def synphotSNR(filtname,
mag,
etime=1200,
nexp=4,
aperture=0.4,
pixscale=0.128):
import pysynphot as syn
from numpy import pi, sqrt
bandpass = syn.ObsBandpass('wfc3,ir,%s,aper#%.1f' % (filtname, aperture))
sn1a = syn.FileSpectrum('sn1a_flux_max.dat').redshift(1.5).renorm(
mag, 'abmag', bandpass)
sourcecps = syn.Observation(sn1a, bandpass).countrate()
srccounts = sourcecps * etime
skybandpass = syn.ObsBandpass('wfc3,ir,%s' % filtname)
earthshine = syn.FileSpectrum('earthshine_average.dat')
earthcps_arcsec2 = syn.Observation(earthshine, skybandpass,
force='extrap').countrate()
V = syn.ObsBandpass('landolt,V')
zodiacal = syn.FileSpectrum('zodiacal_high.dat').renorm(22.7, 'vegamag', V)
zodcps_arcsec2 = syn.Observation(zodiacal, skybandpass,
force='extrap').countrate()
aparea_arcsec2 = pi * aperture**2
aparea_pix = aparea_arcsec2 / pixscale**2
npix = (aparea_arcsec2 / pixscale**2)
sharpness = 3.3 / npix
skycounts = (earthcps_arcsec2 + zodcps_arcsec2) * aparea_arcsec2 * etime
skycounts_pix = skycounts / npix
# TODO : get thermal noise, dark current and readnoise from SYNPHOT
darkcps_arcsec2 = 3.05
darkcounts = darkcps_arcsec2 * aparea_arcsec2 * etime
darkcounts_pix = darkcounts / npix
thermcps_arcsec2 = 8.15
thermcounts = thermcps_arcsec2 * aparea_arcsec2 * etime
thermcounts_pix = thermcounts / npix
rdnoise_pix = 12.5 # read noise per pixel (per exposure)
rdnoiseTot = rdnoise_pix * aparea_pix # 161.61
bgcounts_pix = skycounts_pix + darkcounts_pix + thermcounts_pix
noise = sqrt(srccounts + npix * bgcounts_pix +
npix * nexp * rdnoise_pix**2)
SNR = srccounts / noise
onoise = sqrt(srccounts + (bgcounts_pix + rdnoise_pix**2) / sharpness)
oSNR = srccounts / onoise
return SNR, oSNR
def compute_zodi(ra, dec, jd, FILTER='F140W', verbose=False):
"""
Get the predicted Zodiacal surface brightness and then fold it through the Synphot WFC3 filters
"""
import pysynphot as S
thermal = {
'F160W': 0.134,
'F140W': 0.069,
'F105W': 0.051,
'F110W': 0.05,
'F125W': 0.052,
'F128N': 0.051,
'F130N': 0.051,
'F132N': 0.051,
'F164N': 0.0651,
'G141': 0.1,
'G102': 0.04,
'F098M': 0.05
}
lat, lng = helio_lat_lng(ra, dec, jd)
SB = get_zodi_SB(lat, lng)
if verbose:
print 'Lat, Lng: %f, %f, SB=%.2f' % (lat, lng,
SB + 2.5 * np.log10(0.128254**2))
zod = S.FileSpectrum('%s/zodiacal_model_001.fits' % (datapath()))
nz = zod.renorm(SB, S.units.VegaMag, S.ObsBandpass("V"))
bp = S.ObsBandpass('wfc3,ir,%s' % (FILTER.lower()))
obs = S.Observation(nz, bp)
return obs.countrate() + thermal[FILTER]
def __init__(self,
template,
refband=VJohnson,
refmag=-21.0,
ebmv=0.,
extlaw='xgal',
filters=default_filters,
cosmo=cosmo_def):
self.template = template
self.sp = S.FileSpectrum(template)
if ebmv > 0:
self.sp = self.sp * S.Extinction(ebmv, extlaw)
if type(refband) == type(1500.):
self.refband = S.Box(refband, 100.)
