def setUp(self):
# Create a typical distortion product.
self.bmatrix = [[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8],
[0.8, 0.7, 0.6, 0.5], [0.4, 0.3, 0.2, 0.1]]
self.amatrix = [[0.1, 0.0, 0.0, 0.0], [0.0, 0.1, 0.0, 0.0],
[0.0, 0.0, 0.1, 0.0], [0.0, 0.0, 0.0, 0.1]]
self.tmatrix = [[0.5, 0.0, 0.0], [0.2, 0.5, 0.2], [0.0, 0.0, 0.5]]
self.mmatrix = [[0.0, 0.0, 0.1], [0.0, 0.1, 0.0], [0.1, 0.0, 0.0]]
self.boresight_offsets = [('One', 0.1, 0.2), ('Two', 0.2, 0.3)]
self.dataproduct = MiriImagingDistortionModel(
amatrix=self.bmatrix,
bmatrix=self.amatrix,
tmatrix=self.tmatrix,
mmatrix=self.mmatrix,
dmatrix=self.bmatrix,
cmatrix=self.amatrix,
fmatrix=self.bmatrix,
ematrix=self.amatrix,
boresight_offsets=self.boresight_offsets)
# Add some example metadata.
self.dataproduct.set_target_metadata(0.0, 0.0)
self.dataproduct.set_instrument_metadata(detector='MIRIFUSHORT',
channel='1',
ccc_pos='CLOSED',
deck_temperature=11.0,
detector_temperature=6.0)
self.dataproduct.set_exposure_metadata(readpatt='FAST',
nints=1,
ngroups=1,
frame_time=1.0,
integration_time=10.0,
group_time=10.0,
reset_time=0,
frame_resets=3)
self.testfile = "MiriImagingDistortionModel_test.fits"
def test_creation(self):
# Check the successful creation of a data product with all 4 matrices.
dataproduct1 = MiriImagingDistortionModel(
bmatrix=self.bmatrix,
amatrix=self.amatrix,
tmatrix=self.tmatrix,
mmatrix=self.mmatrix,
boresight_offsets=self.boresight_offsets)
self.assertTrue(np.allclose(dataproduct1.bmatrix, self.bmatrix))
self.assertTrue(np.allclose(dataproduct1.amatrix, self.amatrix))
self.assertTrue(np.allclose(dataproduct1.tmatrix, self.tmatrix))
self.assertTrue(np.allclose(dataproduct1.mmatrix, self.mmatrix))
descr = str(dataproduct1)
self.assertIsNotNone(descr)
del dataproduct1
# It should be possible to create a model containing A and B matrices
# only.
dataproduct2 = MiriImagingDistortionModel(bmatrix=self.bmatrix,
amatrix=self.amatrix)
self.assertTrue(np.allclose(dataproduct2.bmatrix, self.bmatrix))
self.assertTrue(np.allclose(dataproduct2.amatrix, self.amatrix))
descr = str(dataproduct2)
self.assertIsNotNone(descr)
del dataproduct2
# Attempting to create a data model with arrays of the wrong shape
# should fail.
amatrix1D = [0.1, 0.2, 0.3, 0.4]
bmatrix1D = [0.1, 0.0, 0.0, 0.0]
self.assertRaises(TypeError,
MiriImagingDistortionModel,
amatrix=amatrix1D,
bmatrix=bmatrix1D)
# It should be possible to set up an empty data product with
# a specified shape. Both arrays should be initialised to
# the same shape.
emptydp = MiriImagingDistortionModel((4, 4))
self.assertIsNotNone(emptydp.amatrix)
self.assertEqual(emptydp.amatrix.shape, (4, 4))
self.assertIsNotNone(emptydp.bmatrix)
self.assertEqual(emptydp.bmatrix.shape, (4, 4))
descr = str(emptydp)
self.assertIsNotNone(descr)
