def filtertext(phot):
filters = list(set([bmccom.band_name_nodigits_regex(b) for b in phot]))
fw = [dld._eff_wavelength[fil] for fil in filters]
fzip = sorted(list(zip(fw, filters)), key=lambda x: x[0])
txt = "filters present\n(shortest to longest wavelength):\n"
for fz in fzip:
txt += f"{fz[1]} ({fz[0]:.2f} $\mu$m)\n"
return txt
def _etglikelihood(jab, zin, zf, Jfilter, photom):
predJ = 3631. * (10**(-0.4 * jab))
lk = 1.
for band in photom:
if photom[band]['f'] is not None:
colour = _model[zf][f'X-J_{Jfilter}'][
bmccom.band_name_nodigits_regex(band)][f"{zin:.2f}"]
predb = predJ * 10**(-0.4 * colour)
lk *= bmccom.liketerm(predb, photom[band]['f'], photom[band]['e'])
return lk
def _mltlikelihood(fj, t, Jfilter, photom):
lk = 1.
for band in photom:
if photom[band]['f'] is not None:
colour = _model[t][f'X-J_{Jfilter}'][
bmccom.band_name_nodigits_regex(band)]
predb = fj * 10**(
-0.4 * colour
) if colour < 10 else 0. #deals with blue filters which have been set to colour = 100 above
lk *= bmccom.liketerm(predb, photom[band]['f'],
photom[band]['e_mlt'])
return lk
def _qsolikelihood(M, zin, qm, Jfilter, photom):
#always start with a model J band flux based on the value of M passed in
predJ = _Jflx_from_M1450(M, zin, qm, Jfilter)
lk = 1.
for band in photom:
if photom[band]['f'] is not None:
colour = _model[qm][f'X-J_{Jfilter}'][
bmccom.band_name_nodigits_regex(band)][f"{zin:.2f}"]
#the code will have already checked the redshift limits
#so very blue H/K/W-J colours (z>10) are not an issue (where template fJ = 0).
#we now deal with very red X-J colours in bluer X bands,
#where template flux can be zero.
predb = predJ * 10**(-0.4 * colour) if colour < 8 else 0.
lk *= bmccom.liketerm(predb, photom[band]['f'], photom[band]['e'])
return lk
def best_fit_sed_plot(d, phot, JF):
bfq = hzq.best_fit_SED(phot, JF)
qw, qf = zip(*make_plottable_sed(bfq))
bfg = etg.best_fit_SED(phot, JF)
gw, gf = zip(*make_plottable_sed(bfg))
bfs = mlt.best_fit_SED(phot, JF)
sw, sf = zip(*make_plottable_sed(bfs))
#make plot
fig = plt.figure(figsize=(8, 5), dpi=100)
ax1 = fig.add_subplot(111)
#xmin = round(min(bfq['wavelengths'])-0.2,2) ; xmax = round(max(bfq['wavelengths'])+0.25,2)
#ymin = round((1.E6*min(bfq['f']))-0.2,1); ymax = round((1.E6*max(bfq['f']))+0.5,1)
#ax1.set_xlim(xmin,xmax);
ax1.set_xlabel(r'wavelength ($\mu$m)', fontsize=11)
ax1.set_ylabel(r'flux ($\mu$Jy)', fontsize=11)
ax1.plot(qw, qf, 'k-', lw=2, label='quasar')
ax1.plot(gw, gf, 'r--', lw=2, label='galaxy')
ax1.plot(sw, sf, 'y:', lw=2, label='MLT') #plot templates
#plot the source
sourcew, sourcef, sourcee = [], [], []
for i, e in enumerate(bfq['e']):
if type(e) != str:
sourcew.append(bfq['wavelengths'][i])
sourcef.append(bfq['f'][i] * 1.E6)
sourcee.append(e * 1.E6)
ax1.errorbar(sourcew,
sourcef,
sourcee,
linestyle="None",
color='b',
capsize=4,
marker='o',
ms=4,
label='photometry')
#check the photometry dictionary for any limits bands - these won't have been included in the chisq fitting
xmin, xmax, xmajloc, xminloc, ymajloc, yminloc = plot_params(ax1)
for b in phot:
b_nodigits = bmccom.band_name_nodigits_regex(b)
w = dld._eff_wavelength[b_nodigits]
#ax1.text(w,-ymajloc*0.5,f"{b_nodigits}",fontsize=7)
if type(phot[b]['e']) == str:
#b_nodigits = bmccom.remove_digits(b);
w = dld._eff_wavelength[b_nodigits]
ax1.errorbar(w,
phot[b]['f'],
xerr=0.75 * xminloc,
yerr=yminloc,
linestyle="None",
color='b',
uplims=1)
ax1.tick_params(axis='both', which='major', labelsize=10)
ax1.xaxis.set_major_locator(tck.MultipleLocator(xmajloc))
ax1.xaxis.set_minor_locator(tck.MultipleLocator(xminloc))
ax1.yaxis.set_major_locator(tck.MultipleLocator(ymajloc))
ax1.yaxis.set_minor_locator(tck.MultipleLocator(yminloc))
ax1.set_ylim(bottom=-ymajloc)
ax1.set_xlim(right=xmax)
ax1.legend(frameon=0, fontsize=9)
flttxt = filtertext(phot)
ax1.text(0.66 * xmax, yminloc - ymajloc, flttxt, fontsize=8)
plottxt = plottext(d, phot, bfq, bfg, bfs, JF)
ax1.text(xmax + xminloc, ymajloc, plottxt, fontsize=6)
plot_params(ax1)
plt.subplots_adjust(left=0.1, right=0.75)
return fig