else:
self.refband = refband
self.refmag = refmag
self.filternames = [f.name for f in filters]
self.kcorrs = {}
for f in filters:
self.kcorrs[f.name] = kcorr.KCorrect(self.sp, self.refband, f)
if type(cosmo) == type([]):
self.cosmo = cosmoclass.cosmoclass(H0=cosmo[0],
omega_m=cosmo[1],
omega_l=cosmo[2])
else:
self.cosmo = cosmo
def get_pickles_spectrum(spt, verbose=False):
'''
A function that retuns a pysynphot pickles spectrum for a given spectral type
'''
#Read in the pickles master list.
pickles_dir = os.environ['PYSYN_CDBS'] + "grid/pickles/dat_uvk/"
pickles_filename = pickles_dir + "pickles_uk.fits"
pickles_table = np.array(fits.open(pickles_filename)[1].data)
pickles_filenames = [x[0].decode().replace(" ", "") for x in pickles_table]
pickles_spts = [x[1].decode().replace(" ", "") for x in pickles_table]
#The spectral types output by EXOSIMS are sometimes annoying
spt = spt.replace(" ", "").split("/")[-1]
#Sometimes there are fractional spectral types. Rounding to nearest integer
spt_split = spt.split(".")
if np.size(spt_split) > 1:
spt = spt_split[0] + spt_split[1][1:]
#Get the index of the relevant pickles spectrum filename
try:
ind = pickles_spts.index(spt)
except:
if verbose:
print("Couldn't match spectral type {} to the pickles library".
format(spt))
print("Assuming 'G0V'")
ind = pickles_spts.index('G0V')
sp = ps.FileSpectrum(pickles_dir + pickles_filenames[ind] + ".fits")
sp.convert("Micron")
sp.convert("photlam")
return sp
def compute_photometry(catalog, photmode):
""" Compute magnitudes for sources from catalog based on observations photmode
Parameters
-----------
catalog : object
Astropy Table with 'source_sum' column for the measured flux for each source
photmode : String
Specification of the observation filter configuration used for the exposure
as reported by the 'PHOTMODE' keyword from the PRIMARY header.
Returns
--------
phot_cat : object
Astropy Table object of input source catalog with added column for
VEGAMAG photometry (in magnitudes).
"""
# Determine VEGAMAG zero-point using pysynphot for this photmode
photmode = photmode.replace(' ', ',')
vega = S.FileSpectrum(VEGASPEC)
bp = S.ObsBandpass(photmode)
vegauvis = S.Observation(vega, bp)
vegazpt = 2.5 * np.log10(vegauvis.countrate())
# Use zero-point to convert flux values from catalog into magnitudes
source_phot = vegazpt - 2.5 * np.log10(catalog['source_sum'])
source_phot.name = 'vegamag'
# Now add this new column to the catalog table
catalog.add_column(source_phot)
return catalog
def testclip(self):
self.sp = S.FileSpectrum(self.fname)
self.obs = Observation(self.sp, self.bp)
rate = self.obs.countrate()
self.assertTrue(abs(rate - self.refrate) < 1.0,
msg="tst %f ref %f" % (rate, self.refrate))
def test_johnson_v_tabular_spectra():
bp = pysyn.FileBandpass("data/test_bp_johnson_v.fits",
thrucol="THROUGHPUT")
sp = pysyn.FileSpectrum("data/test_sp_vega.fits")
obs = pysyn.Observation(sp, bp)
obs *= 1e20
result(obs, 411896149580, 0, 8893)
def setUp(self):
testdata = os.path.join(locations.rootdir, 'calspec',
'feige66_002.fits')
self.sp = S.FileSpectrum(testdata)
self.bandpass = S.ObsBandpass('acs,hrc,f555w')
self.refrate = 8.30680E+05
self.reflam = 5304.462
def test_hst_tabular_spectra():
bp = pysyn.FileBandpass("data/test_bp_acs_hrc_f555w.fits",
thrucol="THROUGHPUT")
sp = pysyn.FileSpectrum("data/test_sp_vega.fits")
obs = pysyn.Observation(sp, bp)
obs *= 1e20
result(obs, 112231834769, 0, 15866)
def __call__(self, z_range, obsband, outputfile):
