def fitMAPs(type, savefilename):
setup_selection_function()
if os.path.exists(savefilename):
with open(savefilename, 'rb') as savefile:
bf = pickle.load(savefile)
samples = pickle.load(savefile)
bf_g15 = pickle.load(savefile)
samples_g15 = pickle.load(savefile)
bf_zero = pickle.load(savefile)
samples_zero = pickle.load(savefile)
bii = pickle.load(savefile)
else:
bf = []
samples = []
bf_g15 = []
samples_g15 = []
bf_zero = []
samples_zero = []
bii = 0
maps = define_rcsample.MAPs()
for ii, map in enumerate(maps.map()):
if ii < bii: continue
tbf, ts = fitmap(map, type=type, dmap='marshall06')
bf.append(tbf)
samples.append(ts)
tbf, ts = fitmap(map, type=type, dmap='green15')
bf_g15.append(tbf)
samples_g15.append(ts)
tbf, ts = fitmap(map, type=type, dmap='zero')
bf_zero.append(tbf)
samples_zero.append(ts)
print ii, numpy.median(samples[-1], axis=1)
save_pickles(savefilename, bf, samples, bf_g15, samples_g15, bf_zero,
samples_zero, ii + 1)
return None
def plot_maprmax(savefilename, plotname):
with open(savefilename, 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples = numpy.array(pickle.load(savefile))
bf_g15 = numpy.array(pickle.load(savefile))
samples_g15 = numpy.array(pickle.load(savefile))
bf_zero = numpy.array(pickle.load(savefile))
samples_zero = numpy.array(pickle.load(savefile))
maps = define_rcsample.MAPs()
plotthis = numpy.zeros(len(bf)) + numpy.nan
for ii, map in enumerate(maps.map()):
tmed = numpy.median(
numpy.exp(samples[ii, 3])[True -
numpy.isnan(numpy.exp(samples[ii, 3]))])
if tmed < 5.:
tmed = 0.
plotthis[ii] = tmed
bovy_plot.bovy_print()
maps.plot(plotthis,
vmin=5.,
vmax=13.,
minnstar=15,
zlabel=r'$R_{\mathrm{peak}}\,(\mathrm{kpc})$',
shrink=0.68,
cmap='coolwarm_r')
# Sequences
haloc = define_rcsample.highalphalocus()
bovy_plot.bovy_plot(haloc[:, 0],
haloc[:, 1],
'-',
color='0.75',
lw=2.5,
overplot=True)
haloc = define_rcsample.lowalphalocus()
haloc = haloc[(haloc[:, 0] > -0.55) * (haloc[:, 0] < 0.225)]
bovy_plot.bovy_plot(haloc[:, 0],
haloc[:, 1],
'-',
color='0.75',
lw=2.5,
overplot=True)
# Label
#t= pyplot.text(-0.51,0.235,r'$\mathrm{single}$',
# size=16.,color='w')
#t.set_bbox(dict(alpha=0.5,color=cm.coolwarm(0.),
# edgecolor='none'))
#t= pyplot.text(-0.475,0.195,r'$\mathrm{exponential}$',
# size=16.,color='w')
t = pyplot.text(-0.625,
0.195,
r'$R_{\mathrm{peak}} < 5\,\mathrm{kpc}$',
size=16.,
color='w')
t.set_bbox(dict(alpha=0.5, color=cm.coolwarm_r(0.), edgecolor='none'))
pyplot.tight_layout()
bovy_plot.bovy_end_print(plotname, dpi=300)
return None
def plot_maphz(plotname):
# Load the two fit
with open('../mapfits/tribrokentwoexp.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples = numpy.array(pickle.load(savefile))
maps = define_rcsample.MAPs()
plotthisz1 = numpy.zeros(len(bf)) + numpy.nan
plotthisz1e = numpy.zeros(len(bf)) + numpy.nan
plotthisz2 = numpy.zeros(len(bf)) + numpy.nan
plotthisz2e = numpy.zeros(len(bf)) + numpy.nan
for ii, map in enumerate(maps.map()):
hzindx= (True-numpy.isnan(samples[ii,4]))\
*(True-numpy.isnan(samples[ii,5]))\
*(densprofiles.ilogit(samples[ii,4]) > 0.15)
tmed = numpy.median(1. / samples[ii, 1, hzindx])
terr = numpy.std(1. / samples[ii, 1, hzindx])
plotthisz1[ii] = tmed
plotthisz1e[ii] = terr
tmed = numpy.median(1. / samples[ii, 5, hzindx])
terr = numpy.std(1. / samples[ii, 5, hzindx])
plotthisz2[ii] = tmed
plotthisz2e[ii] = terr
plotthisz1[plotthisz1e / plotthisz1 > 0.5] = numpy.nan
bovy_plot.bovy_print()
bovy_plot.bovy_plot(
plotthisz2 * 1000.,
plotthisz1 * 1000.,
'ko',
xrange=[0., 1200.],
yrange=[0., 1200.],
xlabel=r'$2^\mathrm{nd}\ \mathrm{scale\ height\,(pc)}$',
ylabel=r'$1^\mathrm{st}\ \mathrm{scale\ height\,(pc)}$',
zorder=2)
bovy_plot.bovy_plot([0, 1200], [0, 1200], 'k--', overplot=True, lw=2.)
