def test_instr_setup(fitsdict):
""" Test instrument setup naming convention
"""
from pypit import arsciexp
arutils.dummy_settings(spectrograph='shane_kast_blue', set_idx=False)
# Load
settings.argflag['run']['setup'] = True # Over-ride default numbers
filesort = arsort.sort_data(fitsdict)
# Match and test
arsort.match_science(fitsdict, filesort)
# Make a science frame
sciexp = arsciexp.ScienceExposure(0, fitsdict, do_qa=False)
# Get an ID
setup_dict = {}
setupID = arsort.instr_setup(sciexp, 1, fitsdict, setup_dict)
assert setupID == 'A_01_aa'
# Should get same thing
setupID = arsort.instr_setup(sciexp, 1, fitsdict, setup_dict)
assert setupID == 'A_01_aa'
# New det (fake out kast_blue)
settings.spect['det02'] = dict(numamplifiers=1)
setupID2 = arsort.instr_setup(sciexp, 2, fitsdict, setup_dict)
assert setupID2 == 'A_02_aa'
# New calib set
settings.spect['arc']['index'][1] = np.array(
[9]) # Not really an arc, but ok
sciexp1 = arsciexp.ScienceExposure(1, fitsdict, do_qa=False)
setupID3 = arsort.instr_setup(sciexp1, 1, fitsdict, setup_dict)
assert setupID3 == 'A_01_ab'
assert setup_dict['A']['ab']['arcs'][0] == 'b009.fits'
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_load_headers():
arutils.dummy_settings(spectrograph='shane_kast_blue', set_idx=False)
kast_files = [data_path('b1.fits.gz'), data_path('b27.fits.gz')]
fitsdict, updates = arl.load_headers(kast_files)
# Test
headers = fitsdict['headers']
assert len(headers) == 2
assert headers[0][0]['OBJECT'] == 'Arcs'
def test_update_masters():
# Dummy self
arut.dummy_settings(spectrograph='shane_kast_blue', set_idx=True)
slf1 = arut.dummy_self()
slf1._idx_arcs = np.array([0, 1])
slf2 = arut.dummy_self()
sciexp = [slf1, slf2]
# Not actually filling anything
armb.UpdateMasters(sciexp, 0, 1, 'arc')
def test_user_frametype(fitsdict):
""" Test setting frametype manually
"""
arutils.dummy_settings(spectrograph='shane_kast_blue', set_idx=False)
# Modify settings -- WARNING: THIS IS GLOBAL!
settings.spect['set'] = {}
settings.spect['set']['standard'] = ['b009.fits']
filesort = arsort.sort_data(fitsdict)
assert 9 in filesort['standard']
settings.spect['set'] = {}
def test_sort_data(fitsdict):
""" Test sort_data
"""
arutils.dummy_settings(spectrograph='shane_kast_blue', set_idx=False)
# Sort
filesort = arsort.sort_data(fitsdict)
assert filesort['bias'][0] == 0
assert filesort['arc'][0] == 1
assert filesort['trace'][0] == 2
assert filesort['standard'][0] == 4
assert len(filesort['science']) == 5
def test_neg_match_science(fitsdict):
""" Test using negative number for calibs
"""
arutils.dummy_settings(spectrograph='shane_kast_blue', set_idx=False)
# Load
filesort = arsort.sort_data(fitsdict)
# Use negative number
for ftype in ['arc', 'pixelflat', 'trace', 'bias']:
settings.spect[ftype]['number'] = 1
settings.spect['trace']['number'] = -1
arsort.match_science(fitsdict, filesort)
assert len(settings.spect['trace']['index'][1]) == 2
def test_match_science(fitsdict):
""" Test match_science routine
"""
arutils.dummy_settings(spectrograph='shane_kast_blue', set_idx=False)
# Load
settings.argflag['run']['setup'] = True # Over-ride default numbers
filesort = arsort.sort_data(fitsdict)