del emptydp, descr
# A null data product can also be created and populated
# with data later.
nulldp = MiriImagingDistortionModel()
descr1 = str(nulldp)
nulldp.amatrix = np.asarray(self.amatrix)
self.assertIsNotNone(nulldp.amatrix)
nulldp.bmatrix = np.asarray(self.bmatrix)
self.assertIsNotNone(nulldp.bmatrix)
descr2 = str(nulldp)
del nulldp, descr1, descr2
def test_fitsio(self):
# Suppress metadata warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# Check that the data product can be written to a FITS
# file and read back again without changing the data.
self.dataproduct.save(self.testfile, overwrite=True)
with MiriImagingDistortionModel(self.testfile) as readback:
self.assertEqual(self.dataproduct.meta.reftype,
readback.meta.reftype)
self.assertEqual(self.dataproduct.meta.model_type,
readback.meta.model_type)
self.assertTrue(
np.allclose(self.dataproduct.amatrix, readback.amatrix))
self.assertTrue(
np.allclose(self.dataproduct.bmatrix, readback.bmatrix))
del readback
class TestMiriImagingDistortionModel(unittest.TestCase):
# Test the MiriImagingDistortionModel class.
def setUp(self):
# Create a typical distortion product.
self.bmatrix = [[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8],
[0.8, 0.7, 0.6, 0.5], [0.4, 0.3, 0.2, 0.1]]
self.amatrix = [[0.1, 0.0, 0.0, 0.0], [0.0, 0.1, 0.0, 0.0],
[0.0, 0.0, 0.1, 0.0], [0.0, 0.0, 0.0, 0.1]]
self.tmatrix = [[0.5, 0.0, 0.0], [0.2, 0.5, 0.2], [0.0, 0.0, 0.5]]
self.mmatrix = [[0.0, 0.0, 0.1], [0.0, 0.1, 0.0], [0.1, 0.0, 0.0]]
self.boresight_offsets = [('One', 0.1, 0.2), ('Two', 0.2, 0.3)]
self.dataproduct = MiriImagingDistortionModel(
amatrix=self.bmatrix,
bmatrix=self.amatrix,
tmatrix=self.tmatrix,
mmatrix=self.mmatrix,
dmatrix=self.bmatrix,
cmatrix=self.amatrix,
fmatrix=self.bmatrix,
ematrix=self.amatrix,
boresight_offsets=self.boresight_offsets)
# Add some example metadata.
self.dataproduct.set_target_metadata(0.0, 0.0)
self.dataproduct.set_instrument_metadata(detector='MIRIFUSHORT',
channel='1',
ccc_pos='CLOSED',
deck_temperature=11.0,
detector_temperature=6.0)
self.dataproduct.set_exposure_metadata(readpatt='FAST',
nints=1,
ngroups=1,
frame_time=1.0,
integration_time=10.0,
group_time=10.0,
reset_time=0,
frame_resets=3)
self.testfile = "MiriImagingDistortionModel_test.fits"
def tearDown(self):
# Tidy up
del self.dataproduct
# TBD
# Remove temporary file, if able to.
if os.path.isfile(self.testfile):
try:
os.remove(self.testfile)
except Exception as e:
strg = "Could not remove temporary file, " + self.testfile + \
"\n " + str(e)
warnings.warn(strg)
def test_referencefile(self):
# Check that the data product contains the standard
# reference file metadata.
type1 = self.dataproduct.meta.model_type
type2 = self.dataproduct.meta.reftype
self.assertIsNotNone(type1)
self.assertIsNotNone(type2)
pedigree = self.dataproduct.meta.pedigree
self.assertIsNotNone(pedigree)
def test_creation(self):
# Check the successful creation of a data product with all 4 matrices.
dataproduct1 = MiriImagingDistortionModel(
bmatrix=self.bmatrix,
amatrix=self.amatrix,
tmatrix=self.tmatrix,
mmatrix=self.mmatrix,
boresight_offsets=self.boresight_offsets)
self.assertTrue(np.allclose(dataproduct1.bmatrix, self.bmatrix))
self.assertTrue(np.allclose(dataproduct1.amatrix, self.amatrix))
self.assertTrue(np.allclose(dataproduct1.tmatrix, self.tmatrix))
self.assertTrue(np.allclose(dataproduct1.mmatrix, self.mmatrix))
descr = str(dataproduct1)
self.assertIsNotNone(descr)