# Because m(z) = M + DM(z) + K_QR(z) := M + dm,
# so dm = DM + K_QR.
dm = np.zeros(len(z_range))
sp = S.FileSpectrum(self.sedfile)
dust = S.Extinction(self.ebmv, self.extlaw)
sp_ext = sp * dust
K = kcorr.KCorrect(sp_ext, self.Q, obsband, mode='AB')
for i in range(len(z_range)):
z = z_range[i]
## Use my own (clumsy) way to compute dm
# m = self.factory.redshift(z_range[i]).ABmag(obsband)
# dm[i] = m - self.normmag
# self.factory.reset()
## Use my implementation of Hogg et al. K-correction
dm[i] = self.cosmo.distmod(z) + K(z)
if i % 20 == 0:
sys.stdout.write('z = %.3f \r' % z_range[i])
sys.stdout.flush()
f = open(outputfile, 'wb')
f.write('# 1 z \n')
f.write('# 2 dm \n')
for i in range(len(z_range)):
f.write('%.4f %.6f \n' % (z_range[i], dm[i]))
f.close()
print "Done!"
def get_many_bc95():
'''
Get many Galaxy profiles
'''
SEDfile_dir = os.path.join(top_pysynphot_data_dir, dir_nostar,
dir_submodels[5], 'templates')
filelist = os.listdir(SEDfile_dir)
obj_headers = []
obj_files = []
index = 0
for filename in filelist:
if re.search('fits', filename): #example of filename filter
index += 1
fullfilename = os.path.join(SEDfile_dir, filename)
hdr = fits.getheader(fullfilename)
obj_headers.append(hdr)
obj_files.append(filename)
obj_names = []
index = 0
for hdr in obj_headers:
obj_name = obj_headers[index]['TARGETID']
obj_names.append(obj_name)
index += 1
objames_and_objfiles = zip(obj_names, obj_files)
OBJDict = {}
for obj, thefile in objames_and_objfiles:
#print obj,': '
OBJDict[obj] = thefile
#print OBJDict[obj]
all_sed = []
all_z_rs = np.linspace(0, 2., 10)
#for keyobj in OBJDict:
for obj, thefile in objames_and_objfiles:
#the_file=OBJDict[keyobj]
#selected_file=the_file
selected_file = thefile
selected_fullfile = os.path.join(SEDfile_dir, selected_file)
sed = S.FileSpectrum(selected_fullfile)
sed.convert('flam') # to be sure every spectrum is in flam unit
# loop on Redshifts
for z_rs in all_z_rs:
if (z_rs > 0):
sed_z = sed.redshift(z_rs)
all_sed.append(sed_z)
else:
all_sed.append(sed)
return all_sed
def next(self):
if self.pointer >= len(self.lines):
raise StopIteration
a = self.lines[self.pointer].split()
s = S.FileSpectrum(a[0])
s.otherstuff = " ".join(a[1:])
self.pointer += 1
return s
def Vegamag(spec, band): # added by Kuang-Han Huang
vega = S.FileSpectrum(pytool_path + '/sedtools/alpha_lyr_stis_003.fits')
# make sure that this vega spectrum is in the same directory
vegaflux = (vega * band).integrate()
# vegaflux corresponds to vegamag 0 in any band
flux = (spec * band).integrate()
vegamag = -2.5 * log10(flux / vegaflux)
return vegamag
def setUp(self):
self.fname='blank.dat'
fh=open(self.fname,'w')
self.len=writedata(fh)
for k in range(5):
fh.write("\n")
fh.close()
self.sp=S.FileSpectrum(self.fname)
def setUp(self):
self.fname='head2.dat'
fh=open(self.fname,'w')
fh.write("# 1 wave\n")
fh.write("# 2 flux\n")
self.len=writedata(fh)
fh.close()
self.sp=S.FileSpectrum(self.fname)
