def stellarHaloMFRatio(path,
database,
redshifts,
output_folder,
outfile,
xmin=10.0,
xmax=14.4,
ymin=-3,
ymax=-1,
nbins=15,
h=0.7):
'''
Plots stellar to dark matter halo mass ratios as a function of redshift.
'''
#get Behroozi et al. fits
BFits = g.glob(
'/Users/niemi/Dropbox/Research/Observations/behroozi/results/*.dat')
#get a colour scheme
cols = pt.give_colours()
#scale the SAM's output masses with this factor
multiply = 1e9
#make the figure and axes instance
fig = P.figure()
ax = fig.add_subplot(111)
#plot the different redshifts
for ii, redshift in enumerate(redshifts):
#get redshift, add 0.1 so that int/floor returns the closest int
tmp = redshift.split()
rd = int(float(tmp[2]) + 0.1)
#generate the SQL query
query = '''select mstar_disk, mbulge, mhalo from galpropz where ''' + redshift
query += ' and mstar_disk >= 0.0 and mbulge >= 0.0'
query += ' and mstar_disk + mbulge > 0.0'
query += ' and gal_id = 1'
#load Behroozi et al fit data
BF = N.loadtxt(BFits[ii + 1])
ax.plot(
BF[:, 0], # - N.log10(h),
BF[:, 1],
color=cols[ii],
ls='--',
label='Behroozi et al.: $z \sim %s$' %
(BFits[ii + 1].split('z')[2][:4]))
logging.debug(BFits[ii + 1])
#get data from the SQLite3 db
dat = db.sqlite.get_data_sqlite(path, database, query)
#rename data and calculate the ratio
mstar = dat[:, 0] + dat[:, 1]
mhalo = dat[:, 2]
ratio = N.log10(mstar / mhalo)
mhalo = N.log10(mhalo * multiply)
#print ratio, N.min(ratio), N.max(ratio)
#debug output
ngal = len(mstar)
logging.debug('%i galaxies found at z = %i\n' % (ngal, rd))
#bin the data
xbin_mid, y50, y16, y84 = dm.percentile_bins(mhalo,
ratio,
xmin,
xmax,
nxbins=nbins)
msk = y50 > -10
#plot the binned data
# ax.errorbar(xbin_mid[msk],
# y50[msk],
# yerr = [y50[msk]-y16[msk], y84[msk]-y50[msk]],
# color = cols[ii],
# label = 'Bolshoi+SAM: $z \sim %i$' % rd)
ax.plot(xbin_mid[msk],
y50[msk],
color=cols[ii],
ms='o',
ls='-',
label='Bolshoi+SAM: $z \sim %i$' % rd)
#set axes scales and labels
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
ax.set_xlabel(
r'$\log_{10} \left ( M_{\mathrm{dm}} \ [\mathrm{M}_{\odot}] \right )$')
ax.set_ylabel(
r'$\log_{10} \left (\frac{M_{\star}}{M_{\mathrm{dm}}} \right )$')
#set small ticks
m = ax.get_yticks()[1] - ax.get_yticks()[0]
yminorLocator = MultipleLocator(m / 5.)
yminorFormattor = NullFormatter()
ax.yaxis.set_minor_locator(yminorLocator)
ax.yaxis.set_minor_formatter(yminorFormattor)
m = ax.get_xticks()[1] - ax.get_xticks()[0]
xminorLocator = MultipleLocator(m / 5.)
xminorFormattor = NullFormatter()
ax.xaxis.set_minor_locator(xminorLocator)
ax.xaxis.set_minor_formatter(xminorFormattor)
P.legend(shadow=True, fancybox=True, numpoints=1)
P.savefig(output_folder + outfile + '.pdf')
P.close()
def plot_properties(query,
xlabel,
ylabel,
output,
out_folder,
flux=5,
band=250,
pmin=0.1,
pmax=1.0,
xbin=15,
ybin=11,
xmin=7.9,
xmax=11.7,
ymin=0,
ymax=50):
'''
Plots
'''
#get data
data = sq.get_data_sqlitePowerTen(path, db, query)
#set 1: all galaxies
xd = data[:, 0]
yd = data[:, 1]
#set 2
mask = data[:, 1] > flux
xe = data[:, 0][mask]
ye = data[:, 1][mask]
#x limits for the right hand (scatter) plot
secxmin, secxmax = 0.99 * N.min(xe), 1.005 * N.max(xe)
if 'mstardot' in query:
secxmax = 1200.
if 'burst' in query:
secxmax = 1500.
