def test_get_aper_from_model_msa():
"""For a given exposures aperture, make sure the correct
aperture reference data is returned for MSA mode"""
datmod = PathlossModel()
datmod.apertures.append({'shutters': 5})
datmod.meta.exposure.type = 'NRS_MSASPEC'
result = get_aperture_from_model(datmod, 5)
assert (result == datmod.apertures[0])
def test_get_aper_from_model_fixedslit():
"""For a given exposures aperture, make sure the correct
aperture reference data is returned for fixedslit mode"""
datmod = PathlossModel()
datmod.apertures.append({'name': 'S200A1'})
datmod.meta.exposure.type = 'NRS_FIXEDSLIT'
result = get_aperture_from_model(datmod, 'S200A1')
assert (result == datmod.apertures[0])
def test_do_correction_nis_soss_pupil_position_is_none():
"""If pupil_position is None, skip correction"""
datmod = MultiSlitModel()
pathlossmod = PathlossModel()
datmod.meta.exposure.type = 'NIS_SOSS'
datmod.meta.visit.tsovisit = False
datmod.meta.instrument.pupil_position = None
result = do_correction(datmod, pathlossmod)
assert (result.meta.cal_step.pathloss == 'SKIPPED')
def test_do_correction_fixed_slit_exception():
"""If no matching aperture name found, exit."""
datmod = MultiSlitModel()
# Give input_model aperture name
datmod.slits.append({'data':np.array([]), 'name':'S200A1'})
# Do assign pathloss model aperture with similar name.
pathlossmod = PathlossModel()
datmod.meta.exposure.type = 'NRS_FIXEDSLIT'
result, _ = do_correction(datmod, pathlossmod)
assert(result.meta.cal_step.pathloss == 'COMPLETE')
def test_do_correction_nis_soss_aperture_is_none():
"""If no matching aperture is found, skip correction."""
datmod = MultiSlitModel()
# Is FULL an option for NIRISS?
# The test doesn't care but something to remember.
datmod.slits.append({'data':np.array([]), 'name':'FULL'})
# Don't assign pathloss model aperture with similar name
pathlossmod = PathlossModel()
datmod.meta.exposure.type = 'NIS_SOSS'
datmod.meta.visit.tsovisit = False
datmod.meta.instrument.pupil_position = 1
result, _ = do_correction(datmod, pathlossmod)
assert(result.meta.cal_step.pathloss == 'SKIPPED')
def test_calculate_pathloss_vector_interpolation():
"""Calculate the pathloss vector for when interpolation is necessary."""
datmod = PathlossModel()
ref_data = {
'pointsource_data': np.ones((10, 10, 10), dtype=np.float32),
'pointsource_wcs': {
'crval2': -0.5,
'crpix2': 1.0,
'cdelt2': 0.5,
'cdelt3': 1.0,
'crval1': -0.5,
'crpix1': 1.0,
'crpix3': 1.0,
'crval3': 1.0,
'cdelt1': 0.5
}
}
datmod.apertures.append(ref_data)
wavelength, pathloss, is_inside_slitlet = calculate_pathloss_vector(
datmod.apertures[0].pointsource_data,
datmod.apertures[0].pointsource_wcs, 0.0, 0.0)
# Wavelength array is calculated with this: crval3 +(float(i+1) - crpix3)*cdelt3
# Where i is the iteration of np.arange(wavesize) which is the 1st dimension of the pointsource
# data array.
wavelength_comparison = np.array(
[1 + (float(i + 1) - 1.0) * 1 for i in np.arange(10)])
assert (np.all(wavelength == wavelength_comparison))
# In this instance we interpolate to get the array for pathloss VS wavelength.
# With the current values inside of the of the pointsource_wcs starting at line 143 of pathloss.py
# dx1 = 1 - int(1) = 0.0
# dx2 = 1 - dx1 = 1.0
# dy1 = 1 - int(1) = 0.0
# dy2 = 1 - dx1 = 1.0
# This means a11 == a12 == a21 == 0 and a22 == 1
# This simplifies that pathloss vector to:
# pathloss_vector = (a22*pathloss_ref[:,i,j]) = (1*pathloss_ref[:1,j])
# Thus pathloss == the input array to the function.
pathloss_comparison = datmod.apertures[0].pointsource_data
assert (np.all(pathloss == pathloss_comparison))
# With the current wcs values, the logic should be returning True
assert (is_inside_slitlet == True)
def test_calculate_pathloss_vector_pointsource_data():
"""Calculate pathloss vector for 3D pathloss data"""
datmod = PathlossModel()
ref_data = {
'pointsource_data': np.ones((10, 10, 10), dtype=np.float32),
'pointsource_wcs': {
'crval2': -0.5,
'crpix2': 1.0,
'cdelt2': 0.05,
'cdelt3': 1,
'crval1': -0.5,
'crpix1': 1.0,
'crpix3': 1.0,
'crval3': 1,
'cdelt1': 0.05
}
}
datmod.apertures.append(ref_data)
wavelength, pathloss, is_inside_slitlet = calculate_pathloss_vector(
datmod.apertures[0].pointsource_data,
datmod.apertures[0].pointsource_wcs, 0.0, 0.0)
# Wavelength array is calculated with this: crval3 +(float(i+1) - crpix3)*cdelt3
# Where i is the iteration of np.arange(wavesize) which is the 1st dimension of the pointsource
# data array.
wavelength_comparison = np.array(
[1 + (float(i + 1) - 1.0) * 1 for i in np.arange(10)])
assert (np.allclose(wavelength, wavelength_comparison))
# pathloss vector gets assigned at beginning of calculate_pathloss_vector and in this
# case, doesnt change (np.zeros(wavesize, dtype=np.float32))
pathloss_comparison = np.zeros(10, dtype=np.float32)
assert (np.all(pathloss == pathloss_comparison))
# With the current wcs values, the logic should be returning False
assert (is_inside_slitlet == False)
def test_calculate_pathloss_vector_uniform_data():
"""Calculate the pathloss vector for uniform data arrays."""
datmod = PathlossModel()
ref_data = {'uniform_data':np.ones((10,), dtype=np.float32),
'uniform_wcs': {'crpix1': 1.0, 'cdelt1': 1, 'crval1': 1}}
datmod.apertures.append(ref_data)
wavelength, pathloss, _ = calculate_pathloss_vector(datmod.apertures[0].uniform_data,
datmod.apertures[0].uniform_wcs,
0.0, 0.0)
# Wavelength array is calculated with this: crval1 +(float(i+1) - crpix1)*cdelt1
# Where i is the iteration of np.arange(wavesize) which is the shape of the uniform
# data array.
comparison = np.array([1 +(float(i+1) - 1)*1 for i in np.arange(10)])
assert(np.all(wavelength==comparison))
# The same array is returned in this case
assert(np.all(datmod.apertures[0].uniform_data == pathloss))