def compare_missing_mass(tab, mlb='i', cb1='g', cb2='r'):
'''make figure showing effect of aperture correction on total mass
Figure shows P+ (red) and S (blue) samples,
with their integrated color, difference between aperture-corrected
masses using CMLR and ring method
Parameters
----------
tab : astropy.table.Table
full aggregation results table
mlb : {str}, optional
bandpass for mass-to-light ratio
(the default is 'i', which [default_description])
cb1 : {str}, optional
bluer bandpass for color (the default is 'g', SDSS g band)
cb2 : {str}, optional
redder bandpass for color (the default is 'r', SDSS r band)
'''
cb1_nsa_mag = tab[f'mag_nsa_{cb1}']
cb2_nsa_mag = tab[f'mag_nsa_{cb2}']
broadband_color = cb1_nsa_mag - cb2_nsa_mag
outermass_cmlr = (tab[f'logsollum_outer_{mlb}'] + tab['outerml_cmlr']).to(
u.Msun)
mass_cmlr = (tab['mass_in_ifu'].to(u.Msun) + outermass_cmlr)
outermass_ring = (tab[f'logsollum_outer_{mlb}'] + tab['outerml_ring']).to(
u.Msun)
mass_ring = (tab['mass_in_ifu'].to(u.Msun) + outermass_ring)
dlogmass_cmlr_ring = mass_cmlr.to(u.dex(u.Msun)) - mass_ring.to(u.dex(u.Msun))
primarysample = m.mask_from_maskbits(tab['mngtarg1'], [10])
secondarysample = m.mask_from_maskbits(tab['mngtarg1'], [11])
fig, main_ax, hist_ax = make_panel_hist(top=0.9, left=.225)
valid = np.isfinite(dlogmass_cmlr_ring)
for selection, label, marker, color in zip(
[primarysample, secondarysample], ['Primary', 'Secondary'],
['o', 'D'], ['r', 'b']):
main_ax.scatter(
x=broadband_color[selection * valid], y=dlogmass_cmlr_ring[selection * valid],
c=color, edgecolor='None', s=3., marker=marker, label=label,
alpha=0.5)
hist_ax.hist(dlogmass_cmlr_ring[selection * valid], color=color, density=True, bins='auto',
histtype='step', orientation='horizontal', linewidth=0.75)
main_ax.set_xlim(np.percentile(broadband_color[valid], [5., 99.]))
main_ax.set_ylim(np.percentile(dlogmass_cmlr_ring[valid], [5., 99.]))
main_ax.legend(loc='best', prop={'size': 'xx-small'})
main_ax.tick_params(labelsize='xx-small')
main_ax.set_xlabel(r'${}-{}$'.format(cb1, cb2), size='x-small')
main_ax.set_ylabel(r'$\log \frac{M^{\rm tot}_{\rm CMLR}}{M^{\rm tot}_{\rm ring}}$',
size='x-small')
fig.suptitle(r'Impact of aperture-correction on $M^{\rm tot}$', size='small')
fig.savefig(os.path.join(csp_basedir, 'lib_diags/', 'mtot_compare_cmlr_ring.png'))
def make_missing_flux_fig(tab, mlb='i', cb1='g', cb2='r'):
mlb_ix = totalmass.StellarMass.bands_ixs[mlb]
cb1_ix = totalmass.StellarMass.bands_ixs[cb1]
cb2_ix = totalmass.StellarMass.bands_ixs[cb2]
absmag_sun_mlb = totalmass.StellarMass.absmag_sun[mlb_ix]
broadband_color = (tab['nsa_absmag'][:, cb1_ix] - tab['nsa_absmag'][:, cb2_ix])
outerlum_frac = tab['outer_lum'][:, mlb_ix].to(m.bandpass_sol_l_unit) / \
tab['nsa_absmag'][:, mlb_ix].to(m.bandpass_sol_l_unit,
totalmass.bandpass_flux_to_solarunits(absmag_sun_mlb))
primarysample = m.mask_from_maskbits(tab['mngtarg1'], [10])
secondarysample = m.mask_from_maskbits(tab['mngtarg1'], [11])
fig, main_ax, hist_ax = make_panel_hist(top=0.875)
for selection, label, marker, color in zip(
[primarysample, secondarysample], ['Primary+', 'Secondary'],
['o', 'D'], ['r', 'b']):
main_ax.scatter(
x=broadband_color[selection], y=outerlum_frac[selection],
c=color, edgecolor='None', s=5., marker=marker, label=label)
hist_ax.hist(outerlum_frac[selection], color=color, density=True, bins='auto',
histtype='step', orientation='horizontal', linewidth=0.75)
main_ax.set_xlim(np.percentile(broadband_color, [1., 99.]))
main_ax.legend(loc='best', prop={'size': 'xx-small'})
main_ax.tick_params(labelsize='xx-small')
main_ax.set_xlabel(r'${}-{}$'.format(cb1, cb2), size='x-small')
main_ax.set_ylabel('Flux fraction outside IFU', size='x-small')
fig.suptitle('Flux fraction outside IFU', size='small')
fig.savefig(os.path.join(basedir, 'lib_diags/', 'flux_outside_ifu.png'))
def __init__(self, results, pca_system, drp, dap, drpall_row, cosmo, mlband='i'):
self.results = results
self.pca_system = pca_system
self.drp = drp
self.dap = dap
self.drpall_row = drpall_row
self.cosmo = cosmo
self.mlband = mlband
with catch_warnings():
simplefilter('ignore')
self.results.setup_photometry(pca_system)
self.s2p = results.spec2phot
# stellar mass to light ratio
self.ml0 = results.cubechannel('ML{}'.format(mlband), 0)
self.badpdf = results.cubechannel('GOODFRAC', 2) < 1.0e-4
self.ml_mask = np.logical_or.reduce((self.results.mask, self.badpdf))
# infer values in interior spaxels affected by one of the following:
# bad PDF, foreground star, dead fiber
self.low_or_no_cov = m.mask_from_maskbits(
self.drp['MASK'].data, [0, 1]).mean(axis=0) > .3
self.drp3dmask_interior = m.mask_from_maskbits(