# Match and test
arsort.match_science(fitsdict, filesort)
assert settings.spect['arc']['index'][1][0] == 1
assert settings.spect['standard']['index'][1][0] == 4
assert len(settings.spect['trace']['index'][0]) == 2
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_ampsec(fitsdict):
""" Test sort_data
"""
arutils.dummy_settings(spectrograph='shane_kast_blue')
slf = arutils.dummy_self()
# Run
det, scidx = 1, 5
arproc.get_datasec_trimmed(slf, fitsdict, det, scidx)
# Test
assert slf._datasec[det - 1].shape == (2112, 2048)
assert np.sum(np.isclose(slf._datasec[0], 1)) == 2162688 # Data region
assert np.sum(np.isclose(slf._datasec[0], 2)) == 2162688 # second amp
assert settings.spect['det01']['oscansec01'] == [[0, 0], [2049, 2080]]
assert settings.spect['det01']['datasec01'] == [[0, 0], [0, 1024]]
def test_flex_shift():
if not os.getenv('PYPIT'):
pass
else:
# Dummy slf
arut.dummy_settings()
settings.argflag['reduce']['flexure']['maxshift'] = 50
slf = arut.dummy_self()
# Read spectra
obj_spec = lsio.readspec(data_path('obj_lrisb_600_sky.fits'))
arx_file = pypit.__path__[0]+'/data/sky_spec/sky_LRISb_600.fits'
arx_spec = lsio.readspec(arx_file)
# Call
#msgs._debug['flexure'] = True
flex_dict = arwave.flex_shift(slf, 1, obj_spec, arx_spec)
assert np.abs(flex_dict['shift'] - 43.7) < 0.1
def test_geocorrect(fitsdict):
"""
"""
# Initialize some settings
arutils.dummy_settings(spectrograph='shane_kast_blue') #, set_idx=False)
# Load Dummy self
slf = arutils.dummy_self(fitsdict=fitsdict)
# Specobjs
specobjs = arutils.dummy_specobj(fitsdict, extraction=True)
slf._specobjs[0] = [specobjs]
# Run
# vhel = x_keckhelio(106.59770833333332, 30.34736111111111, 2000., jd=2457046.5036, OBS='lick') 9.3166 km/s
helio, hel_corr = py_arwave.geomotion_correct(slf, 1, fitsdict)
assert np.isclose(helio, -9.3344957,
rtol=1e-5) # Checked against x_keckhelio
assert np.isclose(slf._specobjs[0][0][0].boxcar['wave'][0].value,
3999.8754558341816,
rtol=1e-8)
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_load_linelist():
""" Touches nearly all the methods in ararclines
Returns
-------
"""
# Init
arutils.dummy_settings()
# Load
alist = alines.load_arcline_list(None,
None, ['CuI', 'ArI', 'NeI'],
None,
modify_parse_dict=dict(
NeI={'min_wave': 3000.},
ArI={'min_intensity': 399.}))
# Min NeI
NeI = alist['Ion'] == 'NeI'
np.testing.assert_allclose(np.min(alist['wave'][NeI]), 3455.1837999999998)
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 generate_hdf(sav_file, instr, lamps, outfil, dtoler=0.6):
""" Given an input LR IDL save file, generate an hdf5
IDs arc lines too
Parameters
----------
sav_file : str
Root name of the IDL save file from LowRedux, e.g. lris_blue_600.sav
lamps
outfil
Returns
-------
"""
from pypit import pyputils
msgs = pyputils.get_dummy_logger()
from pypit import arwave
from pypit import arutils
arutils.dummy_settings()
#
from arclines.pypit_utils import find_peaks
from arclines.io import load_line_lists
#
# Read IDL save file
sav_file = os.getenv('LONGSLIT_DIR') + 'calib/linelists/' + sav_file
s = readsav(sav_file)
ctbl = Table(s['calib']) # For writing later
# Line list
alist = load_line_lists(lamps)