def test_hst_tabular_spectra_binset():
bp = pysyn.FileBandpass("data/test_bp_acs_hrc_f555w.fits",
thrucol="THROUGHPUT")
sp = pysyn.FileSpectrum("data/test_sp_vega.fits")
binset = np.asarray([1000, 2000, 5000, 11000])
obs = pysyn.Observation(sp, bp, binset=binset)
obs *= 1e20
result(obs, 112231834769, 0, 15866)
def get_many_bpgs():
all_sed = []
SEDfile_dir = os.path.join(top_pysynphot_data_dir, dir_nostar,
dir_submodels[1])
fits_files = [f for f in os.listdir(SEDfile_dir) if f.endswith('.fits')]
obj_headers = []
obj_files = []
for filename in fits_files:
index = 0
if re.search('fits', filename): #example of filename filter
index += 1
fullfilename = os.path.join(SEDfile_dir, filename)
hdr = fits.getheader(fullfilename)
obj_headers.append(hdr)
obj_files.append(filename)
obj_names = []
index = 0
for hdr in obj_headers:
obj_name = obj_headers[index]['TARGETID']
obj_names.append(obj_name)
index += 1
obj_names2 = []
index = 0
for thefile in fits_files:
#thenames=re.findall('^bk_([a-z][0-9]+).fits$',thefile)
thenames = re.findall('^bpgs_([0-9].*).fits$', thefile)
if (len(thenames) > 0):
obj_names2.append('bpgs_' + thenames[0])
else:
print 'bad file ', thefile
index += 1
obj_names = obj_names2
objames_and_objfiles = zip(obj_names, obj_files)
OBJDict = {}
for obj, thefile in objames_and_objfiles:
OBJDict[obj] = thefile
for keyobj in OBJDict:
the_file = OBJDict[keyobj]
selected_file = the_file
selected_fullfile = os.path.join(SEDfile_dir, selected_file)
sed = S.FileSpectrum(selected_fullfile)
sed.convert('flam') # to be sure every spectrum is in flam unit
all_sed.append(sed)
return all_sed
def setUp(self):
self.fname='midcomment.dat'
fh=open(self.fname,'w')
x1=writedata(fh,end=2000)
fh.write("#middle of the file\n")
fh.write("# yet more middle of the file\n")
x2=writedata(fh,start=2000)
fh.close()
self.len=x1+x2
self.sp=S.FileSpectrum(self.fname)
def fit_beta_sed(sedfile, beta0, ebmv=0.):
# a wrapper that takes an SED, read it with pysynphot, and find the best-fit beta
sp = S.FileSpectrum(sedfile)
if ebmv > 0.:
sp = sp * S.Extinction(ebmv, 'xgal')
wave = sp.wave[(sp.wave >= 1220.) &
(sp.wave <= 3200.)] # the rest-frame wavelength range
sedflux = sp.sample(wave) # the flux from sp
x = fit_beta(wave, sedflux, beta0=beta0)
return x[0][0]
def setUp(self):
self.bb = S.BlackBody(5000)
self.em = S.GaussianSource(3300, 1, 1)
self.flat = S.FlatSpectrum(10)
self.pl = S.PowerLaw(5000, -2)
self.tspec = S.ArraySpectrum(self.bb.wave,
self.bb.flux,
fluxunits=self.bb.fluxunits)
self.pl.writefits('ac_pl.fits')
self.fspec = S.FileSpectrum('ac_pl.fits')
def zeropoint(self):
ps.setref(**self.REFS)
standard_star_file = GetStipsData(
os.path.join("standards", "alpha_lyr_stis_008.fits"))
sp = ps.FileSpectrum(standard_star_file)
sp.convert('angstroms')
bp = self.bandpass
sp = sp.renorm(0.0, "VEGAMAG", bp)
obs = ps.Observation(sp, bp, binset=sp.wave)
zeropoint = obs.effstim("OBMAG")
return zeropoint