#hess
sd, sdmin, sdmax = h.hess_plot(xd,
yd,
N.ones(len(xd)),
xmin,
xmax,
xbin,
ymin,
ymax,
ybin,
pmax=pmax,
pmin=pmin)
se, semin, semax = h.hess_plot(xe,
ye,
N.ones(len(xe)),
secxmin,
secxmax,
xbin - 4,
flux,
ymax,
ybin - 1,
pmax=pmax,
pmin=pmin)
#figure
# f, (ax1, ax2) = P.subplots(1, 2, sharey=True)
# f, (ax1, ax2) = P.subplots(1, 2, figsize=(10,10))
f, (ax1, ax2) = P.subplots(1, 2)
f.subplots_adjust(wspace=0.0,
hspace=0.01,
left=0.08,
bottom=0.07,
right=0.97,
top=0.93)
#hess plots
ims = ax1.imshow(sd,
vmin=sdmin,
vmax=sdmax,
origin='lower',
cmap=cm.gray,
interpolation=None,
extent=[xmin, xmax, ymin, ymax],
aspect='auto',
alpha=1)
ims = ax2.imshow(se,
vmin=semin,
vmax=semax,
origin='lower',
cmap=cm.gray,
interpolation=None,
extent=[secxmin, secxmax, flux, ymax],
aspect='auto',
alpha=1)
#flux limit lines
ax1.axhline(flux, color='green', ls='--')
ax2.axhline(flux, color='green', ls='--')
#scatter to the second plot
ax2.scatter(xe, ye, s=6, color='b')
#percentiles
xbin_midd, y50d, y16d, y84d = dm.percentile_bins(xd, yd, xmin, xmax)
md = (y50d > 0) | (y16d > 0) | (y84d > 0)
xbin_mide, y50e, y16e, y84e = dm.percentile_bins(xe, ye, secxmin, secxmax)
me = (y50e > 0) | (y16e > 0) | (y84e > 0)
ax1.plot(xbin_midd[md], y50d[md], 'r-')
ax1.plot(xbin_midd[md], y16d[md], 'r--')
ax1.plot(xbin_midd[md], y84d[md], 'r--')
ax2.plot(xbin_mide[me], y50e[me], 'r-')
ax2.plot(xbin_mide[me], y16e[me], 'r--')
ax2.plot(xbin_mide[me], y84e[me], 'r--')
#add text
P.text(0.5,
0.95,
'All galaxies',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
P.text(0.5,
0.95,
r'%s > %.1f $ mJy' % (ylabel.split()[0], flux),
horizontalalignment='center',
verticalalignment='center',
transform=ax2.transAxes)
#labels
ax1.set_xlabel(xlabel)
ax2.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#y tick positions
# ax1.set_yticks(yticks)
#ax2.set_yticks(yticks[1:-2])
#limits
ax1.set_xlim(xmin, xmax)
ax2.set_xlim(secxmin, secxmax)
ax1.set_ylim(ymin, ymax)
ax2.set_ylim(ymin, ymax)
ax2.set_yticks(ax2.get_yticks()[1:-1])
# ax2.set_xticks(ax2.get_xticks()[1:])
#make grid
ax1.grid()
ax2.grid()
P.savefig(out_folder + output)
def plot_ages(query,
xlabel,
ylabel,
output,
out_folder,
flux=5,
band=250,
title='$2.0 < z < 4.0$',
pmin=0.05,
pmax=1.0,
xbin=10,
ybin=15,
xmin=7.9,
xmax=11.7,
ymin=-1.5,
ymax=1.8,
scatters=False,
mean=False):
'''
Plots age versus a given quantity.
