def rebin_spec(spec, wavnew, waveunits='um'):
from pysynphot import spectrum, observation
# Gives same error answer: Err = np.array([np.sqrt(sum(spec[2].value[idx_include(wavnew,[((wavnew[0] if n==0 else
# wavnew[n-1]+wavnew[n])/2,wavnew[-1] if n==len(wavnew) else (wavnew[n]+wavnew[n+1])/2)])]**2)) for n in range(
# len(wavnew)-1)])*spec[2].unit if spec[2] is not '' else ''
if len(spec) == 2:
spec += ['']
try:
Flx, Err, filt = spectrum.ArraySourceSpectrum(
wave=spec[0].value,
flux=spec[1].value), spectrum.ArraySourceSpectrum(
wave=spec[0].value, flux=spec[2].value
) if spec[2] else '', spectrum.ArraySpectralElement(
spec[0].value, np.ones(len(spec[0])), waveunits=waveunits)
except:
spec, wavnew = [i * q.Unit('') for i in spec], wavnew * q.Unit('')
Flx, Err, filt = spectrum.ArraySourceSpectrum(
wave=spec[0].value,
flux=spec[1].value), spectrum.ArraySourceSpectrum(
wave=spec[0].value, flux=spec[2].value
) if spec[2] else '', spectrum.ArraySpectralElement(
spec[0].value, np.ones(len(spec[0])), waveunits=waveunits)
return [
wavnew,
observation.Observation(Flx, filt, binset=wavnew.value,
force='taper').binflux * spec[1].unit,
observation.Observation(Err, filt, binset=wavnew.value,
force='taper').binflux *
spec[2].unit if spec[2] else np.ones(len(wavnew)) * spec[1].unit
]
def rebin_spec(wave, specin, wavnew):
spec = spectrum.ArraySourceSpectrum(wave=wave, flux=specin)
f = np.ones(len(wave))
filt = spectrum.ArraySpectralElement(wave, f, waveunits='angstrom')
obs = observation.Observation(spec, filt, binset=wavnew, force='taper')
return obs.binflux
def spec_res_downgrade(l_in, spec_in, l_out):
templ_spec = spectrum.ArraySourceSpectrum(wave=l_in, flux=spec_in)
white_filter = spectrum.ArraySpectralElement(l_out,\
np.ones(len(l_out)), waveunits='angstrom')
convolved_spec = observation.Observation(templ_spec,\
white_filter, binset=l_out, force='taper').binflux
return convolved_spec
def spectra_rebin(wave,flux,rebin_wave):
spect=spectrum.ArraySourceSpectrum(wave=wave.values,flux=flux.values)
f = np.ones(len(wave))
filt=spectrum.ArraySpectralElement(wave.values,f,waveunits='microns')
obs=observation.Observation(spect,filt,binset=rebin_wave,force='taper')
return obs.binflux
def rebin(new_wav, old_wav, flux):
f_ = np.ones(len(old_wav))
spec_ = pysynspec.ArraySourceSpectrum(wave=old_wav, flux=flux)
filt = pysynspec.ArraySpectralElement(old_wav, f_, waveunits='angstrom')
obs = observation.Observation(spec_, filt, binset=new_wav, force='taper')
newflux = obs.binflux
return newflux
def rebin_spec(wave, specin, wavnew):
'''(inwave (ndarray),influx (ndarray),outwave(ndarray)-> outflux
Correctly rebins spectra
'''
spec = spectrum.ArraySourceSpectrum(wave=wave, flux=specin)
f = nu.ones(len(wave))
filt = spectrum.ArraySpectralElement(wave, f, waveunits='angstrom')
obs = observation.Observation(spec, filt, binset=wavnew, force='taper')
return obs.binflux
def rebin_spec(wavelength, flux, waveout, keepneg=False):
spec = spectrum.ArraySourceSpectrum(wave=wavelength,
flux=flux,
keepneg=keepneg)
f = np.ones(len(flux))
filt = spectrum.ArraySpectralElement(wavelength.to(wavelength.unit).value,
f,
waveunits=str(wavelength.unit))
obs = observation.Observation(spec, filt, binset=waveout, force='taper')
return obs.binflux
def rebin_spec(wave, specin, wavnew):
'''
Given wavelength, a spectrum, and new wavelength array, this
function resamples the spectrum to match new array.