self.drp['MASK'].data, [2, 3]).mean(axis=0) > .3
self.interior_mask = np.logical_or.reduce((self.badpdf, self.drp3dmask_interior))
self.logml_final = infer_masked(
q_trn=self.ml0, bad_trn=self.ml_mask,
infer_here=self.interior_mask) * u.dex(m.m_to_l_unit)
def make_missing_mass_fig(tab, mltype='ring', mlb='i', cb1='g', cb2='r'):
'''figure with comparison between aperture correction two M/L
Parameters
----------
tab : astropy.table.Table
full aggregation results table
mltype : {str}
'ring' or 'cmlr', the M/L applied to the ouside flux
(the default is 'ring')
mlb : {str}, optional
bandpass for mass-to-light ratio
(the default is 'i')
cb1 : {str}, optional
bluer bandpass for color (the default is 'g', SDSS g band)
cb2 : {str}, optional
redder bandpass for color (the default is 'r', SDSS r band)
'''
cb1_nsa_mag = tab[f'mag_nsa_{cb1}']
cb2_nsa_mag = tab[f'mag_nsa_{cb2}']
broadband_color = cb1_nsa_mag - cb2_nsa_mag
outermass = (tab[f'logsollum_outer_{mlb}'] + tab[f'outerml_{mltype}']).to(
u.Msun)
outermass_frac = outermass / (
tab['mass_in_ifu'].to(u.Msun) + outermass.to(u.Msun))
valid = np.isfinite(tab['outerml_{}'.format(mltype)])
primarysample = m.mask_from_maskbits(tab['mngtarg1'], [10])
secondarysample = m.mask_from_maskbits(tab['mngtarg1'], [11])
fig, main_ax, hist_ax = make_panel_hist(top=0.875)
for selection, label, marker, color in zip(
[primarysample, secondarysample], ['Primary+', 'Secondary'],
['o', 'D'], ['r', 'b']):
main_ax.scatter(
x=broadband_color[selection * valid], y=outermass_frac[selection * valid],
c=color, edgecolor='None', s=3., marker=marker, label=label,
alpha=0.5)
hist_ax.hist(outermass_frac[selection * valid], color=color, density=True, bins='auto',
histtype='step', orientation='horizontal', linewidth=0.75)
main_ax.set_xlim([0.1, 0.9])
main_ax.legend(loc='best', prop={'size': 'xx-small'})
main_ax.tick_params(labelsize='xx-small')
main_ax.set_xlabel(r'${}-{}$'.format(cb1, cb2), size='x-small')
main_ax.set_ylabel('Stellar-mass fraction outside IFU', size='x-small')
fig.suptitle('Inferred mass fraction outside IFU', size='small')
fig.savefig(os.path.join(csp_basedir, 'lib_diags/', f'mass_outside_ifu_{mltype}.png'))
def make_missing_flux_fig(tab, mlb='i', cb1='g', cb2='r'):
'''make figure comparing missing flux for P+ & S samples
Figure shows P+ (red) and S (blue) samples,
with their integrated color, fraction of bandpass flux outside IFU
Parameters
----------
tab : astropy.table.Table
full aggregation results table
mlb : {str}, optional
bandpass for mass-to-light ratio
(the default is 'i', which [default_description])
cb1 : {str}, optional
bluer bandpass for color (the default is 'g', SDSS g band)
cb2 : {str}, optional
redder bandpass for color (the default is 'r', SDSS r band)
'''
cb1_nsa_mag = tab[f'mag_nsa_{cb1}']
cb2_nsa_mag = tab[f'mag_nsa_{cb2}']
broadband_color = cb1_nsa_mag - cb2_nsa_mag
outerlum_frac = tab[f'flux_outer_{mlb}'] / tab[f'flux_nsa_{mlb}']
primarysample = m.mask_from_maskbits(tab['mngtarg1'], [10])
secondarysample = m.mask_from_maskbits(tab['mngtarg1'], [11])
fig, main_ax, hist_ax = make_panel_hist(top=0.875)
for selection, label, marker, color in zip(
[primarysample, secondarysample], ['Primary+', 'Secondary'],
['o', 'D'], ['r', 'b']):
main_ax.scatter(
x=broadband_color[selection], y=outerlum_frac[selection],
c=color, edgecolor='None', s=3., marker=marker, label=label,
alpha=0.5)
hist_ax.hist(outerlum_frac[selection], color=color, density=True, bins='auto',
histtype='step', orientation='horizontal', linewidth=0.75)
main_ax.set_xlim([0.1, 0.9])
main_ax.legend(loc='best', prop={'size': 'xx-small'})
main_ax.tick_params(labelsize='xx-small')
main_ax.set_xlabel(r'${}-{}$'.format(cb1, cb2), size='x-small')
main_ax.set_ylabel('Flux fraction outside IFU', size='x-small')
fig.suptitle('Flux fraction outside IFU', size='small')
fig.savefig(os.path.join(csp_basedir, 'lib_diags/', 'flux_outside_ifu.png'))
def get_emline_qty(maps, qty, key, sn_th=3., maskbits=list(range(64))):
'''
return a masked array for a particular emission-line
'''
qty = qty.upper()
qty_extname = 'EMLINE_{}'.format(qty)
qty_ivar_extname = 'EMLINE_{}_IVAR'.format(qty)
qty_mask_extname = 'EMLINE_{}_MASK'.format(qty)
qty_hdu = maps[qty_extname]
qty_ivar_hdu = maps[qty_ivar_extname]
qty_mask_hdu = maps[qty_mask_extname]
# get a mapping from eline key to channel key
v2k = {v: k for (k, v) in qty_hdu.header.items()}
# get a mapping from channel key to channel
cstring2ix = lambda s: int(s[1:]) - 1
ix = cstring2ix(v2k[key])
meas = qty_hdu.data[ix, ...]
ivar = qty_ivar_hdu.data[ix, ...]
snr = meas * np.sqrt(ivar)
snr_mask = (snr < sn_th)
map_mask = m.mask_from_maskbits(qty_mask_hdu.data[ix, ...])