pyplot.errorbar(plotthisz2 * 1000.,
plotthisz1 * 1000.,
yerr=plotthisz1e * 1000.,
marker='o',
color='k',
ls='none',
zorder=1)
bovy_plot.bovy_end_print(plotname)
return None
def triangleMAPs(savefilename, basename):
with open(savefilename, 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples = numpy.array(pickle.load(savefile))
bf_g15 = numpy.array(pickle.load(savefile))
samples_g15 = numpy.array(pickle.load(savefile))
bf_zero = numpy.array(pickle.load(savefile))
samples_zero = numpy.array(pickle.load(savefile))
labels = []
for jj in range(samples.shape[2]):
labels.append(r"$\mathrm{param}\ %i$" % jj)
maps = define_rcsample.MAPs()
for ii, map in enumerate(maps.map()):
if ii >= len(bf): break
tfeh = numpy.nanmedian(map['FE_H'])
tafe = numpy.nanmedian(map[define_rcsample._AFETAG])
for tbf, tsamples, ext in zip([bf, bf_g15, bf_zero],
[samples, samples_g15, samples_zero],
['fid', 'g15', 'zero']):
try:
triangle.corner(tsamples[ii, ].T,
quantiles=[0.16, 0.5, 0.84],
labels=labels,
show_titles=True,
title_args={"fontsize": 12},
bins=21)
except ValueError:
pass
else:
bovy_plot.bovy_text(r'$[\mathrm{{Fe/H}}] = {feh:.1f},$'\
.format(feh=tfeh)+'\n'
+r'$[\alpha/\mathrm{{Fe}}] = {afe:.2f}$'\
.format(afe=tafe),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basename + "_%i_%s.png" % (ii, ext))
return None
def plot_mapsurfdens(plotname):
with open('../mapfits/tribrokenexpflare.sav','rb') as savefile:
bf= numpy.array(pickle.load(savefile))
samples_brexp= numpy.array(pickle.load(savefile))
plotmaps= [19,26,32,39,45]
bovy_plot.bovy_print(fig_width=8.,fig_height=3.)
maps= define_rcsample.MAPs()
cmap= cm.coolwarm
overplot= False
for ii, map in enumerate(maps.map()):
if not ii in plotmaps: continue
# Create all density profiles
Rs= numpy.linspace(4.,14.,1001)
samples= samples_brexp[ii,:,::_SKIP]
nsamples= len(samples[0])
tRs= numpy.tile(Rs,(nsamples,1)).T
ldp= numpy.empty((len(Rs),nsamples))
Rb= numpy.tile(numpy.exp(samples[3]),(len(Rs),1))
ihRin= numpy.tile(samples[0],(len(Rs),1))
ihRout= numpy.tile(samples[2],(len(Rs),1))
# Rb >= R0
leRb= (tRs <= Rb)*(Rb >= densprofiles._R0)
ldp[leRb]= ihRin[leRb]*(tRs[leRb]-densprofiles._R0)
gtRb= (tRs > Rb)*(Rb >= densprofiles._R0)
ldp[gtRb]= -ihRout[gtRb]*(tRs[gtRb]-densprofiles._R0)\
+ihRout[gtRb]*(Rb[gtRb]-densprofiles._R0)\
+ihRin[gtRb]*(Rb[gtRb]-densprofiles._R0)
# Rb < R0, normalize outer at R0
leRb= (tRs <= Rb)*(Rb < densprofiles._R0)
ldp[leRb]= ihRin[leRb]*(tRs[leRb]-densprofiles._R0)\
-ihRout[leRb]*(Rb[leRb]-densprofiles._R0)\
-ihRin[leRb]*(Rb[leRb]-densprofiles._R0)
gtRb= (tRs > Rb)*(Rb < densprofiles._R0)
ldp[gtRb]= -ihRout[gtRb]*(tRs[gtRb]-densprofiles._R0)
# Label and relative normalization
tfeh= round(numpy.median(map['FE_H'])*20.)/20.