del dataproduct1
# It should be possible to create a model containing A and B matrices
# only.
dataproduct2 = MiriImagingDistortionModel(bmatrix=self.bmatrix,
amatrix=self.amatrix)
self.assertTrue(np.allclose(dataproduct2.bmatrix, self.bmatrix))
self.assertTrue(np.allclose(dataproduct2.amatrix, self.amatrix))
descr = str(dataproduct2)
self.assertIsNotNone(descr)
del dataproduct2
# Attempting to create a data model with arrays of the wrong shape
# should fail.
amatrix1D = [0.1, 0.2, 0.3, 0.4]
bmatrix1D = [0.1, 0.0, 0.0, 0.0]
self.assertRaises(TypeError,
MiriImagingDistortionModel,
amatrix=amatrix1D,
bmatrix=bmatrix1D)
# It should be possible to set up an empty data product with
# a specified shape. Both arrays should be initialised to
# the same shape.
emptydp = MiriImagingDistortionModel((4, 4))
self.assertIsNotNone(emptydp.amatrix)
self.assertEqual(emptydp.amatrix.shape, (4, 4))
self.assertIsNotNone(emptydp.bmatrix)
self.assertEqual(emptydp.bmatrix.shape, (4, 4))
descr = str(emptydp)
self.assertIsNotNone(descr)
del emptydp, descr
# A null data product can also be created and populated
# with data later.
nulldp = MiriImagingDistortionModel()
descr1 = str(nulldp)
nulldp.amatrix = np.asarray(self.amatrix)
self.assertIsNotNone(nulldp.amatrix)
nulldp.bmatrix = np.asarray(self.bmatrix)
self.assertIsNotNone(nulldp.bmatrix)
descr2 = str(nulldp)
del nulldp, descr1, descr2
def test_copy(self):
# Test that a copy can be made of the data product.
datacopy = self.dataproduct.copy()
self.assertIsNotNone(datacopy)
self.assertTrue(np.allclose(self.dataproduct.amatrix,
datacopy.amatrix))
self.assertTrue(np.allclose(self.dataproduct.bmatrix,
datacopy.bmatrix))
del datacopy
def test_fitsio(self):
# Suppress metadata warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# Check that the data product can be written to a FITS
# file and read back again without changing the data.
self.dataproduct.save(self.testfile, overwrite=True)
with MiriImagingDistortionModel(self.testfile) as readback:
self.assertEqual(self.dataproduct.meta.reftype,
readback.meta.reftype)
self.assertEqual(self.dataproduct.meta.model_type,
readback.meta.model_type)
self.assertTrue(
np.allclose(self.dataproduct.amatrix, readback.amatrix))
self.assertTrue(
np.allclose(self.dataproduct.bmatrix, readback.bmatrix))
del readback
def test_description(self):
# Test that the querying and description functions work.
# For the test to pass these need to run without error
# and generate non-null strings.
descr = str(self.dataproduct)
self.assertIsNotNone(descr)
del descr
descr = repr(self.dataproduct)
self.assertIsNotNone(descr)
del descr
descr = self.dataproduct.stats()
self.assertIsNotNone(descr)
del descr
print(help_text)
time.sleep(1) # Ensure help text appears before error messages.
parser.error("Not enough arguments provided")
sys.exit(1)
verb = options.verb
makeplot = options.makeplot
overwrite = options.overwrite
# Read the given file.
if verb:
print("Reading %s" % inputfile)
header, inputdata, = load_distortion_fits( inputfile )
# Convert the distortion data into a MIRI Distortion CDP
with MiriImagingDistortionModel( init=inputfile ) as oldproduct:
# Make a copy of the data product just created.
distortproduct = oldproduct.copy()
# Copy over distortion data
distortproduct.cmatrix = inputdata[0]
distortproduct.rmatrix = inputdata[1]
# default modifications for CDP-1 specifications
distortproduct.meta.filename_original = os.path.basename(inputfile)
distortproduct.meta.filename = os.path.basename(outputfile)
if verb:
print(distortproduct)
if makeplot: