def evaluate(x, temperature):
"""Evaluate the model.
Parameters
----------
x : number or ndarray
Wavelengths in Angstrom.
temperature : number
Temperature in Kelvin.
Returns
-------
y : number or ndarray
Blackbody radiation in PHOTLAM per steradian.
"""
if ASTROPY_LT_2_0:
from astropy.analytic_functions.blackbody import blackbody_nu
else:
from astropy.modeling.blackbody import blackbody_nu
# Silence Numpy
old_np_err_cfg = np.seterr(all='ignore')
wave = np.ascontiguousarray(x) * u.AA
bbnu_flux = blackbody_nu(wave, temperature)
bbflux = (bbnu_flux * u.sr).to(
units.PHOTLAM, u.spectral_density(wave)) / u.sr # PHOTLAM/sr
# Restore Numpy settings
np.seterr(**old_np_err_cfg)
return bbflux.value
def evaluate(x, temperature):
"""Evaluate the model.
Parameters
----------
x : number or ndarray
Wavelengths in Angstrom.
temperature : number
Temperature in Kelvin.
Returns
-------
y : number or ndarray
Blackbody radiation in PHOTLAM per steradian.
"""
# Silence Numpy
old_np_err_cfg = np.seterr(all="ignore")
wave = np.ascontiguousarray(x) * u.AA
bbnu_flux = blackbody_nu(wave, temperature)
bbflux = (bbnu_flux * u.sr).to(units.PHOTLAM, u.spectral_density(wave)) / u.sr # PHOTLAM/sr
# Restore Numpy settings
np.seterr(**old_np_err_cfg)
return bbflux.value
def evaluate(x, temperature):
"""Evaluate the model.
Parameters
----------
x : number or ndarray
Wavelengths in Angstrom.
temperature : number
Temperature in Kelvin.
Returns
-------
y : number or ndarray
Blackbody radiation in PHOTLAM per steradian.
"""
# Silence Numpy
old_np_err_cfg = np.seterr(all='ignore')
wave = u.Quantity(np.ascontiguousarray(x), unit=u.AA)
bbnu_flux = blackbody_nu(wave, temperature)
bbflux = (bbnu_flux * u.sr).to(
units.PHOTLAM, u.spectral_density(wave)) / u.sr # PHOTLAM/sr
# Restore Numpy settings
dummy = np.seterr(**old_np_err_cfg)
return bbflux.value
def makeplot_v2(photdat, norm_wave=8, normval = None, specfile='../pb_m31_spectra/nucFLUX',spec_area =1500):
'''plots photometry and some spectra on same plot'''
# difference btw this one and makeplot() is how the normalization is done
photwaves = photdat['Wavelength'] # known wavelengths
photvals = photdat['MJy_counts']
if normval == None:
normval = photvals[np.searchsorted(photwaves, norm_wave)]
# load the IRS spectrum and convert to MJy
nuc_wave,nuc_irs = np.loadtxt(specfile,unpack=True, usecols=[0,1])
nuc_irs = nuc_irs*((spec_area*u.arcsec**2).to(u.sr).value)
# normalize
nuc_irs = spect_norm(nuc_wave,nuc_irs, norm_wave, normval)
# read the nu Pav spectrum
nupav_wave, nupav_flux = np.loadtxt('nu_pav_spect.txt',unpack=True,usecols=[0,1])
# normalize
nupav_flux = spect_norm(nupav_wave,nupav_flux, norm_wave, normval)
# create a RJ tail to compare to
bb_wl = np.arange(1.0,22,0.4)
bb = blackbody_nu(bb_wl*u.micron,5000)
# normalize
bb = spect_norm(bb_wl,bb, norm_wave, normval)
# plot
f,ax=plt.subplots()
ax.plot(bb_wl, bb.value*1e6, ls='dashed', color='k',marker=None, lw=2, label = '5000K BB' )
ax.plot(nupav_wave, nupav_flux*1e6, ls = 'solid', color='k',marker=None, lw=2,label='nu Pav')
ax.plot(nuc_wave,nuc_irs*1e6,ls='solid',marker=None, lw=2,label= 'M31 IRS')
ax.plot(photwaves[5:],photvals[5:]*1e6,ms=10,label='IRAC') # factor 1e6 makes plot in Jy, gives nice scale
ax.set_xlabel('Wavelength [micron]')
ax.set_ylabel('Flux density [Jy]')
ax.legend(loc='best')
ax.set_xlim(3,22)
ax.set_ylim(0,4)
return
def makeplot(photdat=None, specfile='../pb_m31_spectra/nucFLUX',spec_area =1500):
'''plots photometry and some spectra on same plot'''
if photdat == None:
# do photometry
photdat = dophot(imglist)
photwaves = photdat['Wavelength'] # known wavelengths
photvals = photdat['MJy_counts']
# load the IRS spectrum and convert to MJy
nuc_wave,nuc_irs = np.loadtxt(specfile,unpack=True,usecols=[0,1])
nuc_irs = nuc_irs*((spec_area*u.arcsec**2).to(u.sr).value)
# read the nu Pav spectrum
nupav_wave, nupav_flux = np.loadtxt('nu_pav_spect.txt',unpack=True,usecols=[0,1])
# normalize
find_8micron = np.searchsorted(nupav_wave,8)
nupav_flux = nupav_flux*(photvals[-1]/nupav_flux[find_8micron])
# create a RJ tail to compare to
bb_wl = np.arange(1.0,22,0.4)
bb = blackbody_nu(bb_wl*u.micron,5000)
# normalize it to the IRAC flux at 8um
find_8micron = np.searchsorted(bb_wl,8)
bb = bb*(photvals[-1]/bb[find_8micron].value)
# plot
f,ax=plt.subplots()
ax.plot(nuc_wave,nuc_irs*1e6,ls='solid',marker=None, lw=2,label= 'M31 IRS')
ax.plot(bb_wl, bb.value*1e6, ls='dashed', color='k',marker=None, lw=2, label = '5000K BB' )
ax.plot(nupav_wave, nupav_flux*1e6, ls = 'solid', color='k',marker=None, lw=2,label='nu Pav')
ax.plot(photwaves[0:2],photvals[0:2]*1e6,ms=10,label='HST') # factor 1e6 makes plot in Jy, gives nice scale
ax.plot(photwaves[2:5],photvals[2:5]*1e6,ms=10,label='2MASS') # factor 1e6 makes plot in Jy, gives nice scale
ax.plot(photwaves[5:],photvals[5:]*1e6,ms=10,label='IRAC') # factor 1e6 makes plot in Jy, gives nice scale
ax.set_xlabel('Wavelength [micron]')
ax.set_ylabel('Flux density [Jy]')
ax.legend(loc='best')
ax.set_xlim(0,22)
# ax.set_ylim(0,10)
return(photvals)