'''
import numpy as np
from pysynphot import observation
from pysynphot import spectrum
spec = spectrum.ArraySourceSpectrum(wave=wave, flux=specin, keepneg=True)
f = np.ones(len(wave))
filt = spectrum.ArraySpectralElement(wave, f, waveunits='angstrom')
obs = observation.Observation(spec, filt, binset=wavnew, force='taper')
return obs.binflux
def rebin_spec(wave, specin, wavnew):
"""
Rebin spectra to bins used in wavnew.
Ref: http://www.astrobetter.com/blog/2013/08/12/python-tip-re-sampling-spectra-with-pysynphot/
"""
from pysynphot import observation
from pysynphot import spectrum as pysynphot_spec
import numpy as np
spec = pysynphot_spec.ArraySourceSpectrum(wave=wave, flux=specin)
f = np.ones(len(wave))
filt = pysynphot_spec.ArraySpectralElement(wave, f, waveunits='angstrom')
obs = observation.Observation(spec, filt, binset=wavnew, force='taper')
return obs.binflux
def read_mk_sky_emission_ir(pwv=1.6, airmass=1.5):
path_to_file = inspect.getsourcefile(Vega)
directory = os.path.split(path_to_file)[0] + '/maunakea_files/'
pwv_suffix = {1.0: '10', 1.6: '16', 3.0: '30', 5.0: '50'}
am_suffix = {1.0: '10', 1.5: '15', 2.0: '20'}
if pwv not in pwv_suffix.keys():
print 'Invalid precipatable water vapor (PWV): ', pwv
print 'Choose from:'
print pwv_suffix.keys()
return
if airmass not in am_suffix.keys():
print 'Invalid airmass (AM): ', airmass
print 'Choose from:'
print am_suffix.keys()
return
mk_sky_file = 'mk_skybg_zm_'
mk_sky_file += pwv_suffix[pwv] + '_'
mk_sky_file += am_suffix[airmass] + '_ph.dat'
try:
t = Table.read(directory + mk_sky_file + '.fits')
except IOError:
t = Table.read(directory + mk_sky_file, format='ascii')
t.rename_column('col1', 'wave') # Units in nm
t.rename_column('col2', 'flux') # Units in ph/sec/arcsec^2/nm/m^2
t['wave'].unit = 'nm'
t['flux'].unit = 'photons s^-1 arcsec^-2 nm^-1 m^-2'
t.write(directory + mk_sky_file + '.fits')