'''
#get data
data = N.array(sq.get_data_sqlitePowerTen(path, db, query))
#observable galaxies
mask = data[:, 1] > flux
xe = data[:, 0][mask]
ye = N.log10(data[:, 1][mask])
#all galaxies
xd = data[:, 0]
yd = N.log10(data[:, 1])
mask = yd != N.nan
xd = xd[mask]
yd = yd[mask]
#hess
sd, sdmin, sdmax = h.hess_plot(xd,
yd,
N.ones(len(xd)),
xmin,
xmax,
xbin,
ymin,
ymax,
ybin,
pmax=pmax,
pmin=pmin)
se, semin, semax = h.hess_plot(xe,
ye,
N.ones(len(xe)),
xmin,
xmax,
xbin,
N.log10(flux),
ymax,
ybin,
pmax=pmax,
pmin=pmin)
#figure
fig = P.figure()
fig.suptitle(title)
f, (ax1, ax2) = P.subplots(1, 2, sharey=True)
f.subplots_adjust(wspace=0.0,
hspace=0.01,
left=0.08,
bottom=0.07,
right=0.97,
top=0.93)
#contours
ims = ax1.imshow(sd,
vmin=sdmin,
vmax=sdmax,
origin='lower',
cmap=cm.gray,
interpolation=None,
extent=[xmin, xmax, ymin, ymax],
aspect='auto',
alpha=1)
ims = ax2.imshow(se,
vmin=semin,
vmax=semax,
origin='lower',
cmap=cm.gray,
interpolation=None,
extent=[xmin, xmax, N.log10(flux), ymax],
aspect='auto',
alpha=1)
#percentiles
xbin_midd, y50d, y16d, y84d = dm.percentile_bins(xd, yd, xmin, xmax)
md = (y50d > 0) & (y16d > 0) & (y84d > 0)
xbin_mide, y50e, y16e, y84e = dm.percentile_bins(xe, ye, xmin, xmax)
me = (y50e > 0) & (y16e > 0) & (y84e > 0)
ax1.plot(xbin_midd[md], y50d[md], 'r-')
ax1.plot(xbin_midd[md], y16d[md], 'r--')
ax1.plot(xbin_midd[md], y84d[md], 'r--')
ax2.plot(xbin_mide[me], y50e[me], 'r-')
ax2.plot(xbin_mide[me], y16e[me], 'r--')
ax2.plot(xbin_mide[me], y84e[me], 'r--')
#dots
if scatters:
ax1.scatter(xd, yd, c='b', s=10, marker='h', label='All galaxies')
ax2.scatter(xe,
ye,
c='r',
s=10,
marker='o',
label=r'$S_{%i} > %.1f$ mJy' % (band, flux))
#hlines
if mean:
#mean size
mean_disk = N.mean(yd)
mean_early = N.mean(ye)
ax1.axhline(mean_disk, c='b', label='Mean')
ax2.axhline(mean_early, c='r', label='Mean')
#add text
P.text(0.5,
0.95,
'All galaxies',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
P.text(0.5,
0.95,
r'$S_{%i} > %.1f$ mJy' % (band, flux),
horizontalalignment='center',
verticalalignment='center',
transform=ax2.transAxes)
#labels
ax1.set_xlabel(xlabel)
ax2.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#ax2.set_yticks([])
#limits
#ax1.set_ylim(ymin, ymax)
#ax2.set_ylim(ymin, ymax)
ax1.set_xlim(xmin, xmax)
ax2.set_xlim(xmin, xmax)
#yticks
#ax1.set_yticks(y1ticks)
#ax2.set_yticks(y2ticks)
#make grid
ax1.grid()
ax2.grid()
#write a legend
ax1.legend(shadow=True,
fancybox=True,
numpoints=1,
loc='upper left',
scatterpoints=1)
ax2.legend(shadow=True,
fancybox=True,
numpoints=1,
loc='upper left',
scatterpoints=1)
P.savefig(out_folder + output)
def stellarHaloMFRatioMultiPanel(path,
database,
redshifts,
output_folder,
outfile,
xmin=10.0,
xmax=14.4,
ymin=-3,
ymax=-1,
nbins=15):
'''
Plots stellar to dark matter halo mass ratios as a function of redshift.
'''
#get Behroozi et al. fits
BFits = g.glob(hm +
'/Dropbox/Research/Observations/behroozi/results/*.dat')
#get a colour scheme
cols = pt.give_colours()
#scale the SAM's output masses with this factor
multiply = 1e9
#make the figure and axes instance
fig = P.figure()
P.subplots_adjust(left=0.13, bottom=0.1, wspace=0.0, hspace=0.0)
#plot the different redshifts
for ii, redshift in enumerate(redshifts):
#get redshift, add 0.1 so that int/floor returns the closest int
tmp = redshift.split()
rd = int(float(tmp[2]) + 0.1)
#generate the SQL query
query = '''select mstar_disk, mbulge, mhalo from galpropz where ''' + redshift
query += ' and mstar_disk >= 0.0 and mbulge >= 0.0'
query += ' and mstar_disk + mbulge > 0.0'
query += ' and gal_id = 1'
if ii < 6:
ax = fig.add_subplot(2, 3, ii + 1)
#get data from the SQLite3 db
dat = db.sqlite.get_data_sqlite(path, database, query)
#rename data and calculate the ratio
mstar = dat[:, 0] + dat[:, 1]
mhalo = dat[:, 2]
ratio = N.log10(mstar / mhalo)
mhalo = N.log10(mhalo * multiply)
#print ratio, N.min(ratio), N.max(ratio)
#debug output
ngal = len(mstar)
logging.debug('%i galaxies found at z = %i\n' % (ngal, rd))
#bin the data
xbin_mid, y50, y16, y84 = dm.percentile_bins(mhalo,
ratio,
xmin,
xmax,
nxbins=nbins)
msk = y50 > -10
#plot the binned data
if ii == 6:
ax.plot(xbin_mid[msk], y50[msk], 'k--', label='Bolshoi+SAM')
else:
ax.errorbar(xbin_mid[msk],
y50[msk],
yerr=[y50[msk] - y16[msk], y84[msk] - y50[msk]],
color='k',
label='Bolshoi+SAM')
#load Behroozi et al fit data
BF = N.loadtxt(BFits[ii + 1])
#plot Behroozi data
if ii == 6:
ax.plot(BF[:, 0],
BF[:, 1],
color='r',
ls='--',
label='Behroozi et al.')