if tfeh == 0.25: tfeh= 0.3
if tfeh == -0.1: tfeh= -0.1
print ii, tfeh, len(map)
anorm= 10**(-10.*(-tfeh-0.5))
#if (-tfeh-0.4) > 0.3: anorm= 10**(-12.*(-tfeh+0.4))
if (-tfeh-0.5) == -0.2: anorm= 10**(-11.*(-tfeh-0.5))
anorm= 1./anorm # re-order
anorm/= 3.
norm= numpy.exp(numpy.median(ldp,axis=1))[numpy.argmin(numpy.fabs(Rs-densprofiles._R0))]/anorm
bovy_plot.bovy_plot(Rs,numpy.exp(numpy.median(ldp,axis=1))/norm,
'-',
color=cmap((tfeh+0.5)*0.95/0.5+0.05),
lw=2.,overplot=overplot,
ylabel=r'$\Sigma(R)\times\mathrm{constant}$',
xrange=[0.,16.],
yrange=[0.000001,90.],
semilogy=True)
pyplot.fill_between(Rs,
numpy.exp(numpy.sort(ldp,axis=1)[:,int(round(_SIGNIF*nsamples))])/norm,
numpy.exp(numpy.sort(ldp,axis=1)[:,int(round((1.-_SIGNIF)*nsamples))])/norm,
color=cmap((tfeh+0.5)/0.4),
lw=0.)
overplot= True
if ii == 19:
bovy_plot.bovy_text(2.,
10.**1.,
r'$[\mathrm{Fe/H}]$',size=16.,color='k')
bovy_plot.bovy_text(2.,(numpy.exp(numpy.median(ldp,axis=1))/norm)[0],
r'$%+.1f$' % tfeh,size=16.,
color=cmap((tfeh+0.5)*0.95/0.5+0.05))
bovy_plot.bovy_text(10.,10.**1.,
r'$\mathrm{high-}[\alpha/\mathrm{Fe}]\ \mathrm{MAPs}$',
size=16.)
pyplot.gca().tick_params(axis='y',which='minor',left='off',right='off')
bovy_plot.bovy_end_print(plotname)
def plot_mapflare(plotname):
with open('../mapfits/tribrokenexpflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples_brexp = numpy.array(pickle.load(savefile))
with open('../mapfits/tribrokenexpinvlinflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples_brexp_invlin = numpy.array(pickle.load(savefile))
with open('../mapfits/tribrokenexplinflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples_brexp_lin = numpy.array(pickle.load(savefile))
plotmaps = [19, 26, 32, 39, 45]
bovy_plot.bovy_print(fig_width=8., fig_height=9. * 3.99 / 8.98)
maps = define_rcsample.MAPs()
cmap = cm.coolwarm
overplot = False
for ii, map in enumerate(maps.map()):
if not ii in plotmaps: continue
# Create all flaring profiles
#Rmin= numpy.sort(map['RC_GALR_H'])[int(round(0.005*len(map)))]
#Rmax= numpy.sort(map['RC_GALR_H'])[numpy.amin([len(map)-1,int(round(0.995*len(map)))])]
Rs = numpy.linspace(4., 14., 1001)
samples = samples_brexp[ii, :, ::_SKIP]
samples_invlin = samples_brexp_invlin[ii, :, ::_SKIP]
samples_lin = samples_brexp_lin[ii, :, ::_SKIP]
nsamples = len(samples[0])
tRs = numpy.tile(Rs, (nsamples, 1)).T
ldp = numpy.empty((len(Rs), nsamples))
ldp = samples[1] * numpy.exp(samples[4] * (tRs - densprofiles._R0))
ldp_invlin= samples_invlin[1]\
*(1.+samples_invlin[4]*(tRs-densprofiles._R0)*(numpy.exp(1.)-1.))
ldp_lin= samples_lin[1]\
/(1.-(tRs-densprofiles._R0)*samples_lin[4]*(numpy.exp(1.)-1.))
ldp = 1000. / ldp # make it hz instead of its inverse
ldp_invlin = 1000. / ldp_invlin
ldp_lin = 1000. / ldp_lin
# Label and relative normalization
tfeh = round(numpy.median(map['FE_H']) * 20.) / 20.
