def test_gen_sensfunc():
# Load a random spectrum for the sensitivity function
sfile = data_path('spec1d_J0025-0312_KASTr_2015Jan23T025323.85.fits')
specobjs = arload.load_specobj(sfile)
# Settings, etc.
arutils.dummy_settings()
settings.argflag['run']['spectrograph'] = 'shane_kast_blue'
settings.argflag['reduce']['masters']['setup'] = 'C_01_aa'
settings.spect['arc'] = {}
settings.spect['arc']['index'] = [[0]]
fitsdict = arutils.dummy_fitsdict()
slf = arutils.dummy_self()
slf._msstd[0]['RA'] = '05:06:36.6'
slf._msstd[0]['DEC'] = '52:52:01.0'
# Generate
slf._sensfunc = arflx.generate_sensfunc(slf, 4, [specobjs], fitsdict)
# Save
try:
os.mkdir('MF_shane_kast_blue')
except FileExistsError:
pass
armasters.save_sensfunc(slf, 'C_01_aa')
# Test
assert isinstance(slf._sensfunc, dict)
assert isinstance(slf._sensfunc['wave_min'], Quantity)
def test_save1d_hdf5():
""" save1d to FITS and HDF5
"""
# Dummy self
slf = arut.dummy_self()
fitsdict = arut.dummy_fitsdict(nfile=1, spectrograph='none')
# specobj
slf._specobjs = []
slf._specobjs.append([])
slf._specobjs[0].append([mk_specobj(objid=455), mk_specobj(flux=3., objid=555)])
# Write to HDF5
arsv.save_1d_spectra_hdf5(slf, fitsdict)
def test_save1d_fits():
""" save1d to FITS and HDF5
"""
arut.dummy_settings()
fitsdict = arut.dummy_fitsdict(nfile=10, spectrograph='shane_kast_blue', directory=data_path(''))
# Dummy self
slf = arut.dummy_self()
slf._specobjs = []
slf._specobjs.append([])
slf._specobjs[0].append([mk_specobj()])
# Write to FITS
arsv.save_1d_spectra_fits(slf, fitsdict)
def test_setup_param():
""" Run the parameter setup script
Returns
-------
"""
# Initialize some settings
arut.dummy_settings()
# Load Dummy self
slf = arut.dummy_self()
settings.argflag['run']['spectrograph'] = 'shane_kast_blue'
settings.spect['arc'] = {}
settings.spect['arc']['index'] = [[0]]
fitsdict = arut.dummy_fitsdict()
# Run
arcparm = pyarc.setup_param(slf, 0, 1, fitsdict)
for key in ['llist', 'disp', 'wvmnx']:
assert key in arcparm
def test_save2d_fits():
arut.dummy_settings()
# Dummy self
slf = arut.dummy_self()
fitsdict = arut.dummy_fitsdict(nfile=1, spectrograph='none', directory=data_path(''))
fitsdict['filename'] = np.array(['b1.fits.gz'])
# Settings
settings.argflag['run']['directory']['science'] = data_path('')
settings.argflag['reduce']['masters']['setup'] = 'A_01_aa'
# Fill with dummy images
dum = np.ones((100,100))
slf._sciframe[0] = dum
slf._modelvarframe[0] = dum * 2
slf._bgframe[0] = dum + 0.1
slf._basename = 'test'
slf._idx_sci[0] = 0
# Call
arsv.save_2d_images(slf, fitsdict)
# Read and test
head0 = pyfits.getheader(data_path('spec2d_test.fits'))
assert head0['PYPCNFIG'] == 'A'
assert head0['PYPCALIB'] == 'aa'
assert 'PYPIT' in head0['PIPELINE']
def fitsdict():
return arutils.dummy_fitsdict()