mask = np.logical_or.reduce((snr_mask, map_mask))
return np.ma.array(meas, mask=mask)
def fit_dlogM_mw(tab, sfrsd_tab, mltype='ring', mlb='i'):
merge_tab = t.join(tab, sfrsd_tab, 'plateifu')
is_agn = m.mask_from_maskbits(merge_tab['mngtarg3'], [1, 2, 3, 4])
mlb_ix = totalmass.StellarMass.bands_ixs[mlb]
absmag_sun_mlb = totalmass.StellarMass.absmag_sun[mlb_ix]
logmass_in_ifu = merge_tab['mass_in_ifu'].to(u.dex(u.Msun))
logmass_in_ifu_lw = merge_tab['ml_fluxwt'] + merge_tab['ifu_absmag'][:, mlb_ix].to(
u.dex(m.bandpass_sol_l_unit), totalmass.bandpass_flux_to_solarunits(absmag_sun_mlb))
merge_tab['dlogmass_lw'] = logmass_in_ifu - logmass_in_ifu_lw
ha_corr = np.exp(merge_tab['mean_atten_mwtd'] * (6563 / 5500)**-1.3)
sfrsd = merge_tab['sigma_sfr'] * ha_corr * u.Msun / u.yr / u.pc**2
mass_pca = merge_tab['mass_in_ifu'] + merge_tab['outer_mass_{}'.format(mltype)]
ssfrsd = sfrsd / mass_pca
merge_tab['log_ssfrsd'] = ssfrsd.to(u.dex(ssfrsd.unit))
merge_tab['log_ssfrsd'][~np.isfinite(merge_tab['log_ssfrsd'])] = np.nan * merge_tab['log_ssfrsd'].unit
ols = OLS(
endog=np.array(merge_tab['dlogmass_lw'][~is_agn]),
exog=sm_add_constant(
t.Table(merge_tab['mean_atten_mwtd', 'std_atten_mwtd', 'log_ssfrsd'])[~is_agn].to_pandas(),
prepend=False),
hasconst=True, missing='drop')
olsfit = ols.fit()
return olsfit
def fit_dlogM_mw(tab, sfrsd_tab, mltype='ring', mlb='i'):
merge_tab = t.join(tab, sfrsd_tab, 'plateifu')
is_agn = m.mask_from_maskbits(merge_tab['mngtarg3'], [1, 2, 3, 4])
mlb_ix = totalmass.StellarMass.bands_ixs[mlb]
absmag_sun_mlb = totalmass.StellarMass.absmag_sun[mlb_ix]
logmass_in_ifu = merge_tab['mass_in_ifu'].to(u.dex(u.Msun))
logmass_in_ifu_lw = merge_tab['ml_fluxwt'] + merge_tab[f'logsollum_in_ifu_{mlb}']
merge_tab['dlogmass_lw'] = logmass_in_ifu - logmass_in_ifu_lw
std_atten_mwtd = merge_tab['std_atten_mwtd']
mean_atten_mwtd = merge_tab['mean_atten_mwtd']
ha_corr = np.exp(merge_tab['mean_atten_mwtd'] * (6563 / 5500)**-1.3)
sfrsd = merge_tab['sigma_sfr'] * ha_corr * u.Msun / u.yr / u.pc**2
outer_mass = (merge_tab[f'outerml_{mltype}'] + \
merge_tab[f'logsollum_outer_{mlb}']).to(u.Msun)
mass_pca = merge_tab['mass_in_ifu'].to(u.Msun) + outer_mass
ssfrsd = sfrsd / mass_pca
merge_tab['log_ssfrsd'] = ssfrsd.to(u.dex(ssfrsd.unit))
merge_tab['log_ssfrsd'][~np.isfinite(merge_tab['log_ssfrsd'])] = np.nan * merge_tab['log_ssfrsd'].unit
ols = OLS(
endog=np.array(merge_tab['dlogmass_lw'][~is_agn]),
exog=sm_add_constant(
t.Table(merge_tab['mean_atten_mwtd', 'std_atten_mwtd', 'log_ssfrsd'])[~is_agn].to_pandas(),
prepend=False),
hasconst=True, missing='drop')
olsfit = ols.fit()
return olsfit
def __init__(self, drpall):
sky_ifus = drpall[np.where(
m.mask_from_maskbits(drpall['mngtarg2'], [1]))[0]]
flux, ivar, lam = zip(
*[self.get_ifu_sky_spectra(row) for row in sky_ifus])
self.flux, self.ivar = np.row_stack(flux), np.row_stack(ivar)
self.lam = lam[0]
def get_ifu_sky_spectra(self, row):
drp = m.load_drp_logcube(row['plate'], row['ifudsgn'], mpl_v)
mask = m.mask_from_maskbits(drp['MASK'].data,
[3, 10]).astype(float).mean(axis=0) >= .1
sp_i, sp_j = np.where(~mask)
flux = drp['FLUX'].data[:, sp_i, sp_j].T
ivar = drp['IVAR'].data[:, sp_i, sp_j].T
lam = drp['WAVE'].data
drp.close()
return flux, ivar, lam
def get_ifu_sky_spectra(self, row):
drp = m.load_drp_logcube(row['plate'], row['ifudsgn'], mpl_v)
mask = m.mask_from_maskbits(
drp['MASK'].data, [3, 10]).astype(float).mean(axis=0) >= .1
sp_i, sp_j = np.where(~mask)
flux = drp['FLUX'].data[:, sp_i, sp_j].T
ivar = drp['IVAR'].data[:, sp_i, sp_j].T
lam = drp['WAVE'].data
drp.close()
return flux, ivar, lam
def compare_missing_mass(tab, mlb='i', cb1='g', cb2='r'):
mlb_ix = totalmass.StellarMass.bands_ixs[mlb]
cb1_ix = totalmass.StellarMass.bands_ixs[cb1]
cb2_ix = totalmass.StellarMass.bands_ixs[cb2]
absmag_sun_mlb = totalmass.StellarMass.absmag_sun[mlb_ix]
broadband_color = (tab['nsa_absmag'][:, cb1_ix] - tab['nsa_absmag'][:, cb2_ix])
outerlum_frac = tab['outer_lum'][:, mlb_ix].to(m.bandpass_sol_l_unit) / \
tab['nsa_absmag'][:, mlb_ix].to(m.bandpass_sol_l_unit,
totalmass.bandpass_flux_to_solarunits(absmag_sun_mlb))
dlogmass_cmlr_ring = ((tab['mass_in_ifu'] + tab['outer_mass_cmlr']).to(u.dex(u.Msun)) - \
(tab['mass_in_ifu'] + tab['outer_mass_ring']).to(u.dex(u.Msun)))
primarysample = m.mask_from_maskbits(tab['mngtarg1'], [10])
secondarysample = m.mask_from_maskbits(tab['mngtarg1'], [11])
fig, main_ax, hist_ax = make_panel_hist(top=0.9, left=.225)
valid = np.isfinite(dlogmass_cmlr_ring)
for selection, label, marker, color in zip(
[primarysample, secondarysample], ['Primary', 'Secondary'],
['o', 'D'], ['r', 'b']):
main_ax.scatter(
x=broadband_color[selection * valid], y=dlogmass_cmlr_ring[selection * valid],
c=color, edgecolor='None', s=5., marker=marker, label=label)
hist_ax.hist(dlogmass_cmlr_ring[selection * valid], color=color, density=True, bins='auto',
histtype='step', orientation='horizontal', linewidth=0.75)
main_ax.set_xlim(np.percentile(broadband_color[valid], [1., 99.]))
main_ax.set_ylim(np.percentile(dlogmass_cmlr_ring[valid], [1., 99.]))