if tfeh == 0.25: tfeh = 0.3
if tfeh == -0.0: tfeh = 0.0
offset = 10.**(-6. * (tfeh + 0.1)) - 0.4
if tfeh < -0.1: offset /= 1.5
if tfeh < -0.2: offset /= 1.15
print ii, tfeh, len(map), offset
bovy_plot.bovy_plot(
Rs,
numpy.median(ldp, axis=1) * offset,
'-',
color=cmap((tfeh + 0.5) * 0.95 / 0.5 + 0.05),
lw=2.,
overplot=overplot,
ylabel=r'$h_Z\,(\mathrm{pc})\times\mathrm{constant}$',
xrange=[0., 16.],
yrange=[10.**2., 10**5.99],
zorder=10 + ii,
semilogy=True)
#skipped because the median for the other profiles are indistinguishable for that with exponential flaring
"""
pyplot.fill_between(Rs,
numpy.median(ldp_lin,axis=1)*offset,
numpy.median(ldp_invlin,axis=1)*offset,
color='0.65',
lw=0.,zorder=ii-1)
"""
pyplot.fill_between(
Rs,
numpy.sort(ldp, axis=1)[:, int(round(_SIGNIF * nsamples))] *
offset,
numpy.sort(ldp, axis=1)[:,
int(round(
(1. - _SIGNIF) * nsamples))] * offset,
color=cmap((tfeh + 0.5) / 0.4),
lw=0.,
zorder=ii)
line, = pyplot.plot(Rs,
Rs * 0. + 300. * offset,
color=cmap((tfeh + 0.5) * 0.95 / 0.5 + 0.05),
ls='--',
lw=2. * 0.8,
zorder=ii + 5)
line.set_dashes([8, 6])
overplot = True
if ii == 19:
bovy_plot.bovy_text(2.,
10.**5.6,
r'$[\mathrm{Fe/H}]$',
size=16.,
color='k')
bovy_plot.bovy_text(2.,
numpy.median(ldp, axis=1)[0] * offset,
r'$%+.1f$' % tfeh,
size=16.,
color=cmap((tfeh + 0.5) * 0.95 / 0.5 + 0.05))
bovy_plot.bovy_text(10.,
10.**5.6,
r'$\mathrm{high-}[\alpha/\mathrm{Fe}]\ \mathrm{MAPs}$',
size=16.)
bovy_plot.bovy_end_print(plotname)
def plot_mapsurfdens(plotname):
with open('../mapfits/tribrokenexpflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples_brexp = numpy.array(pickle.load(savefile))
plotmaps = [19, 26, 32, 39, 45]
bovy_plot.bovy_print(fig_width=8., fig_height=3.)
maps = define_rcsample.MAPs()
cmap = cm.coolwarm
overplot = False
Rs = numpy.linspace(4., 14., 1001)
# Setup for saving the profiles
csvfile = open(os.path.join('..', 'out', 'mapsurfdens_highalpha.csv'), 'w')
writer = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_NONE)
writer.writerow(
['# Surface density profile for MAPs (Figure 11 in Bovy et al. 2016)'])
writer.writerow([
'# The first line lists the radii at which the surface density profiles'
])
writer.writerow(['# are evaluated'])
writer.writerow(
['# The rest of the file are the log surface profiles; the 0.025'])
writer.writerow(['# lower limit and the 0.0975 upper limit (each 1 line)'])
writer.writerow(['# Different MAPs are separated by a comment line'])
writer.writerow(['{:.2f}'.format(x) for x in Rs])
for ii, map in enumerate(maps.map()):
if not ii in plotmaps: continue
# Create all density profiles
samples = samples_brexp[ii, :, ::_SKIP]
nsamples = len(samples[0])
tRs = numpy.tile(Rs, (nsamples, 1)).T
ldp = numpy.empty((len(Rs), nsamples))
Rb = numpy.tile(numpy.exp(samples[3]), (len(Rs), 1))
ihRin = numpy.tile(samples[0], (len(Rs), 1))
ihRout = numpy.tile(samples[2], (len(Rs), 1))
# Rb >= R0
leRb = (tRs <= Rb) * (Rb >= densprofiles._R0)
ldp[leRb] = ihRin[leRb] * (tRs[leRb] - densprofiles._R0)
gtRb = (tRs > Rb) * (Rb >= densprofiles._R0)
ldp[gtRb]= -ihRout[gtRb]*(tRs[gtRb]-densprofiles._R0)\
+ihRout[gtRb]*(Rb[gtRb]-densprofiles._R0)\
+ihRin[gtRb]*(Rb[gtRb]-densprofiles._R0)
# Rb < R0, normalize outer at R0
leRb = (tRs <= Rb) * (Rb < densprofiles._R0)
ldp[leRb]= ihRin[leRb]*(tRs[leRb]-densprofiles._R0)\
-ihRout[leRb]*(Rb[leRb]-densprofiles._R0)\
-ihRin[leRb]*(Rb[leRb]-densprofiles._R0)
gtRb = (tRs > Rb) * (Rb < densprofiles._R0)
ldp[gtRb] = -ihRout[gtRb] * (tRs[gtRb] - densprofiles._R0)
# Label and relative normalization
tfeh = round(numpy.median(map['FE_H']) * 20.) / 20.
if tfeh == 0.25: tfeh = 0.3
if tfeh == -0.1: tfeh = -0.1
print ii, tfeh, len(map)
anorm = 10**(-10. * (-tfeh - 0.5))
#if (-tfeh-0.4) > 0.3: anorm= 10**(-12.*(-tfeh+0.4))
if (-tfeh - 0.5) == -0.2: anorm = 10**(-11. * (-tfeh - 0.5))
anorm = 1. / anorm # re-order
anorm /= 3.
norm = numpy.exp(numpy.median(ldp, axis=1))[numpy.argmin(
numpy.fabs(Rs - densprofiles._R0))] / anorm
bovy_plot.bovy_plot(Rs,
numpy.exp(numpy.median(ldp, axis=1)) / norm,
'-',
color=cmap((tfeh + 0.5) * 0.95 / 0.5 + 0.05),
lw=2.,
overplot=overplot,
ylabel=r'$\Sigma(R)\times\mathrm{constant}$',
xrange=[0., 16.],
yrange=[0.000001, 90.],
semilogy=True)
pyplot.fill_between(
Rs,
numpy.exp(
numpy.sort(ldp, axis=1)[:, int(round(_SIGNIF * nsamples))]) /
norm,
numpy.exp(
numpy.sort(ldp,
axis=1)[:, int(round(
(1. - _SIGNIF) * nsamples))]) / norm,
color=cmap((tfeh + 0.5) / 0.4),
lw=0.)
overplot = True
if ii == 19:
bovy_plot.bovy_text(2.,
10.**1.,
r'$[\mathrm{Fe/H}]$',
size=16.,
color='k')
bovy_plot.bovy_text(2.,
(numpy.exp(numpy.median(ldp, axis=1)) / norm)[0],
r'$%+.1f$' % tfeh,
size=16.,
color=cmap((tfeh + 0.5) * 0.95 / 0.5 + 0.05))
writer.writerow(['# High-alpha MAP w/ [Fe/H]=%g' % tfeh])
writer.writerow(
['{:.3f}'.format(x) for x in list(numpy.median(ldp, axis=1))])
writer.writerow([
'{:.3f}'.format(x) for x in list(
numpy.sort(ldp, axis=1)[:, int(round(_SIGNIF * nsamples))])
])
writer.writerow([
'{:.3f}'.format(x) for x in list(
numpy.sort(ldp, axis=1)[:,
int(round((1. - _SIGNIF) * nsamples))])
])
csvfile.close()
bovy_plot.bovy_text(10.,
10.**1.,
r'$\mathrm{high-}[\alpha/\mathrm{Fe}]\ \mathrm{MAPs}$',
size=16.)
pyplot.gca().tick_params(axis='y', which='minor', left='off', right='off')
bovy_plot.bovy_end_print(plotname)
def plot_mapflarepdf(savename, plotname):
# Load the samples
with open('../mapfits/tribrokenexpflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples = numpy.array(pickle.load(savefile))
maps = define_rcsample.MAPs()
# Loop through the low-alpha MAPs and compute the XD decomposition
if 'lowalpha' in savename:
plotmaps = [9, 16, 23, 29, 36, 43, 50, 57, 64, 71]
else:
plotmaps = [19, 26, 32, 39, 45]
if not os.path.exists(savename):
ngauss = 2
allxamp = numpy.empty((len(plotmaps), ngauss))
allxmean = numpy.empty((len(plotmaps), ngauss, 1))
allxcovar = numpy.empty((len(plotmaps), ngauss, 1, 1))
cnt = 0
for ii, map in enumerate(maps.map()):
if not ii in plotmaps: continue
print ii
# Fit PDFs with XD
xamp = numpy.array([0.45, 0.5])
xmean = numpy.array([
numpy.mean(samples[ii, 4]) +
numpy.random.normal() * numpy.std(samples[ii, 4]),
numpy.mean(samples[ii, 4]) +
numpy.random.normal() * numpy.std(samples[ii, 4])
])[:, numpy.newaxis]
xcovar = numpy.reshape(
numpy.tile(numpy.var(samples[ii, 4]), (2, 1)), (2, 1, 1))
XD.extreme_deconvolution(samples[ii, 4][:, numpy.newaxis],
numpy.zeros((len(samples[ii, 4]), 1)),
xamp, xmean, xcovar)
allxamp[cnt] = xamp
allxmean[cnt] = xmean
allxcovar[cnt] = xcovar
cnt += 1
save_pickles(savename, allxamp, allxmean, allxcovar)
else:
with open(savename, 'rb') as savefile:
allxamp = pickle.load(savefile)
allxmean = pickle.load(savefile)
allxcovar = pickle.load(savefile)
# Now plot
cmap = cm.coolwarm
xrange = [-0.37, 0.25]
if 'lowalpha' in savename:
# xrange= [-0.4,0.2]
yrange = [0., 30.]