def setUp(self):
self.fname = os.path.join(locations.rootdir, 'calspec',
'feige66_002.fits')
self.old_sp = S.FileSpectrum(self.fname)
self.openfits = pyfits.open(self.fname)
fdata = self.openfits[1].data
self.new_sp = S.ArraySpectrum(
wave=fdata.field('wavelength'),
flux=fdata.field('flux'),
waveunits=self.openfits[1].header['tunit1'],
fluxunits=self.openfits[1].header['tunit2'],
name='table from feige66')
def add_spectrum_to_library():
spec_dict = {}
tab = ascii.read('CTTS_etc_d140pc_101116.txt', names=['wave','flux'])
sp = S.ArraySpectrum(wave=tab['wave'], flux=tab['flux'], waveunits='Angstrom', fluxunits='flam')
ctts = sp.renorm(21., 'abmag', S.ObsBandpass('galex,fuv'))
spec_dict['Classical T Tauri'] = ctts
tab = ascii.read('dM1_etc_d5pc_101116.txt', names=['wave','flux'])
sp = S.ArraySpectrum(wave=tab['wave'], flux=tab['flux'], waveunits='Angstrom', fluxunits='flam')
Mdwarf = sp.renorm(21., 'abmag', S.ObsBandpass('galex,fuv'))
spec_dict['M1 Dwarf'] = Mdwarf
tab = ascii.read('10Myr_Starburst_nodust.dat', names=['wave', 'flux'])
sp = S.ArraySpectrum(wave=tab['wave'], flux=tab['flux'], waveunits='Angstrom', fluxunits='flam')
s99 = sp.renorm(21., 'abmag', S.ObsBandpass('galex,fuv'))
spec_dict['10 Myr Starburst'] = s99
filename_qso = os.path.join(os.environ['PYSYN_CDBS'], 'grid', 'agn', 'qso_template.fits')
qso = S.FileSpectrum(filename_qso)
q = qso.renorm(21., 'abmag', S.ObsBandpass('galex,fuv'))
spec_dict['QSO'] = q
filename_star = os.path.join(os.environ['PYSYN_CDBS'], 'grid', 'pickles', 'dat_uvk', 'pickles_uk_1.fits')
star = S.FileSpectrum(filename_star)
s = star.renorm(21., 'abmag', S.ObsBandpass('galex,fuv'))
spec_dict['O5V Star'] = s
filename_star = os.path.join(os.environ['PYSYN_CDBS'], 'grid', 'pickles', 'dat_uvk', 'pickles_uk_26.fits')
star = S.FileSpectrum(filename_star)
s = star.renorm(21., 'abmag', S.ObsBandpass('galex,fuv'))
spec_dict['G2V Star'] = s
filename_star = os.path.join(os.environ['PYSYN_CDBS'], 'grid', 'galactic', 'orion_template.fits')
star = S.FileSpectrum(filename_star)
s = star.renorm(21., 'abmag', S.ObsBandpass('galex,fuv'))
spec_dict['Orion Nebula'] = s
filename_star = os.path.join(os.environ['PYSYN_CDBS'], 'calspec', 'g191b2b_mod_010.fits')
star = S.FileSpectrum(filename_star)
s = star.renorm(21., 'abmag', S.ObsBandpass('galex,fuv'))
spec_dict['G191B2B'] = s
filename_star = os.path.join(os.environ['PYSYN_CDBS'], 'grid', 'kc96', 'starb1_template.fits')
star = S.FileSpectrum(filename_star)
s = star.renorm(21., 'abmag', S.ObsBandpass('galex,fuv'))
spec_dict['Starburst, No Dust'] = s
filename_star = os.path.join(os.environ['PYSYN_CDBS'], 'grid', 'kc96', 'starb6_template.fits')
star = S.FileSpectrum(filename_star)
s = star.renorm(21., 'abmag', S.ObsBandpass('galex,fuv'))
spec_dict['Starburst, E(B-V) = 0.6'] = s
return spec_dict
def add_models_and_beams(grp, source_id, beam_id, grism_filt, spectrum):
""" Add models and create beam cutouts.