else:
ax.plot(BF[:, 0],
BF[:, 1],
color='r',
ls='-',
label='Behroozi et al.')
#output some information
logging.debug(BFits[ii + 1])
#mark the redshift
if ii < 5:
P.text(0.5,
0.93,
r'$ z \sim %.1f $' % (rd),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=12)
elif ii == 5:
P.text(0.5,
0.93,
r'$z \sim %.1f (s)\ \& \ %.1f (d)$' % (rd, rd + 1),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=12)
#set axes scales and labels
ax.set_xlim(xmin - 0.1, xmax)
ax.set_ylim(ymin, ymax)
#set small ticks
m = ax.get_yticks()[1] - ax.get_yticks()[0]
yminorLocator = MultipleLocator(m / 5.)
yminorFormattor = NullFormatter()
ax.yaxis.set_minor_locator(yminorLocator)
ax.yaxis.set_minor_formatter(yminorFormattor)
m = ax.get_xticks()[1] - ax.get_xticks()[0]
xminorLocator = MultipleLocator(m / 5.)
xminorFormattor = NullFormatter()
ax.xaxis.set_minor_locator(xminorLocator)
ax.xaxis.set_minor_formatter(xminorFormattor)
#set legend
if ii == 4:
ax.legend(shadow=True,
fancybox=True,
numpoints=1,
loc='lower right')
#set ylabel
if ii == 0 or ii == 3:
ax.set_ylabel(
r'$\log_{10} \left (\frac{M_{\star}}{M_{\mathrm{dm}}} \right )$'
)
#set xlabel
if ii == 4: # or ii == 3 or ii == 5:
ax.set_xlabel(
r'$\log_{10} \left ( M_{\mathrm{dm}} \ [\mathrm{M}_{\odot}] \right )$'
)
#remove some y ticks
if ii == 0 or ii == 3:
ax.set_yticks(ax.get_yticks()[:-1])
else:
ax.set_yticklabels([])
#remove some x ticks
if ii == 3 or ii == 4 or ii == 5:
#ax.set_xticks(ax.get_xticks()[:-1])
continue
elif ii == 6:
continue
else:
ax.set_xticklabels([])
P.savefig(output_folder + outfile + 'Multi.pdf')
P.close()
def plot_tmerge(query1,
query2,
xlabel,
ylabel,
output,
out_folder,
pmin=0.05,
pmax=1.0,
xbin1=15,
ybin1=15,
xbin2=15,
ybin2=15,
y1ticks=[0, .4, .8, 1.2, 1.6, 2.0, 2.5, 3],
y2ticks=[.4, .8, 1.2, 1.6, 2.0, 2.5],
xmin1=7.9,
xmax1=11.7,
xmin2=7.9,
xmax2=11.7,
ymin=0.0,
ymax=3.0,
scatters=False,
mean=False):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query1)
xd1 = data[:, 0]
yd1 = data[:, 1]
#get data, S_250 > 5 mJy
data = sq.get_data_sqliteSMNfunctions(path, db, query2)
xd2 = data[:, 0]
yd2 = data[:, 1]
#the fraction of no mergers?
nm1 = len(yd1[yd1 < 0.0]) / float(len(yd1)) * 100.
nm2 = len(yd2[yd2 < 0.0]) / float(len(yd2)) * 100.