combDiv = 2.
colorFunc = lambda x: cmap((x + 0.6) * 0.95 / 0.9 + 0.05)
else:
# xrange= [-0.3,0.3]
yrange = [0., 13.5]
colorFunc = lambda x: cmap((x + 0.5) * 0.95 / 0.5 + 0.05)
combDiv = 1.5
overplot = False
plotXDFit = True
cnt = 0
bovy_plot.bovy_print(axes_labelsize=18,
text_fontsize=18,
xtick_labelsize=14,
ytick_labelsize=14)
for ii, map in enumerate(maps.map()):
if not ii in plotmaps: continue
tfeh = round(numpy.median(map['FE_H']) * 20.) / 20.
if tfeh == 0.25: tfeh = 0.3
if tfeh == -0.1: tfeh = -0.1
bovy_plot.bovy_hist(
samples[ii, 4],
range=xrange,
bins=51,
overplot=overplot,
yrange=yrange,
histtype='step',
normed=True,
zorder=2,
color=colorFunc(tfeh),
xlabel=r'$R_{\mathrm{flare}}^{-1}\,(\mathrm{kpc}^{-1})$')
if plotXDFit:
txs = numpy.linspace(xrange[0], xrange[1], 1001)
pyplot.plot(
txs,
1. / numpy.sqrt(2. * numpy.pi) *
(allxamp[cnt, 0] / numpy.sqrt(allxcovar[cnt, 0, 0, 0]) *
numpy.exp(-0.5 * (txs - allxmean[cnt, 0, 0])**2. /
allxcovar[cnt, 0, 0, 0]) +
allxamp[cnt, 1] / numpy.sqrt(allxcovar[cnt, 1, 0, 0]) *
numpy.exp(-0.5 * (txs - allxmean[cnt, 1, 0])**2. /
allxcovar[cnt, 1, 0, 0])),
color=colorFunc(tfeh),
zorder=1)
overplot = True
cnt += 1
txs = numpy.linspace(xrange[0], xrange[1], 1001)
comb = numpy.ones_like(txs)
for ii in range(len(plotmaps)):
comb *= 1. / numpy.sqrt(2. * numpy.pi) * (
allxamp[ii, 0] / numpy.sqrt(allxcovar[ii, 0, 0, 0]) *
numpy.exp(-0.5 *
(txs - allxmean[ii, 0, 0])**2. / allxcovar[ii, 0, 0, 0])
+ allxamp[ii, 1] / numpy.sqrt(allxcovar[ii, 1, 0, 0]) *
numpy.exp(-0.5 *
(txs - allxmean[ii, 1, 0])**2. / allxcovar[ii, 1, 0, 0]))
comb /= numpy.sum(comb) * (txs[1] - txs[0])
pyplot.plot(txs, comb / combDiv, 'k-', lw=2., zorder=20)
pyplot.plot([0., 0.], [0., 50.], 'k--', lw=1.5, zorder=0)
t = pyplot.text(
xrange[0] + 0.25 * (xrange[1] - xrange[0]) + 0.03 *
('highalpha' in savename),
0.8 * yrange[1],
r'$R_{\mathrm{flare}}^{-1} = %.2f \pm %.2f\,\mathrm{kpc}^{-1}$' %
(numpy.sum(comb * txs) / numpy.sum(comb),
numpy.sqrt(
numpy.sum(comb * txs**2.) / numpy.sum(comb) -
(numpy.sum(comb * txs) / numpy.sum(comb))**2.)),
size=18.)