main_ax.legend(loc='best', prop={'size': 'xx-small'})
main_ax.tick_params(labelsize='xx-small')
main_ax.set_xlabel(r'${}-{}$'.format(cb1, cb2), size='x-small')
main_ax.set_ylabel(r'$\log \frac{M^{\rm tot}_{\rm CMLR}}{M^{\rm tot}_{\rm ring}}$',
size='x-small')
fig.suptitle(r'Impact of aperture-correction on $M^{\rm tot}$', size='small')
fig.savefig(os.path.join(basedir, 'lib_diags/', 'mtot_compare_cmlr_ring.png'))
def compare_outerml_ring_cmlr(tab, mlb='i', cb1='g', cb2='r'):
mlb_ix = totalmass.StellarMass.bands_ixs[mlb]
cb1_ix = totalmass.StellarMass.bands_ixs[cb1]
cb2_ix = totalmass.StellarMass.bands_ixs[cb2]
absmag_sun_mlb = totalmass.StellarMass.absmag_sun[mlb_ix]
broadband_color = (tab['nsa_absmag'][:, cb1_ix] - tab['nsa_absmag'][:, cb2_ix])
ml_cmlr_ring = tab['outer_ml_cmlr'] - tab['outer_ml_ring']
ifu_lum = tab['ifu_absmag'][:, mlb_ix].to(
m.bandpass_sol_l_unit, totalmass.bandpass_flux_to_solarunits(absmag_sun_mlb))
outer_lum = tab['outer_lum'][:, mlb_ix].to(m.bandpass_sol_l_unit)
total_lum = tab['nsa_absmag'][:, mlb_ix].to(
m.bandpass_sol_l_unit, totalmass.bandpass_flux_to_solarunits(absmag_sun_mlb))
lum_frac_outer = outer_lum / total_lum
valid = np.isfinite(tab['outer_ml_cmlr'])
primarysample = m.mask_from_maskbits(tab['mngtarg1'], [10])
secondarysample = m.mask_from_maskbits(tab['mngtarg1'], [11])
fig, main_ax, hist_ax = make_panel_hist(top=0.875)
for selection, label, marker, color in zip(
[primarysample, secondarysample], ['Primary+', 'Secondary'],
['o', 'D'], ['r', 'b']):
main_ax.scatter(x=broadband_color[selection * valid], y=ml_cmlr_ring[selection * valid],
c=color, marker=marker, s=8. * lum_frac_outer[selection * valid],
edgecolor='None', label=label)
hist_ax.hist(ml_cmlr_ring[selection * valid], color=color, density=True, bins='auto',
histtype='step', orientation='horizontal', linewidth=0.75)
main_ax.legend(loc='best', prop={'size': 'xx-small'})
main_ax.tick_params(labelsize='xx-small')
main_ax.set_xlabel(r'${}-{}$'.format(cb1, cb2), size='x-small')
main_ax.set_ylabel(r'$\log{\frac{\Upsilon^*_{\rm CMLR}}{\Upsilon^*_{\rm ring}}}$',
size='x-small')
main_ax.set_ylim(np.percentile(ml_cmlr_ring[valid], [1., 99.]))
fig.suptitle(r'$\Upsilon^*_{\rm CMLR}$ vs $\Upsilon^*_{\rm ring}$', size='small')
fig.savefig(os.path.join(basedir, 'lib_diags/', 'outer_ml.png'))
def __init__(self,
drp_hdulist,
dap_hdulist,
drpall_row,
max_vel_unc=500. * u.Unit('km/s'),
drp_dlogl=None):
self.drp_hdulist = drp_hdulist
self.dap_hdulist = dap_hdulist
self.drpall_row = drpall_row
self.plateifu = self.drp_hdulist[0].header['PLATEIFU']
self.vel = dap_hdulist['STELLAR_VEL'].data * u.Unit('km/s')
self.vel_ivar = dap_hdulist['STELLAR_VEL_IVAR'].data * u.Unit('km-2s2')
self.z = drpall_row['nsa_z']
# mask all the spaxels that have high stellar velocity uncertainty
self.vel_ivar_mask = (1. / np.sqrt(self.vel_ivar)) > max_vel_unc
self.vel_mask = m.mask_from_maskbits(
self.dap_hdulist['STELLAR_VEL_MASK'].data, [30])
self.drp_l = drp_hdulist['WAVE'].data
self.drp_logl = np.log10(self.drp_l)
if drp_dlogl is None:
drp_dlogl = ut.determine_dlogl(self.drp_logl)
self.drp_dlogl = drp_dlogl
flux_hdu, ivar_hdu, l_hdu = (drp_hdulist['FLUX'], drp_hdulist['IVAR'],
drp_hdulist['WAVE'])
self.flux = flux_hdu.data
self.ivar = ivar_hdu.data
self.units = {'l': u.AA, 'flux': u.Unit('1e-17 erg s-1 cm-2 AA-1')}
self.S2P = Spec2Phot(lam=(self.drp_l * self.units['l']),
flam=(self.flux * self.units['flux']))
self.DONOTUSE = m.mask_from_maskbits(drp_hdulist['MASK'].data, [10])
self.ivar *= ~self.DONOTUSE
def __init__(self,
results,
pca_system,
drp,
dap,
drpall_row,
cosmo,
mlband='i'):
self.results = results
self.pca_system = pca_system
self.drp = drp
self.dap = dap
self.drpall_row = drpall_row
self.cosmo = cosmo
self.mlband = mlband
with catch_warnings():
simplefilter('ignore')
self.results.setup_photometry(pca_system)
self.s2p = results.spec2phot
# stellar mass to light ratio
self.ml0 = results.cubechannel('ML{}'.format(mlband), 0)
self.badpdf = results.cubechannel('GOODFRAC', 2) < 1.0e-4
self.ml_mask = np.logical_or.reduce((self.results.mask, self.badpdf))
# infer values in interior spaxels affected by one of the following:
# bad PDF, foreground star, dead fiber
self.low_or_no_cov = m.mask_from_maskbits(self.drp['MASK'].data,
[0, 1]).mean(axis=0) > .3
self.drp3dmask_interior = m.mask_from_maskbits(
self.drp['MASK'].data, [2, 3]).mean(axis=0) > .3
self.interior_mask = np.logical_or.reduce(
(self.badpdf, self.drp3dmask_interior))
self.logml_final = infer_masked(q_trn=self.ml0,
bad_trn=self.ml_mask,
infer_here=self.interior_mask) * u.dex(
m.m_to_l_unit)
def __init__(self, drp_hdulist, dap_hdulist, drpall_row,
max_vel_unc=500. * u.Unit('km/s'), drp_dlogl=None):
self.drp_hdulist = drp_hdulist
self.dap_hdulist = dap_hdulist
self.drpall_row = drpall_row
self.plateifu = self.drp_hdulist[0].header['PLATEIFU']
self.vel = dap_hdulist['STELLAR_VEL'].data * u.Unit('km/s')
self.vel_ivar = dap_hdulist['STELLAR_VEL_IVAR'].data * u.Unit(
'km-2s2')
self.z = drpall_row['nsa_z']
# mask all the spaxels that have high stellar velocity uncertainty
self.vel_ivar_mask = (1. / np.sqrt(self.vel_ivar)) > max_vel_unc
self.vel_mask = m.mask_from_maskbits(
self.dap_hdulist['STELLAR_VEL_MASK'].data, [30])
self.drp_l = drp_hdulist['WAVE'].data
self.drp_logl = np.log10(self.drp_l)
if drp_dlogl is None:
drp_dlogl = ut.determine_dlogl(self.drp_logl)
self.drp_dlogl = drp_dlogl
flux_hdu, ivar_hdu, l_hdu = (drp_hdulist['FLUX'], drp_hdulist['IVAR'],
drp_hdulist['WAVE'])
self.flux = flux_hdu.data
self.ivar = ivar_hdu.data
self.units = {'l': u.AA, 'flux': u.Unit('1e-17 erg s-1 cm-2 AA-1')}
self.S2P = Spec2Phot(lam=(self.drp_l * self.units['l']),
flam=(self.flux * self.units['flux']))
self.DONOTUSE = m.mask_from_maskbits(drp_hdulist['MASK'].data, [10])
self.ivar *= ~self.DONOTUSE
def get_qty(self, qty, key, sn_th, maskbits=[30]):
maps = self.hdulist
qty = qty.upper()
qty_extname = 'EMLINE_{}'.format(qty)
qty_ivar_extname = 'EMLINE_{}_IVAR'.format(qty)
qty_mask_extname = 'EMLINE_{}_MASK'.format(qty)
qty_hdu = maps[qty_extname]
qty_ivar_hdu = maps[qty_ivar_extname]
qty_mask_hdu = maps[qty_mask_extname]
# get a mapping from eline key to channel key
v2k = {v: k for (k, v) in qty_hdu.header.items()}
# get a mapping from channel key to channel
cstring2ix = lambda s: int(s[1:]) - 1
ix = cstring2ix(v2k[key])
meas = qty_hdu.data[ix, ...]
