.asdf") as m:
assert isinstance(m, DataModel)
def test_default_input_with_new_model():
"""Test getting input name with new model"""
step = StepWithModel()
model = DataModel()
step.run(model)
assert step._input_filename is None
def test_stringify():
im = DataModel()
assert str(im) == '<DataModel>'
im = ImageModel((10, 100))
assert str(im) == '<ImageModel(10, 100)>'
image = os.path.join(ROOT_DIR, "nircam_mask.fits")
with MaskModel(image) as im:
assert str(im) == '<MaskModel(2048, 2048) from nircam_mask.fits>'
def make_models(tmpdir_factory):
"""Create basic models
Returns
-------
path_just_fits, path_model : (str, str)
`path_just_fits` is a FITS file of `DataModel` without the ASDF extension.
`path_model` is a FITS file of `DataModel` with the ASDF extension.
"""
path = tmpdir_factory.mktemp('skip_fits_update')
path_just_fits = str(path / 'just_fits.fits')
path_model = str(path / 'model.fits')
primary_hdu = fits.PrimaryHDU()
primary_hdu.header['exp_type'] = 'NRC_IMAGE'
hduls = fits.HDUList([primary_hdu])
hduls.writeto(path_just_fits)
model = DataModel(hduls)
model.save(path_model)
return {'just_fits': path_just_fits, 'model': path_model}
def test_stringify(tmpdir):
im = DataModel()
assert str(im) == '<DataModel>'
im = ImageModel((10, 100))
assert str(im) == '<ImageModel(10, 100)>'
path = str(tmpdir.join("nircam_mask.fits"))
m = MaskModel((2048, 2048))
m.save(path)
m.close()
with MaskModel(path) as im:
assert str(im) == '<MaskModel(2048, 2048) from nircam_mask.fits>'
def test_open_asdf_model():
# Open an empty asdf file, pass extra arguments
model = DataModel(init=None,
ignore_version_mismatch=False,
ignore_unrecognized_tag=True)
assert model._asdf._ignore_version_mismatch == False
assert model._asdf._ignore_unrecognized_tag == True
model.close()
# Open an existing asdf file
model = DataModel(ASDF_FILE,
ignore_version_mismatch=False,
ignore_unrecognized_tag=True)
assert model._asdf._ignore_version_mismatch == False
assert model._asdf._ignore_unrecognized_tag == True
model.close()
def data_model_list():
pytest.importorskip("jwst")
from jwst.datamodels import DataModel, ModelContainer
models = []
for k in range(6):
m = DataModel()
m.meta.observation.program_number = '0001'
m.meta.observation.observation_number = '1'
m.meta.observation.visit_number = '1'
m.meta.observation.visit_group = '1'
m.meta.observation.sequence_id = '01'
m.meta.observation.activity_id = '1'
m.meta.observation.exposure_number = '1'
m.meta.instrument.name = 'NIRCAM'
m.meta.instrument.channel = 'SHORT'
m.meta.filename = 'file{:d}.fits'.format(k)
models.append(m)
models[-3].meta.observation.observation_number = '2'
models[-2].meta.observation.observation_number = '3'
models[-1].meta.observation.observation_number = '3'
return ModelContainer(models)
def test_from_fits_write(dm):
dm.to_fits(TMP_FITS, overwrite=True)
return DataModel.from_fits(TMP_FITS)
def test_hasattr():
model = DataModel()
assert model.meta.hasattr('date')
assert not model.meta.hasattr('filename')
def test_datamodel_raises_filenotfound():
with pytest.raises(FileNotFoundError):
DataModel(init='file_does_not_exist.fits')
def inputs(request):
table = None
instrument, exptype = request.param
dm = DataModel()
dm.meta.instrument.name = instrument
dm.meta.exposure.type = exptype
if instrument == 'MIRI':
if 'LRS' in exptype:
dm.meta.subarray.name = 'FULL'
table = Table(
{
'subarray': ['FULL', 'sub02'],
'nelem_wl': [3, 3],
'nelem_size': [3, 3],
'wavelength': [[1, 2, 3, 0], [1, 2, 3, 0]],
'size': [[1, 2, 3, 0], [1, 2, 3, 0]],
'apcorr': np.full((2, 4, 4), 0.5)
}
)
if instrument == 'NIRSPEC': # Too complicated to come up with silly example data; using "dummy" ref file
dm.meta.instrument.filter = 'CLEAR'
dm.meta.instrument.grating = 'PRISM'
if 'FIXEDSLIT' in dm.meta.exposure.type:
dm.meta.instrument.fixed_slit = 'S200A1'
table = Table.read(NIR_TEST_FILES['FIXEDSLIT/BRIGHTOBJ'], format='fits')[:2]
else:
table = Table.read(NIR_TEST_FILES['MSASPEC'], format='fits')[:2]
table['APCORR'] = table['APCORR'].reshape((len(table), 3, 2048, 3)) # Reshape test data to expected shape
if instrument == 'NIRCAM':
dm.meta.instrument.filter = 'F322W2'
dm.meta.instrument.pupil = 'GRISMR'
table = Table(
{
'filter': ['F322W2', 'filter02'],
'pupil': ['GRISMR', 'pupil02'],
'nelem_wl': [3, 3],
'nelem_size': [3, 3],
'wavelength': [[1, 2, 3, 0], [1, 2, 3, 0]],
'size': [[1, 2, 3, 0], [1, 2, 3, 0]],
'apcorr': np.full((2, 4, 4), 0.5)
}
)
if instrument == 'NIRISS':
dm.meta.instrument.filter = 'GR150R'
dm.meta.instrument.pupil = 'F090W'
table = Table(
{
'filter': ['GR150R', 'filter02'],
'pupil': ['F090W', 'pupil02'],
'nelem_wl': [3, 3],
'nelem_size': [3, 3],
'wavelength': [[1, 2, 3, 0], [1, 2, 3, 0]],
'size': [[1, 2, 3, 0], [1, 2, 3, 0]],
'apcorr': np.full((2, 4, 4), 0.5)
}
)
return dm, fits.table_to_hdu(table).data
def empty_model():
"""Create an empy model"""
return DataModel()
in the certification of reference files.
"""
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
import os.path
import re
from crds import (log, rmap, data_file, config, utils, timestamp)
from . import tpn
# =======================================================================
try:
from jwst.datamodels import DataModel
MODEL = DataModel()
except Exception:
MODEL = None
# =======================================================================
HERE = os.path.dirname(__file__) or "./"
# =======================================================================
def test():
"""Run the module doctests."""
import doctest
from . import locate
return doctest.testmod(locate)
def test_copy_model():
with DataModel() as dm:
with DataModel(dm) as dm2:
assert hasattr(dm2, 'meta')
def test_base_model_has_no_arrays():
with pytest.raises(AttributeError):
with DataModel() as dm:
dm.data
def test_subarray():
with DataModel(FITS_FILE) as dm:
dm.meta.subarray.xstart
def test_delete():
with DataModel() as dm:
dm.meta.instrument.name = 'NIRCAM'
assert dm.meta.instrument.name == 'NIRCAM'
del dm.meta.instrument.name
assert dm.meta.instrument.name is None