t.set_bbox(dict(color='w', edgecolor='none'))
if 'lowalpha' in savename:
bovy_plot.bovy_text(
r'$\mathrm{low-}[\alpha/\mathrm{Fe}]\ \mathrm{MAPs}$',
top_left=True,
size=16.)
else:
bovy_plot.bovy_text(
r'$\mathrm{high-}[\alpha/\mathrm{Fe}]\ \mathrm{MAPs}$',
top_left=True,
size=16.)
bovy_plot.bovy_end_print(plotname)
return None
def plot_mapflare(plotname):
# Open all three alternative models
with open('../mapfits/tribrokenexpflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples_brexp = numpy.array(pickle.load(savefile))
with open('../mapfits/tribrokenexpinvlinflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples_brexp_invlin = numpy.array(pickle.load(savefile))
with open('../mapfits/tribrokenexplinflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples_brexp_lin = numpy.array(pickle.load(savefile))
plotmaps = [16, 23, 29, 36, 43, 50, 57, 64, 71]
bovy_plot.bovy_print(fig_width=8., fig_height=9. * 4.99 / 8.98)
maps = define_rcsample.MAPs()
cmap = cm.coolwarm
overplot = False
Rs = numpy.linspace(4., 14., 1001)
# Setup for saving the profiles
csvfile = open(os.path.join('..', 'out', 'mapflare.csv'), 'w')
writer = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_NONE)
writer.writerow(
['# Scale height profile for MAPs (Figure 13 in Bovy et al. 2016)'])
writer.writerow([
'# The first line lists the radii at which the scale height profiles'
])
writer.writerow(['# are evaluated'])
writer.writerow(
['# The rest of the file are the scale heights; the 0.025'])
writer.writerow(['# lower limit and the 0.0975 upper limit (each 1 line)'])
writer.writerow(['# Different MAPs are separated by a comment line'])
writer.writerow(['{:.2f}'.format(x) for x in Rs])
for ii, map in enumerate(maps.map()):
if not ii in plotmaps: continue
# Create all flaring profiles
#Rmin= numpy.sort(map['RC_GALR_H'])[int(round(0.005*len(map)))]
#Rmax= numpy.sort(map['RC_GALR_H'])[numpy.amin([len(map)-1,int(round(0.995*len(map)))])]
samples = samples_brexp[ii, :, ::_SKIP]
samples_invlin = samples_brexp_invlin[ii, :, ::_SKIP]
samples_lin = samples_brexp_lin[ii, :, ::_SKIP]
nsamples = len(samples[0])
tRs = numpy.tile(Rs, (nsamples, 1)).T
ldp = numpy.empty((len(Rs), nsamples))
ldp = samples[1] * numpy.exp(samples[4] * (tRs - densprofiles._R0))
ldp_invlin= samples_invlin[1]\
*(1.+samples_invlin[4]*(tRs-densprofiles._R0)*(numpy.exp(1.)-1.))
ldp_lin= samples_lin[1]\
/(1.-(tRs-densprofiles._R0)*samples_lin[4]*(numpy.exp(1.)-1.))
ldp = 1000. / ldp # make it hz instead of its inverse
ldp_invlin = 1000. / ldp_invlin
ldp_lin = 1000. / ldp_lin
# Label and relative normalization
tfeh = round(numpy.median(map['FE_H']) * 20.) / 20.
if tfeh == 0.25: tfeh = 0.3
if tfeh == -0.0: tfeh = 0.0
offset = 10.**(4. * (tfeh + 0.5))
if tfeh == 0.3: offset *= 3.