ivar = qty_ivar_hdu.data[ix, ...]
ivar_zero = (ivar == 0.)
std = 1. / np.sqrt(ivar.clip(min=1.0e-20))
std[ivar_zero] = 0.
snr_mask = ((meas * np.sqrt(ivar)) < sn_th)
map_mask = m.mask_from_maskbits(qty_mask_hdu.data[ix, ...])
mask = np.logical_or.reduce((ivar_zero, map_mask, snr_mask))
unit = self.units[qty]
data = nddata.NDDataRef(
meas, uncertainty=nddata.StdDevUncertainty(std, unit=unit),
unit=unit, mask=mask, wcs=wcs)
return data
def __init__(self, drpall):
sky_ifus = drpall[np.where(m.mask_from_maskbits(drpall['mngtarg2'], [1]))[0]]
flux, ivar, lam = zip(*[self.get_ifu_sky_spectra(row) for row in sky_ifus])
self.flux, self.ivar = np.row_stack(flux), np.row_stack(ivar)
self.lam = lam[0]
def __init__(self, lam_model, spec_model, meta_model,
row, drp_base, dap_base, plateifu_base, model_ix,
Kspec_obs=None, sky=None):
'''
create mocks of DRP LOGCUBE and DAP MAPS
1. characterize SNR of observed data
2. redshift model to observed-frame
3. attenuate according to MW reddening law
4. blur according to instrumental dispersion
5. resample onto rectified (observed) wavelength grid
6. add noise from full covariance prescription OR from SNR
7. scale according to r-band surface brightness
8. mask where there's no flux
'''
self.model_ix = model_ix
flux_obs = drp_base['FLUX'].data
ivar_obs = drp_base['IVAR'].data
lam_obs = drp_base['WAVE'].data
specres_obs = drp_base['SPECRES'].data
cubeshape = drp_base['FLUX'].data.shape
nl_obs, *mapshape = cubeshape
mapshape = tuple(mapshape)
'''STEP 1'''
# find SNR of each pixel in cube (used to scale noise later)
snrcube_obs, rmsmap_obs = compute_snrcube(
flux=flux_obs, ivar=ivar_obs,
filtersize_l=15, return_rms_map=True)
'''STEP 2'''
# compute the redshift map
z_cosm = row['nsa_zdist']
z_pec = (dap_base['STELLAR_VEL'].data * u.Unit('km/s') / c.c).to('').value
z_obs = (1. + z_cosm) * (1. + z_pec) - 1.
# create a placeholder model cube since flexible broadcasting is hard
if specmodel.ndim == 3:
spec_model_cube = specmodel
else:
spec_model_cube = np.tile(spec_model[:, None, None], (1, ) + mapshape)
ivar_model_cube = np.ones_like(spec_model_cube)
lam_model_z, spec_model_z, ivar_model_z = ut.redshift(
l=lam_model, f=spec_model_cube, ivar=ivar_model_cube,
z_in=0., z_out=z_obs)
'''STEP 3'''
# figure out attenuation
# there are issues with extinction library's handling of multidim arrays
# so we'll interpolate
atten_l = np.linspace(3000., 20000., 10000)
r_v = 3.1
ext_mag_interp = interp1d(
x=atten_l,
y=extinction.fitzpatrick99(
atten_l, r_v=r_v, a_v=drp_base[0].header['EBVGAL'] * r_v),
fill_value='extrapolate', bounds_error=False)
ext_mag = ext_mag_interp(lam_model_z)
atten_factor = 2.5**-ext_mag
spec_model_mwred = spec_model_z * atten_factor
ivar_model_mwred = ivar_model_z / atten_factor**2.
'''STEP 4'''
# specres of observed cube at model wavelengths
spec_model_instblur = ut.blur_cube_to_psf(
l_ref=drp_base['WAVE'].data, specres_ref=drp_base['SPECRES'].data,
l_eval=lam_model_z, spec_unblurred=spec_model_mwred)
ivar_model_instblur = ivar_model_mwred
'''STEP 5'''
# create placeholder arrays for ivar and flux
final_fluxcube = np.empty(cubeshape)
# wavelength grid for final cube
l_grid = drp_base['WAVE'].data
# populate flam and ivar pixel-by-pixel
for ind in np.ndindex(mapshape):
final_fluxcube[:, ind[0], ind[1]] = np.interp(
xp=lam_model_z[:, ind[0], ind[1]],
fp=spec_model_instblur[:, ind[0], ind[1]],
x=l_grid)
# normalize each spectrum to mean 1
final_fluxcube /= np.mean(final_fluxcube, axis=0, keepdims=True)
'''STEP 6'''
# spectrophotometric noise
cov_noise = noisify_cov(Kspec_obs, mapshape=mapshape)
# random noise: signal * (gauss / snr)
random_noise = np.random.randn(*cubeshape) / (snrcube_obs + 1.0e-6)
fluxscaled_random_noise = random_noise * final_fluxcube
final_fluxcube += (cov_noise + fluxscaled_random_noise)
'''STEP 7'''
# normalize everything to have the same observed-frame r-band flux
u_flam = 1.0e-17 * (u.erg / (u.s * u.cm**2 * u.AA))
rband_drp = Spec2Phot(
lam=drp_base['WAVE'].data,
flam=drp_base['FLUX'].data * u_flam).ABmags['sdss2010-r'] * u.ABmag
rband_drp[~np.isfinite(rband_drp)] = rband_drp[np.isfinite(rband_drp)].max()
rband_model = Spec2Phot(
lam=drp_base['WAVE'].data,
flam=final_fluxcube * u_flam).ABmags['sdss2010-r'] * u.ABmag
rband_model[~np.isfinite(rband_model)] = rband_model[np.isfinite(rband_model)].max()
# flux ratio map
r = (rband_drp.to(m.Mgy) / rband_model.to(m.Mgy)).value
final_fluxcube *= r[None, ...]
# initialize the ivar cube according to the SNR cube
# of base observations
# this is because while we think we know the actual spectral covariance,
# that is not necessarily reflected in the quoted ivars!!!
final_ivarcube = (snrcube_obs / final_fluxcube)**2.
# add sky spectrum
if sky:
skyfluxs, skyivars = sky.make_skycube(mapshape)
final_fluxcube = final_fluxcube + skyfluxs
#final_ivarcube = 1. / (1. / final_ivarcube + 1. / skyivars)
'''STEP 8'''
# mask where the native datacube has no signal
rimg = drp_base['RIMG'].data
nosignal = (rimg == 0.)[None, ...]
nosignal_cube = np.broadcast_to(nosignal, final_fluxcube.shape)
final_fluxcube[nosignal_cube] = 0.
final_ivarcube[final_fluxcube == 0.] = 0.
# mask where there's bad velocity info
badvel = m.mask_from_maskbits(
dap_base['STELLAR_VEL_MASK'].data, [30])[None, ...]
final_ivarcube[np.tile(badvel, (nl_obs, 1, 1))] = 0.
# replace infinite flux elements with median-filtered
flux_is_inf = ~np.isfinite(final_fluxcube)
final_fluxcube[flux_is_inf] = medfilt(
np.nan_to_num(final_fluxcube), [11, 1, 1])[flux_is_inf]
self.dap_base = dap_base
self.drp_base = drp_base
self.fluxcube = final_fluxcube
self.fluxcube_ivar = final_ivarcube
self.row = row
self.metadata = meta_model
self.plateifu_base = plateifu_base
def __init__(self,
lam_model,
spec_model,
meta_model,
row,
drp_base,
dap_base,
plateifu_base,
model_ix,
Kspec_obs=None,
sky=None):
'''
create mocks of DRP LOGCUBE and DAP MAPS
1. characterize SNR of observed data
2. redshift model to observed-frame
3. attenuate according to MW reddening law
4. blur according to instrumental dispersion
5. resample onto rectified (observed) wavelength grid
6. add noise from full covariance prescription OR from SNR
7. scale according to r-band surface brightness
8. mask where there's no flux
'''
self.model_ix = model_ix
flux_obs = drp_base['FLUX'].data
ivar_obs = drp_base['IVAR'].data
lam_obs = drp_base['WAVE'].data
specres_obs = drp_base['SPECRES'].data
cubeshape = drp_base['FLUX'].data.shape
nl_obs, *mapshape = cubeshape
mapshape = tuple(mapshape)
'''STEP 1'''
# find SNR of each pixel in cube (used to scale noise later)
snrcube_obs, rmsmap_obs = compute_snrcube(flux=flux_obs,
ivar=ivar_obs,
filtersize_l=15,
return_rms_map=True)
'''STEP 2'''
# compute the redshift map
z_cosm = row['nsa_zdist']
z_pec = (dap_base['STELLAR_VEL'].data * u.Unit('km/s') /
c.c).to('').value
z_obs = (1. + z_cosm) * (1. + z_pec) - 1.
# create a placeholder model cube since flexible broadcasting is hard
if specmodel.ndim == 3:
spec_model_cube = specmodel
else:
spec_model_cube = np.tile(spec_model[:, None, None],
(1, ) + mapshape)
ivar_model_cube = np.ones_like(spec_model_cube)
lam_model_z, spec_model_z, ivar_model_z = ut.redshift(
l=lam_model,
f=spec_model_cube,
ivar=ivar_model_cube,
z_in=0.,
z_out=z_obs)
'''STEP 3'''
# figure out attenuation
# there are issues with extinction library's handling of multidim arrays
# so we'll interpolate
atten_l = np.linspace(3000., 20000., 10000)
r_v = 3.1
ext_mag_interp = interp1d(x=atten_l,
y=extinction.fitzpatrick99(
atten_l,
r_v=r_v,
a_v=drp_base[0].header['EBVGAL'] * r_v),
fill_value='extrapolate',
bounds_error=False)
ext_mag = ext_mag_interp(lam_model_z)
atten_factor = 2.5**-ext_mag
spec_model_mwred = spec_model_z * atten_factor
ivar_model_mwred = ivar_model_z / atten_factor**2.
'''STEP 4'''
# specres of observed cube at model wavelengths
spec_model_instblur = ut.blur_cube_to_psf(
l_ref=drp_base['WAVE'].data,
specres_ref=drp_base['SPECRES'].data,
l_eval=lam_model_z,
spec_unblurred=spec_model_mwred)
ivar_model_instblur = ivar_model_mwred
'''STEP 5'''
# create placeholder arrays for ivar and flux
final_fluxcube = np.empty(cubeshape)
# wavelength grid for final cube
l_grid = drp_base['WAVE'].data
# populate flam and ivar pixel-by-pixel
for ind in np.ndindex(mapshape):
final_fluxcube[:, ind[0],
ind[1]] = np.interp(xp=lam_model_z[:, ind[0],
ind[1]],
fp=spec_model_instblur[:,
ind[0],
ind[1]],
x=l_grid)
# normalize each spectrum to mean 1
final_fluxcube /= np.mean(final_fluxcube, axis=0, keepdims=True)
'''STEP 6'''
# spectrophotometric noise
cov_noise = noisify_cov(Kspec_obs, mapshape=mapshape)
# random noise: signal * (gauss / snr)
random_noise = np.random.randn(*cubeshape) / (snrcube_obs + 1.0e-6)
fluxscaled_random_noise = random_noise * final_fluxcube
final_fluxcube += (cov_noise + fluxscaled_random_noise)
'''STEP 7'''
# normalize everything to have the same observed-frame r-band flux
u_flam = 1.0e-17 * (u.erg / (u.s * u.cm**2 * u.AA))
rband_drp = Spec2Phot(
lam=drp_base['WAVE'].data,
flam=drp_base['FLUX'].data * u_flam).ABmags['sdss2010-r'] * u.ABmag
rband_drp[~np.isfinite(rband_drp)] = rband_drp[np.isfinite(
rband_drp)].max()
rband_model = Spec2Phot(
lam=drp_base['WAVE'].data,
flam=final_fluxcube * u_flam).ABmags['sdss2010-r'] * u.ABmag
rband_model[~np.isfinite(rband_model)] = rband_model[np.isfinite(
rband_model)].max()
# flux ratio map
r = (rband_drp.to(m.Mgy) / rband_model.to(m.Mgy)).value
final_fluxcube *= r[None, ...]
# initialize the ivar cube according to the SNR cube
# of base observations
# this is because while we think we know the actual spectral covariance,
# that is not necessarily reflected in the quoted ivars!!!
final_ivarcube = (snrcube_obs / final_fluxcube)**2.
# add sky spectrum
if sky:
skyfluxs, skyivars = sky.make_skycube(mapshape)
final_fluxcube = final_fluxcube + skyfluxs
#final_ivarcube = 1. / (1. / final_ivarcube + 1. / skyivars)
'''STEP 8'''
# mask where the native datacube has no signal
rimg = drp_base['RIMG'].data
nosignal = (rimg == 0.)[None, ...]
nosignal_cube = np.broadcast_to(nosignal, final_fluxcube.shape)
final_fluxcube[nosignal_cube] = 0.
final_ivarcube[final_fluxcube == 0.] = 0.
# mask where there's bad velocity info
badvel = m.mask_from_maskbits(dap_base['STELLAR_VEL_MASK'].data,
[30])[None, ...]
final_ivarcube[np.tile(badvel, (nl_obs, 1, 1))] = 0.
# replace infinite flux elements with median-filtered
flux_is_inf = ~np.isfinite(final_fluxcube)
final_fluxcube[flux_is_inf] = medfilt(np.nan_to_num(final_fluxcube),
[11, 1, 1])[flux_is_inf]
self.dap_base = dap_base
self.drp_base = drp_base
self.fluxcube = final_fluxcube
self.fluxcube_ivar = final_ivarcube
self.row = row
self.metadata = meta_model
self.plateifu_base = plateifu_base
def compare_outerml_ring_cmlr(tab, mlb='i', cb1='g', cb2='r'):
'''figure with comparison between aperture correction two M/L
Parameters
----------
tab : astropy.table.Table
full aggregation results table
mlb : {str}, optional
bandpass for mass-to-light ratio
(the default is 'i', which [default_description])
cb1 : {str}, optional
bluer bandpass for color (the default is 'g', SDSS g band)
cb2 : {str}, optional
redder bandpass for color (the default is 'r', SDSS r band)
'''
cb1_nsa_mag = tab[f'mag_nsa_{cb1}']
cb2_nsa_mag = tab[f'mag_nsa_{cb2}']
broadband_color = cb1_nsa_mag - cb2_nsa_mag
ml_cmlr_ring = tab['outerml_diff']
lum_frac_outer = tab[f'flux_outer_{mlb}'] / tab[f'flux_nsa_{mlb}']
valid = np.isfinite(tab['outerml_cmlr'])
primarysample = m.mask_from_maskbits(tab['mngtarg1'], [10])
secondarysample = m.mask_from_maskbits(tab['mngtarg1'], [11])
fig, main_ax, hist_ax = make_panel_hist(top=0.875)
for selection, label, marker, color in zip(
[primarysample, secondarysample], ['Primary+', 'Secondary'],
['o', 'D'], ['r', 'b']):
main_ax.scatter(x=broadband_color[selection * valid], y=ml_cmlr_ring[selection * valid],
c=color, marker=marker, s=10. * lum_frac_outer[selection * valid],
edgecolor='None', label=label, alpha=0.5)
hist_ax.hist(ml_cmlr_ring[selection * valid], color=color, density=True, bins='auto',
histtype='step', orientation='horizontal', linewidth=0.75)
main_ax.legend(loc='lower right', prop={'size': 'xx-small'})
# make point size legend
legend_lumfracs = np.array([.05, .1, .25, .5])
sc_pri = [main_ax.scatter(
[], [], c='r', marker='o', s=10. * frac, edgecolor='None', alpha=0.5)
for frac in legend_lumfracs]
sc_sec = [main_ax.scatter(
[], [], c='b', marker='D', s=10. * frac, edgecolor='None', alpha=0.5)
for frac in legend_lumfracs]
merged_markers = list(zip(sc_pri, sc_sec))
merged_labels = [r'{:.0f}\%'.format(frac * 100.) for frac in legend_lumfracs]
hmap = {tuple: HandlerTuple(ndivide=None)}
lumfrac_legend = hist_ax.legend(
list(zip(sc_pri, sc_sec)), merged_labels,
handler_map=hmap, loc='upper right', prop={'size': 'xx-small'},
title='\% flux outside IFU', title_fontsize='xx-small')
main_ax.tick_params(labelsize='xx-small')
main_ax.set_xlabel(r'${}-{}$'.format(cb1, cb2), size='x-small')
main_ax.set_ylabel(r'$\log{\frac{\Upsilon^*_{\rm CMLR}}{\Upsilon^*_{\rm ring}}}$',
size='x-small')
main_ax.set_xlim([0.1, 0.9])
main_ax.set_ylim([-0.5, 0.5])
fig.suptitle(r'$\Upsilon^*_{\rm CMLR}$ vs $\Upsilon^*_{\rm ring}$', size='small')
fig.savefig(os.path.join(csp_basedir, 'lib_diags/', 'outer_ml.png'))
def overplot_spectroscopic(res,
drp_logcube,
drpall_row,
mass_ax,
ml_ax,
f_ax,
pca_system,
res_wcs,
cmlr,
mlb='i'):
'''
overplot cumulative spectroscopic masses, log M/L, cumulative fluxes
'''
# synthetic photometry of IFU data in rest frame
ebvgal = drpall_row['ebvgal']
r_v = 3.1
dustcorr = 10.**(0.4 * extinction.fitzpatrick99(
drp_logcube['WAVE'].data, a_v=r_v * ebvgal, r_v=r_v))[:, None, None]
ifu_s2p = spectrophot.Spec2Phot(
lam=drp_logcube['WAVE'].data / (1. + drpall_row['nsa_z']),
flam=1.0e-17 * drp_logcube['FLUX'].data * (1. + drpall_row['nsa_z']) *
dustcorr)
res_flux = (ifu_s2p.ABmags['sdss2010-{}'.format(mlb)] * u.ABmag).to(m.Mgy)
# map sky coordinates to number of effective radii from center
pos_to_nRe = RotatedParaboloid(ctr=np.array(
[drpall_row['objra'], drpall_row['objdec']]),
phi=drpall_row['nsa_elpetro_phi'] * u.deg,
axis_ratio=drpall_row['nsa_elpetro_ba'],
Re=drpall_row['nsa_elpetro_th50_r'] / 3600.)
badpdf = res.badPDF()
ml_mask = np.logical_or(res.mask, badpdf)
# WCS from results file determines where elliptical projection is sampled
#res_wcs = wcs.WCS(res['SNRMED'].header)
res_II, res_JJ = np.meshgrid(
*[np.linspace(0., s - 1., s) for s in res['SNRMED'].shape],
indexing='ij')
res_AA, res_DD = res_wcs.wcs_pix2world(res_JJ, res_II, 1)
# sample Re at spaxel centers
res_Re = pos_to_nRe(np.column_stack([res_AA.flatten(),
res_DD.flatten()
])).reshape(res_AA.shape)
nRe_to_plot = np.linspace(0., res_Re[~ml_mask].max(), 50.)