# Convert to photlam flux units (m->cm and nm->Ang).
# Drop the arcsec density... but sill there
t['flux'] *= (1.0 / 100.0)**2 * (1.0 / 10.0)
t['flux'].unit = 'photons cm^-2 s^-1 Ang^-1 arcsec^-2'
spec = spectrum.ArraySourceSpectrum(wave=t['wave'], flux=t['flux'],
waveunits='nm', fluxunits=units.Photlam,
name='Maunakea Sky')
spec.convert(units.Angstrom)
return spec
def testrows(self):
try:
sp = spectrum.ArraySourceSpectrum(self.wv, self.fx)
except exceptions.DuplicateWavelength as e:
self.assertEqual(e.rows, 1)
def testpass(self):
self.wv = np.array([10, 20, 30, 40, 50, 100])
sp = spectrum.ArraySourceSpectrum(self.wv, self.fx)
def rebin_spec(wave, specin, wavnew): # wavnew is the new wave grid spec = spectrum.ArraySourceSpectrum(wave=wave, flux=specin) # configures wave/spectrum for pysynphot f = np.ones(len(wave)) # preserve all flux at all wavelengths (no throughput curve) filt = spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') # apply throughput curve 'filter' (not necessary for us?) obs = observation.Observation(spec, filt, binset=wavnew, force='taper') return obs.binflux
def ssp_rebin(logL_ssp, spec_ssp, dlogL_new, Lll=3250.):
'''
rebin a GRID of model spectrum to have an identical velocity
resolution to an input spectrum
intended to be used on a grid of models with wavelength varying
along final axis (in 3d array)
DEPENDS ON pysynphot, which may not be an awesome thing, but
it definitely preserves spectral integrity, and does not suffer
from drawbacks of interpolation (failing to average line profiles)
'''
dlogL_ssp = np.median(logL_ssp[1:] - logL_ssp[:-1])
f = dlogL_ssp / dlogL_new
# print 'zoom factor: {}'.format(f)
# print 'new array should have length {}'.format(logL_ssp.shape[0]*f)
# print spec_ssp.shape
# we want to only sample where we're sure we have data
CRVAL1_new = logL_ssp[0] - 0.5 * dlogL_ssp + 0.5 * dlogL_new
CRSTOP_new = logL_ssp[-1] + 0.5 * dlogL_ssp - 0.5 * dlogL_new
NAXIS1_new = int((CRSTOP_new - CRVAL1_new) / dlogL_new)
# start at exp(CRVAL1_new) AA, and take samples every exp(dlogL_new) AA
logL_ssp_new = CRVAL1_new + \
np.linspace(0., dlogL_new*(NAXIS1_new - 1), NAXIS1_new)
L_new = np.exp(logL_ssp_new)
L_ssp = np.exp(logL_ssp)
# now find the desired new wavelengths
spec = spectrum.ArraySourceSpectrum(wave=L_ssp, flux=spec_ssp)
f = np.ones_like(L_ssp)
filt = spectrum.ArraySpectralElement(wave=L_ssp,
throughput=f,
waveunits='angstrom')
obs = observation.Observation(spec, filt, binset=L_new, force='taper')
spec_ssp_new = obs.binflux
# the following are previous attempts to do this rebinning
'''
# first, interpolate to a constant multiple of the desired resolution
r_interm = int(1./f)
print r_interm
dlogL_interm = f * dlogL_new
print dlogL_interm
CDELT1_interm = dlogL_interm
CRVAL1_interm = logL_ssp[0] + 0.5*CDELT1_interm
CRSTOP_interm = logL_ssp[-1] - 0.5*CDELT1_interm
NAXIS1_interm = int((CRSTOP_interm - CRVAL1_interm) / CDELT1_interm)
logL_ssp_interm = CRVAL1_interm + np.linspace(
0., CDELT1_interm * (NAXIS1_interm - 1), NAXIS1_interm)
edges_interm = np.column_stack((logL_ssp_interm - 0.5*CDELT1_interm,
logL_ssp_interm + 0.5*CDELT1_interm))
spec_interp = interp1d(logL_ssp, spec_ssp)
spec_interm = spec_interp(logL_ssp_interm)
print spec_interm.shape
spec_ssp_new = zoom(spec_interm, zoom=[1., 1., 1./r_interm])[1:-1]
logL_ssp_new = zoom(logL_ssp_interm, zoom=1./r_interm)[1:-1]
print logL_ssp_new.shape'''
'''s = np.cumsum(spec_ssp, axis=-1)
# interpolate cumulative array
s_interpolator = interp1d(x=logL_ssp, y=s, kind='linear')
s_interpolated_l = s_interpolator(edges[:, 0])
s_interpolated_u = s_interpolator(edges[:, 1])
total_in_bin = np.diff(
np.row_stack((s_interpolated_l, s_interpolated_u)), n=1, axis=0)
spec_ssp_new = total_in_bin * (dlogL_new/dlogL_ssp)'''
return spec_ssp_new, logL_ssp_new