# One spectrum?
ashape = s['archive_arc'].shape
if len(ashape) == 1:
nspec = 1
npix = ashape[0]
else:
nspec = s['archive_arc'].shape[0]
npix = ashape[1]
# Meta data
mdict = dict(
npix=npix,
instr=instr,
lamps=[str(ilamp) for ilamp in lamps], # For writing to hdf5
nspec=nspec,
infil=sav_file,
IDairvac='vac')
print("Processing {:d} spectra in {:s}".format(mdict['nspec'], sav_file))
# Start output
outh5 = h5py.File(out_path + outfil, 'w')
outh5.create_group('arcs')
# Loop on spectra
for ss in range(mdict['nspec']):
sss = str(ss)
# Parse
if nspec == 1:
spec = s['archive_arc']
else:
spec = s['archive_arc'][ss]
calib = s['calib'][ss]
# Peaks
tampl, tcent, twid, w, yprep = find_peaks(spec)
pixpk = tcent[w]
pixampl = tampl[w]
# Wavelength solution
if calib['func'] == 'CHEBY':
wv_air = cheby_val(calib['ffit'], np.arange(mdict['npix']),
calib['nrm'], calib['nord'])
elif calib['func'] == 'POLY':
wv_air = poly_val(calib['ffit'], np.arange(mdict['npix']),
calib['nrm'])
# Check blue->red or vice-versa
if ss == 0:
if wv_air[0] > wv_air[-1]:
mdict['bluered'] = False
else:
mdict['bluered'] = True
# Peak waves
if calib['func'] == 'CHEBY':
twave_air = cheby_val(calib['ffit'], pixpk, calib['nrm'],
calib['nord'])
else:
twave_air = poly_val(calib['ffit'], pixpk, calib['nrm'])
# Air to Vac
twave_vac = arwave.airtovac(twave_air * u.AA)
wave_vac = arwave.airtovac(wv_air * u.AA)
if ss == 0:
disp = np.median(np.abs(wave_vac - np.roll(wave_vac, 1)))
print("Average dispersion = {:g}".format(disp))
# IDs
idwv = np.zeros_like(pixpk)
idsion = np.array([str('12345')] * len(pixpk))
for kk, twv in enumerate(twave_vac.value):
# diff
diff = np.abs(twv - alist['wave'])
if np.min(diff) < dtoler:
imin = np.argmin(diff)
idwv[kk] = alist['wave'][imin]
#idsion[kk] = alist['Ion'][imin] NIST
idsion[kk] = alist['ion'][imin]
# Red to blue?
if mdict['bluered'] is False:
pixpk = mdict['npix'] - 1 - pixpk
# Re-sort
asrt = np.argsort(pixpk)
pixpk = pixpk[asrt]
idwv = idwv[asrt]
# Reverse
spec = spec[::-1]
wave_vac = wave_vac[::-1]
# Output
outh5['arcs'].create_group(sss)
# Datasets
outh5['arcs'][sss]['wave'] = wave_vac
outh5['arcs'][sss]['wave'].attrs['airvac'] = 'vac'
outh5['arcs'][sss]['spec'] = spec
outh5['arcs'][sss]['spec'].attrs['flux'] = 'counts'
outh5['arcs'][sss]['pixpk'] = pixpk
outh5['arcs'][sss]['ID'] = idwv
outh5['arcs'][sss]['ID'].attrs['airvac'] = 'vac'
outh5['arcs'][sss]['Ion'] = idsion
# LR wavelengths
outh5['arcs'][sss]['LR_wave'] = wv_air
outh5['arcs'][sss]['LR_wave'].attrs['airvac'] = 'air'
# LR Fit
outh5['arcs'][sss].create_group('LR_fit')
for key in ctbl.keys():
outh5['arcs'][sss]['LR_fit'][key] = ctbl[ss][key]
# Meta data
outh5.create_group('meta')
for key in mdict.keys():
try:
outh5['meta'][key] = mdict[key]
except TypeError: # Probably a unicode thing
pdb.set_trace()
# Close
outh5.close()
print('Wrote {:s}'.format(out_path + outfil))
def iterative_fitting(spec,
tcent,
ifit,
IDs,
llist,
disp,
plot_fil=None,
verbose=False,
load_pypit=False,
aparm=None):
if aparm is None:
aparm = dict(
llist='',
disp=disp, # Ang/unbinned pixel
disp_toler=0.1, # 10% tolerance
match_toler=3., # Matcing tolerance (pixels)
func='legendre', # Function for fitting
n_first=3, # Order of polynomial for first fit
n_final=4, # Order of polynomial for final fit
nsig_rej=2., # Number of sigma for rejection
nsig_rej_final=3.0) # Number of sigma for rejection (final fit)
# PYPIT
if load_pypit:
from pypit import pyputils
msgs = pyputils.get_dummy_logger()
from pypit import arutils
from pypit import arqa
if load_pypit:
arutils.dummy_settings()
npix = spec.size
# Setup for fitting
sv_ifit = list(ifit) # Keep the originals
all_ids = -999. * np.ones(len(tcent))
all_idsion = np.array(['UNKNWN'] * len(tcent))
all_ids[ifit] = IDs
# Fit
n_order = aparm['n_first']
flg_quit = False
fmin, fmax = -1., 1.