print ii, tfeh, len(map), offset
bovy_plot.bovy_plot(
Rs,
numpy.median(ldp, axis=1) * offset,
'-',
color=cmap((tfeh + 0.6) * 0.95 / 0.9 + 0.05),
lw=2.,
overplot=overplot,
xlabel=r'$R\,(\mathrm{kpc})$',
ylabel=r'$h_Z\,(\mathrm{pc})\times\mathrm{constant}$',
xrange=[0., 16.],
yrange=[10.**2., 10.**6.99],
zorder=12 + ii,
semilogy=True)
pyplot.fill_between(Rs,
numpy.median(ldp_lin, axis=1) * offset,
numpy.median(ldp_invlin, axis=1) * offset,
color='0.65',
lw=0.,
zorder=ii - 1)
pyplot.fill_between(
Rs,
numpy.sort(ldp, axis=1)[:, int(round(_SIGNIF * nsamples))] *
offset,
numpy.sort(ldp, axis=1)[:,
int(round(
(1. - _SIGNIF) * nsamples))] * offset,
color=cmap((tfeh + 0.6)),
lw=0.,
zorder=ii)
line, = pyplot.plot(Rs,
Rs * 0. + 300. * offset,
color=cmap((tfeh + 0.6) * 0.95 / 0.9 + 0.05),
ls='-',
lw=2. * 0.8,
zorder=ii + 5)
line.set_dashes([8, 6])
overplot = True
if ii == 16:
bovy_plot.bovy_text(2.,
10.**6.3,
r'$[\mathrm{Fe/H}]$',
size=16.,
color='k')
bovy_plot.bovy_text(2.,
numpy.median(ldp, axis=1)[0] * offset,
r'$%+.1f$' % tfeh,
size=16.,
color=cmap((tfeh + 0.6) * 0.95 / 0.9 + 0.05))
writer.writerow(['# Low-alpha MAP w/ [Fe/H]=%g' % tfeh])
writer.writerow(
['{:.3f}'.format(x) for x in list(numpy.median(ldp, axis=1))])
writer.writerow([
'{:.3f}'.format(x) for x in list(
numpy.sort(ldp, axis=1)[:, int(round(_SIGNIF * nsamples))])
])
writer.writerow([
'{:.3f}'.format(x) for x in list(
numpy.sort(ldp, axis=1)[:,
int(round((1. - _SIGNIF) * nsamples))])
])
csvfile.close()
bovy_plot.bovy_text(1.,
10.**6.6,
r'$\mathrm{low-}[\alpha/\mathrm{Fe}]\ \mathrm{MAPs}$',
size=16.)
bovy_plot.bovy_end_print(plotname)
def plot_maphz(plotname):
# Load the three fits
with open('../mapfits/tribrokenexpflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples = numpy.array(pickle.load(savefile))
with open('../mapfits/tribrokenexp.sav', 'rb') as savefile:
bfnf = numpy.array(pickle.load(savefile))
samplesnf = numpy.array(pickle.load(savefile))
with open('../mapfits/tribrokenexpfixedflare.sav', 'rb') as savefile:
bfff = numpy.array(pickle.load(savefile))
samplesff = numpy.array(pickle.load(savefile))
maps = define_rcsample.MAPs()
plotthisz = numpy.zeros(len(bf)) + numpy.nan
plotthisze = numpy.zeros(len(bf)) + numpy.nan
for ii, map in enumerate(maps.map()):
if numpy.median(
numpy.exp(
samples[ii,
3])[True -
numpy.isnan(numpy.exp(samples[ii, 3]))]) < 5.:
tmed = numpy.median(
(1. / samplesnf[ii, 1])[True -
numpy.isnan(1. / samplesnf[ii, 1])])
terr = numpy.std(
(1. / samplesnf[ii, 1])[True -
numpy.isnan(1. / samplesnf[ii, 1])])
else:
tmed = numpy.median(
(1. / samplesff[ii, 1])[True -
numpy.isnan(1. / samplesff[ii, 1])])
terr = numpy.std(
(1. / samplesff[ii, 1])[True -
numpy.isnan(1. / samplesff[ii, 1])])
plotthisz[ii] = tmed
plotthisze[ii] = terr
plotthisz[plotthisze / plotthisz > 0.2] = numpy.nan
bovy_plot.bovy_print()
maps.plot(plotthisz * 1000.,
vmin=200.,
vmax=1000.,
minnstar=15,
zlabel=r'$h_Z\,(\mathrm{pc})$',
shrink=0.655)
# Sequences
haloc = define_rcsample.highalphalocus()
bovy_plot.bovy_plot(haloc[:, 0],
haloc[:, 1],
'-',
color='0.75',
lw=2.5,
overplot=True)
haloc = define_rcsample.lowalphalocus()
haloc = haloc[(haloc[:, 0] > -0.55) * (haloc[:, 0] < 0.225)]
bovy_plot.bovy_plot(haloc[:, 0],
haloc[:, 1],
'-',
color='0.75',
lw=2.5,
overplot=True)
# Label
#t= pyplot.text(-0.51,0.235,r'$\mathrm{single}$',
# size=16.,color='w')
#t.set_bbox(dict(alpha=0.5,color=cm.coolwarm(0.),
# edgecolor='none'))
#t= pyplot.text(-0.475,0.195,r'$\mathrm{exponential}$',
# size=16.,color='w')
#t.set_bbox(dict(alpha=0.5,color=cm.coolwarm(0.),
# edgecolor='none'))
pyplot.tight_layout()
bovy_plot.bovy_end_print(plotname, dpi=300)
return None