Parameters
----------
grp : GroupFLT object
The GroupFLT grizli object. A collection of files,
models, extensions, etc.
source_id : int
The the seg id of the source that needs extracting.
beam_id : str
The fringe/beam_id to model.
grism_filt : str
The grism filter.
spectrum : str
Either 'skip' to not do a more complex model, or the
path to a data file with a source spectra.
Returns
-------
beams : list of beam objects
List of beam objects, which is like a GroupFLT but
centered around these beam cutouts tracing the
grism.
"""
# Start with general model
grp.compute_full_model()
# Build beam objects
beams = grp.get_beams(source_id, size=24, beam_id=beam_id)
if spectrum == 'skip':
return beams
else:
mb = grizli.multifit.MultiBeam(beams, fcontam=0.2, psf=True)
# Read in alternate data
sp = S.FileSpectrum(spectrum)
bp = S.ObsBandpass('wfc3,ir,' + grism_filt)
rn = sp.renorm(1, 'flam', bp)
# Cast to floats
rn_wave = np.cast[float](rn.wave)
rn_flux = np.cast[float](rn.flux)
mb.compute_model(spectrum_1d=[rn_wave, rn_flux])
beams = mb.beams
return beams
def testcrspec(self):
iraf.thermback(obsmode=self.obsmode, form='counts', output=self.csname)
spref = S.FileSpectrum(self.csname)
self.savepysyn(spref.wave,
self.sptest.sample(spref.wave),
self.csname,
units='counts')
ridx = N.where(spref.wave >= 900.0)
rflux = spref.flux[ridx]
tflux = self.sptest.sample(spref.wave[ridx])
self.arraysigtest(tflux, rflux)
def do_calc(src_type, mag_src, obs_wave, exp_time, coadds, dither,
dither_repeat, seeing, num_reads, redshift):
global qso_sp, sy1_sp, sy2_sp
qso = os.path.join(datadir, 'qso_template.fits')
qso_sp = S.FileSpectrum(qso).redshift(redshift)
sy1 = os.path.join(datadir, 'seyfert1_template.fits')
sy1_sp = S.FileSpectrum(sy1).redshift(redshift)
sy2 = os.path.join(datadir, 'seyfert2_template.fits')
sy2_sp = S.FileSpectrum(sy2).redshift(redshift)
## fixed instrument parameters
slit_width = 0.55 #arcsec
slit_lenth = 18.0 #arcsec
print(src_type)
if src_type == "PointSource":
s2n = calculate_nires_s2n_pointsource(mag_src, obs_wave, exp_time,
coadds, dither, dither_repeat,
seeing, num_reads)
print("S/N = %f" % s2n)
elif src_type == "qso":
s2n = calculate_nires_s2n_qso(src_type, redshift, obs_wave, exp_time,
coadds, dither, dither_repeat, seeing,
num_reads)
print("S/N = %f" % s2n)
elif src_type == "sy1":
s2n = calculate_nires_s2n_qso(src_type, redshift, obs_wave, exp_time,
coadds, dither, dither_repeat, seeing,
num_reads)
print("S/N = %f" % s2n)
elif src_type == "sy2":
s2n = calculate_nires_s2n_qso(src_type, redshift, obs_wave, exp_time,
coadds, dither, dither_repeat, seeing,
num_reads)
print("S/N = %f" % s2n)
else:
s2n = 'N/A'
return s2n
def renorm_compare(sp, rnval, rnunits, bp, callable):
"""Sp and bp must have a special .syndescrip attribute that
contains the string needed to make it with synphot"""
# Renormalize the spectrum
pysyn = callable(sp, bp, rnval, rnunits)
#pysyn=sp.renorm(rnval,rnunits,bp)
#pysyn = spectrum.StdRenorm(sp,bp,rnval,rnunits)
userdir = tempfile.mkdtemp(suffix='pysynphot')
old_cwd = os.getcwd()
iraf.chdir(userdir)
# Make a wavetable and a wavecat
fname = "%s.fits" % rnunits
pysyn.writefits(fname, clobber=True)
