# Initialize array for rebinned blurred stars:
cap_sdssbin = n.zeros((npars, naxis1), dtype=float)
# Do the blurring and rebinning:
for i in xrange(npars):
print(i)
blurspec = sdss_blur * data_flat[i]
pxspline = pxs.PixelSpline(hires_wavebound, sdss_blur * data_flat[i])
cap_sdssbin[i] = pxspline.resample(wavebound_sdss)
#i = -1L
#
#i += 1
#p.plot(10.**loglam_sdss, cap_sdssbin[i], drawstyle='steps-mid', hold=False)
# reshape the array:
out_shape = data[...,0].shape + (naxis1,)
cap_sdssbin.resize(out_shape)
out_infodict = copy.deepcopy(infodict)
out_infodict['version'] = infodict['version'] + '_lr'
out_infodict['filename'] = 'ndArch-' + out_infodict['class'] + '-' + out_infodict['version'] + '.fits'
out_infodict['coeff0'] = coeff0
out_infodict['coeff1'] = coeff1
io.write_ndArch(n.float32(cap_sdssbin), baselines, out_infodict)
#junk, bjunk, ijunk = io.read_ndArch(infodict['filename'])
cont_halpha = n.asarray([n.sum(sdss_meanspec[i] * wtest * deltawave_sdss) /
n.sum(deltawave_sdss * wtest) for i in xrange(nsub_age)])
# Let's try the following range of Halpha equivalent widths:
ew_ha = n.asarray([0., 3.0, 9.0])
n_ew = len(ew_ha)
# Broadcast-fu to add the line spectra and expand the grid:
full_sdssbin = cont_halpha.reshape((-1,1,1,1)) * \
ew_ha.reshape((1,1,-1,1)) * \
sdss_line_spec.reshape((1,1,1,-1)) + \
ssp_sdssbin.reshape((nsub_age,log_vnum,1,naxis1))
baselines = [n.log10(ssp_agegyr[idx]), log_vbase, ew_ha]
this_class = 'ssp_galaxy_cont'
this_version = 'v002'
infodict = {'par_names': n.asarray(['log10-age', 'log10-vdisp', 'EW_Halpha']),
'par_units': n.asarray(['log10-Gyr', 'log10-km/s', 'rest-frame Angstroms']),
'par_axistype': n.asarray(['regular', 'regular', 'irregular']),
'coeff0': coeff0, 'coeff1': coeff1,
'fluxunit': '10^'+str(exp_div)+' erg/s/Ang/M_sun_init',
'filename': 'ndArch-'+this_class+'-'+this_version+'.fits'}
io.write_ndArch(n.float32(full_sdssbin), baselines, infodict)
#junk, bjunk, ijunk = io.read_ndArch(infodict['filename'])
wavebound_rebin = 10.**logbound_rebin
# Initialize array for rebinned blurred SSPs:
ssp_rebin = n.zeros((nsub_age, naxis1), dtype=float)
# Do the rebinning:
for i_age in xrange(nsub_age):
print(i_age)
pxspline = pxs.PixelSpline(ssp_wavebound,
rebin_blur * ssp_flam[idx[i_age]])
ssp_rebin[i_age] = pxspline.resample(wavebound_rebin)
baselines = [n.log10(ssp_agegyr[idx])]
this_class = 'ssp_hires_galaxy'
this_version = 'v001'
infodict = {
'par_names': n.asarray(['log10-age']),
'par_units': n.asarray(['log10-Gyr']),
'par_axistype': n.asarray(['regular']),
'coeff0': coeff0,
'coeff1': coeff1,
'fluxunit': '10^' + str(exp_div) + ' erg/s/Ang/M_sun_init',
'filename': 'ndArch-' + this_class + '-' + this_version + '.fits'
}
io.write_ndArch(n.float32(ssp_rebin), baselines, infodict)
#junk, bjunk, ijunk = io.read_ndArch(infodict['filename'])
# Initialize array for rebinned blurred SSPs:
ssp_rebin = n.zeros((n_vdisp, nsub_age, naxis1), dtype=float)
# Do the rebinning:
for j_v in xrange(n_vdisp):
print('vdisp step ', j_v, ' of ', n_vdisp)
# Desired output sigma on baseline of the input SSPs:
sig_wave_final = ssp_wave * vdisp_base[j_v] / c_kms
# Subtract starting dispersion in quadrature:
sig_wave_net = n.sqrt(sig_wave_final**2 - ssp_dwave**2)
rebin_blur = misc.gaussproj(ssp_wavebound, sig_wave_net, wavebound_rebin)
for i_age in xrange(nsub_age): ssp_rebin[j_v,i_age] = rebin_blur * ssp_flam[idx[i_age]]
baselines = [vdisp_base, n.log10(ssp_agegyr[idx])]
this_class = 'ssp_hires_galaxy'
this_version = 'v002'
infodict = {'par_names': n.asarray(['vdisp', 'log10-age']),
'par_units': n.asarray(['km/s', 'log10-Gyr']),
'par_axistype': n.asarray(['regular', 'regular']),
'coeff0': coeff0, 'coeff1': coeff1,
'fluxunit': '10^'+str(exp_div)+' erg/s/Ang/M_sun_init',
'filename': 'ndArch-'+this_class+'-'+this_version+'.fits'}
io.write_ndArch(n.float32(ssp_rebin), baselines, infodict)
#junk, bjunk, ijunk = io.read_ndArch(infodict['filename'])
# Initialize array for rebinned blurred stars:
cap_sdssbin = n.zeros((npars, naxis1), dtype=float)
# Do the blurring and rebinning:
for i in xrange(npars):
print(i)
blurspec = sdss_blur * data_flat[i]
pxspline = pxs.PixelSpline(hires_wavebound, sdss_blur * data_flat[i])
cap_sdssbin[i] = pxspline.resample(wavebound_sdss)
#i = -1L
#
#i += 1
#p.plot(10.**loglam_sdss, cap_sdssbin[i], drawstyle='steps-mid', hold=False)
# reshape the array:
out_shape = data[..., 0].shape + (naxis1, )
cap_sdssbin.resize(out_shape)
out_infodict = copy.deepcopy(infodict)
out_infodict['version'] = infodict['version'] + '_lr'
out_infodict['filename'] = 'ndArch-' + out_infodict[
'class'] + '-' + out_infodict['version'] + '.fits'
out_infodict['coeff0'] = coeff0
out_infodict['coeff1'] = coeff1
io.write_ndArch(n.float32(cap_sdssbin), baselines, out_infodict)
#junk, bjunk, ijunk = io.read_ndArch(infodict['filename'])