# mass, log M/L
badpdf = res.badPDF()
ml_mask = np.logical_or.reduce((res.mask, badpdf))
interior_mask = np.logical_or.reduce(
(badpdf,
(m.mask_from_maskbits(drp_logcube['MASK'].data, [3]).sum(axis=0) > 0),
(m.mask_from_maskbits(drp_logcube['MASK'].data, [2]).sum(axis=0) >
0)))
logml = infer_masked(res.param_dist_med(extname='ML{}'.format(mlb)),
ml_mask, interior_mask)
ml = 10.**logml * m.m_to_l_unit
res_MAG = res_flux.to(u.ABmag) - cosmo.distmod(drpall_row['nsa_zdist'])
mlb_sollum = 10.**(-0.4 * (res_MAG - spectrophot.absmag_sun_band[mlb] * u.ABmag)).value * \
m.bandpass_sol_l_unit
spectro_mass = (ml * mlb_sollum).to(u.Msun)
# sum mass within some number of Re
mass_within_nRe = np.array(
[sum_within_nRe(spectro_mass, res_Re, n) for n in nRe_to_plot])
mass_ax.plot(nRe_to_plot, mass_within_nRe, c='C1')
# plot mass to light versus Re
ml_sc = ml_ax.scatter(res_Re[~ml_mask],
logml[~ml_mask],
c='C1',
edgecolor='None',
s=.5)
res_Re_m = np.ma.array(res_Re, mask=ml_mask)
# find ring method log M/L
outer_ring = np.logical_and((res_Re_m <= res_Re_m.max()),
(res_Re_m >= res_Re_m.max() - .5))
outer_logml_ring = np.median(logml[~ml_mask * outer_ring])
ml_ax.scatter(x=[np.median(res_Re_m[~ml_mask * outer_ring])],
y=[outer_logml_ring],
marker='x',
c='C1')
# find CMLR log M/L
nsa_MAG = (np.array(drpall_row['nsa_elpetro_absmag'][2:]) * \
(u.ABmag - u.MagUnit(u.littleh**2))).to(u.ABmag, u.with_H0(cosmo.H0))
nsa_mag = (nsa_MAG + cosmo.distmod(drpall_row['nsa_zdist']))
nsa_flux = nsa_mag.to(m.Mgy)
ifu_mag = np.array(
[ifu_s2p.ABmags['sdss2010-{}'.format(b_)] for b_ in 'ugriz']) * u.ABmag
ifu_mag[~np.isfinite(ifu_mag)] = 40. * u.ABmag
ifu_flux = ifu_mag.to(m.Mgy)
flux_deficit = nsa_flux - ifu_flux.sum(axis=(1, 2))
logml_missingflux_cmlr = cmlr(2.5 *
np.log10(flux_deficit[1] / flux_deficit[2]))
ml_ax.scatter(x=[np.median(res_Re_m[~ml_mask * outer_ring])],
y=[logml_missingflux_cmlr],
marker='s',
c='C1',
edgecolor='k')
#
ml_missingflux_cmlr = 10.**logml_missingflux_cmlr * m.m_to_l_unit
ml_missingflux_ring = 10.**outer_logml_ring * m.m_to_l_unit
missing_mlb_MAG = flux_deficit[3].to(u.ABmag) - cosmo.distmod(
drpall_row['nsa_zdist'])
missing_mlb_sollum = 10.**(-0.4 * (missing_mlb_MAG - spectrophot.absmag_sun_band[mlb] * u.ABmag)).value * \
m.bandpass_sol_l_unit
missing_mass_cmlr = (ml_missingflux_cmlr * missing_mlb_sollum).to(u.Msun)
missing_mass_ring = (ml_missingflux_ring * missing_mlb_sollum).to(u.Msun)
mass_ax.scatter(x=res_Re.max() - .1,
y=mass_within_nRe[-1] * u.Msun + missing_mass_cmlr,
marker='s',
c='C1',
edgecolor='k',
zorder=3,
label='+AC(CMLR)')
mass_ax.scatter(x=res_Re.max(),
y=mass_within_nRe[-1] * u.Msun + missing_mass_ring,
marker='x',
c='C1',
edgecolor='k',
zorder=3,
label='+AC(RING)')
mass_ax.legend(loc='best', prop={'size': 'x-small'})
summed_flux = np.array([
sum_within_nRe(arr=res_flux, Re_a=res_Re, nRe=n) for n in nRe_to_plot
])
f_ax.plot(nRe_to_plot, summed_flux, label='IFU', c='C1')
f_ax.scatter(x=res_Re.max() + .1,
y=summed_flux[-1] * m.Mgy + flux_deficit[3],
marker='o',
c='C1',
edgecolor='k',
zorder=3)
ydata=mean_smsd_in_bin[usebin],
sigma=std_smsd_in_bin[usebin],
absolute_sigma=True,
p0=[2.])
return popt, pcov
if __name__ == '__main__':
import read_results
import os
pca_system = read_results.PCASystem.fromfile(
os.path.join(basedir, 'pc_vecs.fits'))
drpall = m.load_drpall(mpl_v, index='plateifu')
all_dms_galaxies = drpall[m.mask_from_maskbits(drpall['mngtarg3'], [16])]
h_dms = 0.7
fig, (ax1, ax2, ax3, ax4, ax5, _) = plt.subplots(nrows=2,
ncols=3,
figsize=(7, 8),
sharex=True,
sharey=True)
for plateifu, dms_name, ax, Rbulge, F, incl in zip(
[
'8566-12705', '8567-12701', '8939-12704', '10494-12705',
'10510-12704'
], ['UGC3997', 'UGC4107', 'UGC4368', 'UGC4380', 'UGC6918'],
[ax1, ax2, ax3, ax4, ax5], [1.97, 1.18, 2.23, 2.44, 0.655],
[.48, .56, .72, .46, .64],
np.array([26.2, 24.1, 45.3, 14.2, 38.0]) * u.deg):