wname = "%s.cat" % rnunits
f = open(wname, 'w')
f.write("box %s\n" % fname)
f.close()
oname = "syn_%s" % fname
try:
#Run countrate
spstring = "rn(%s,%s,%s,%s) " % (sp.syndescrip,
bp.syndescrip, rnval,
rnunits)
iraf.countrate(spectrum=spstring, magnitude="",
instrument="box(15000,30000)",
form=str(pysyn.fluxunits), wavecat=wname,
output=oname)
syn = S.FileSpectrum(oname)
# Check that they have the same shape and fluxunits
assert (syn.flux.shape == pysyn.flux.shape)
assert (type(syn.fluxunits) == type(pysyn.fluxunits))
# Now a real test
idx = np.where(syn.flux != 0)
rat = (syn.flux[idx] / pysyn.flux[idx])
q = abs(1 - rat[2:-2])
qtrunc = (10**4*q).astype(np.int)
assert np.alltrue(qtrunc < 110), \
"Min/max ratio = %f,%f" % (q.min(),q.max())
finally:
iraf.chdir(old_cwd)
shutil.rmtree(userdir)
def getSpectrum(self,Teff=5800,z=0.,logg=4.43,vega=False,): #default: Sun at 10pc Teff=5800, z=0, logg=4.4, johnson_v_abs=4.68
self.johnson_v = S.ObsBandpass('johnson,v')
self.T=Teff
self.z=z
self.g=logg
if vega:
self.star= S.FileSpectrum(S.locations.VegaFile)
print ' Using default Pysynphot Vega spectrum'
else:
print self.getFiltername(), self.getSpectrumInfo()
self.star= S.Icat('ck04models',self.T,self.z,self.g) #FLAM surface flux units, i.e. ergs cm-2 s-1 A-1
#star = S.BlackBody(6000)
#self.star= self.star.renorm(self.j_v_abs,'vegamag',self.johnson_v) #renorm spectrum to (abs_mag, units system, band)
return self.star
def get_all_pickle():
all_sed = [] # common SED container
SEDfile_dir = os.path.join(top_pysynphot_data_dir, dir_nostar,
dir_submodels[9], 'dat_uvi')
fits_files = [f for f in os.listdir(SEDfile_dir) if f.endswith('.fits')]
fits_files.remove('pickles.fits')
obj_headers = []
obj_files = []
for filename in fits_files:
index = 0
if re.search('fits', filename): #example of filename filter
index += 1
fullfilename = os.path.join(SEDfile_dir, filename)
hdr = fits.getheader(fullfilename)
obj_headers.append(hdr)
obj_files.append(filename)
obj_names2 = []
index = 0
for thefile in fits_files:
#thenames=re.findall('^bk_([a-z][0-9]+).fits$',thefile)
thenames = re.findall('^(.*).fits$', thefile)
if (len(thenames) > 0):
obj_names2.append(thenames[0])
else:
print 'bad file ', thefile
index += 1
obj_names = obj_names2
objames_and_objfiles = zip(obj_names, obj_files)
OBJDict = {}
for obj, thefile in objames_and_objfiles:
#print obj,': '
OBJDict[obj] = thefile
#print OBJDict[obj]
for keyobj in OBJDict:
the_file = OBJDict[keyobj]
selected_file = the_file
selected_fullfile = os.path.join(SEDfile_dir, selected_file)
sed = S.FileSpectrum(selected_fullfile)
sed.convert('flam') # to be sure every spectrum is in flam unit
all_sed.append(sed)
return all_sed
def setUp(self):
self.oldcwd = os.getcwd()
os.chdir(os.path.dirname(__file__))
self.oldref = S.refs.getref()
S.setref(comptable='$PYSYN_CDBS/mtab/t260548pm_tmc.fits')
self.spectrum = "data/qso_template.fits"
self.obsmode = "cos,fuv,g140l,c1230,PSA"
self.bp = S.ObsBandpass(self.obsmode)
self.sp = S.FileSpectrum(self.spectrum)
self.tst = self.sp.renorm(17.0, 'vegamag', self.bp)
self.tda = dict(spectrum=self.spectrum,
obsmode=self.obsmode,
file=S.__file__)