while (n_order <= aparm['n_final']) and (flg_quit is False):
# Fit with rejection
xfit, yfit = tcent[ifit], all_ids[ifit]
mask, fit = arutils.robust_polyfit(xfit,
yfit,
n_order,
function=aparm['func'],
sigma=aparm['nsig_rej'],
minv=fmin,
maxv=fmax)
rms_ang = arutils.calc_fit_rms(xfit[mask == 0],
yfit[mask == 0],
fit,
aparm['func'],
minv=fmin,
maxv=fmax)
rms_pix = rms_ang / disp
if verbose:
print("RMS = {:g}".format(rms_pix))
# DEBUG
# Reject but keep originals (until final fit)
ifit = list(ifit[mask == 0]) + sv_ifit
# Find new points (should we allow removal of the originals?)
twave = arutils.func_val(fit,
tcent,
aparm['func'],
minv=fmin,
maxv=fmax)
for ss, iwave in enumerate(twave):
mn = np.min(np.abs(iwave - llist['wave']))
if mn / aparm['disp'] < aparm['match_toler']:
imn = np.argmin(np.abs(iwave - llist['wave']))
#if verbose:
# print('Adding {:g} at {:g}'.format(llist['wave'][imn],tcent[ss]))
# Update and append
all_ids[ss] = llist['wave'][imn]
all_idsion[ss] = llist['ion'][imn]
ifit.append(ss)
# Keep unique ones
ifit = np.unique(np.array(ifit, dtype=int))
# Increment order
if n_order < (aparm['n_final'] + 2):
n_order += 1
else:
# This does 2 iterations at the final order
flg_quit = True
# Final fit (originals can now be rejected)
fmin, fmax = 0., 1.
xfit, yfit = tcent[ifit] / (npix - 1), all_ids[ifit]
mask, fit = arutils.robust_polyfit(xfit,
yfit,
n_order,
function=aparm['func'],
sigma=aparm['nsig_rej_final'],
minv=fmin,
maxv=fmax) #, debug=True)
irej = np.where(mask == 1)[0]
if len(irej) > 0:
xrej = xfit[irej]
yrej = yfit[irej]
if verbose:
for kk, imask in enumerate(irej):
wave = arutils.func_val(fit,
xrej[kk],
aparm['func'],
minv=fmin,
maxv=fmax)
print('Rejecting arc line {:g}; {:g}'.format(
yfit[imask], wave))
else:
xrej = []
yrej = []
xfit = xfit[mask == 0]
yfit = yfit[mask == 0]
ions = all_idsion[ifit][mask == 0]
# Final RMS
rms_ang = arutils.calc_fit_rms(xfit,
yfit,
fit,
aparm['func'],
minv=fmin,
maxv=fmax)
rms_pix = rms_ang / disp
#
'''
if msgs._debug['arc']:
msarc = slf._msarc[det-1]
wave = arutils.func_val(fit, np.arange(msarc.shape[0])/float(msarc.shape[0]),
'legendre', minv=fmin, maxv=fmax)
debugger.xplot(xfit,yfit, scatter=True,
xtwo=np.arange(msarc.shape[0])/float(msarc.shape[0]),
ytwo=wave)
debugger.xpcol(xfit*msarc.shape[0], yfit)
debugger.set_trace()
'''
#debugger.xplot(xfit, np.ones(len(xfit)), scatter=True,
# xtwo=np.arange(msarc.shape[0]),ytwo=yprep)
#debugger.xplot(xfit,yfit, scatter=True, xtwo=np.arange(msarc.shape[0]),
# ytwo=wave)
#debugger.set_trace()
#wave = arutils.func_val(fit, np.arange(msarc.shape[0])/float(msarc.shape[0]),
# 'legendre', min=fmin, max=fmax)
# Pack up fit
final_fit = dict(fitc=fit,
function=aparm['func'],
xfit=xfit,
yfit=yfit,
ions=ions,
fmin=fmin,
fmax=fmax,
xnorm=float(npix),
xrej=xrej,
yrej=yrej,
mask=mask,
spec=spec,
nrej=aparm['nsig_rej_final'],
shift=0.,
tcent=tcent,
rms=rms_pix)
# QA
if plot_fil is not None:
arqa.arc_fit_qa(None, final_fit, outfil=plot_fil)
# Return
return final_fit