#print out some statistics
print len(yd2)
print 'Mean tmerge of all galaxies', N.mean(yd1[yd1 > 0.0])
print 'Mean tmerge of SPIRE detected galaxies', N.mean(yd2[yd2 > 0.0])
print
print 'Max tmerge of all galaxies', N.max(yd1[yd1 > 0.0])
print 'Max tmerge of SPIRE detected galaxies', N.max(yd2[yd2 > 0.0])
print
print 'Fraction of all galaxies that have experienced a merger', 100. - nm1
print 'Fraction of SPIRE that have experienced a merger', 100. - nm2
#calculate 2D probability fields
sd1, sdmin1, sdmax1 = h.hess_plot(xd1,
yd1,
N.ones(len(xd1)),
xmin1,
xmax1,
xbin1,
ymin,
ymax,
ybin1,
pmax=pmax,
pmin=pmin)
sd2, sdmin2, sdmax2 = h.hess_plot(xd2,
yd2,
N.ones(len(xd2)),
xmin2,
xmax2,
xbin2,
ymin,
ymax,
ybin2,
pmax=pmax,
pmin=pmin)
#make the figure
fig = P.figure()
fig.subplots_adjust(wspace=0.0,
hspace=0.01,
left=0.08,
bottom=0.07,
right=0.97,
top=0.93)
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ims = ax1.imshow(sd1,
vmin=sdmin1,
vmax=sdmax1,
origin='lower',
cmap=cm.gray,
interpolation=None,
extent=[xmin1, xmax1, ymin, ymax],
aspect='auto',
alpha=1)
ims = ax2.imshow(sd2,
vmin=sdmin2,
vmax=sdmax2,
origin='lower',
cmap=cm.gray,
interpolation=None,
extent=[xmin2, xmax2, ymin, ymax],
aspect='auto',
alpha=1)
ax2.scatter(xd2, yd2, s=7, marker='o', color='blue')
#percentiles
xbin_midd1, y50d1, y16d1, y84d1 = dm.percentile_bins(xd1,
yd1,
xmin1,
xmax1,
nxbins=xbin1)
md1 = (y50d1 >= 0) & (y16d1 >= 0) & (y84d1 >= 0)
xbin_midd2, y50d2, y16d2, y84d2 = dm.percentile_bins(xd2,
yd2,
xmin2,
xmax2,
nxbins=xbin2)
md2 = (y50d2 >= 0) | (y16d2 >= 0) | (y84d2 >= 0)
ax1.plot(xbin_midd1[md1], y50d1[md1], 'r-')
ax1.plot(xbin_midd1[md1], y16d1[md1], 'r--')
ax1.plot(xbin_midd1[md1], y84d1[md1], 'r--')
ax2.plot(xbin_midd2[md2], y50d2[md2], 'r-')
ax2.plot(xbin_midd2[md2], y16d2[md2], 'r--')
ax2.plot(xbin_midd2[md2], y84d2[md2], 'r--')
#add text
P.text(0.5,
0.93,
'All galaxies\n$2 \leq z < 4$',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
P.text(0.5,
0.96,
'$S_{250} > 5$ mJy',
horizontalalignment='center',
verticalalignment='center',
transform=ax2.transAxes)
#labels
ax1.set_xlabel(xlabel)
ax2.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#limits
ax1.set_ylim(ymin, ymax)
ax2.set_ylim(ymin, ymax)
ax1.set_xlim(xmin1, xmax1)
ax2.set_xlim(xmin2, xmax2)
#yticks
ax2.set_yticks([])
ax2.set_xticks(ax2.get_xticks()[1:])
ax1.set_yticks(y1ticks)
ax2.set_yticks(y2ticks)
#make grid
#ax1.grid()
#ax2.grid()
P.savefig(out_folder + output)
def plot_ssfr_paper(query1, query2, out_folder,
xmin1=7.7, xmax1=11.8, xbin1=16,
ymin1=-11, ymax1=-7.5, ybin1=16,
xmin2=9.8, xmax2=11.8, xbin2=15,
ymin2=-10, ymax2=-7.5, ybin2=15,
pmax=1.0, pmin=0.01):
#get data, all galaxies
data1 = sq.get_data_sqliteSMNfunctions(path, db, query1)
ssfr1 = N.log10(data1[:, 0] / (10 ** data1[:, 1]))
x1 = data1[:, 1]
#get data, S_250 > 5mJy
data2 = sq.get_data_sqliteSMNfunctions(path, db, query2)
ssfr2 = N.log10(data2[:, 0] / (10 ** data2[:, 1]))
x2 = data2[:, 1]
#make hess data
s1, smin1, smax1 = h.hess_plot(x1, ssfr1,
N.ones(len(x1)),
xmin1, xmax1, xbin1,
ymin1, ymax1, ybin1,
pmax, pmin)
s2, smin2, smax2 = h.hess_plot(x2, ssfr2,
N.ones(len(x2)),
xmin2, xmax2, xbin2,
ymin2, ymax2, ybin2,
pmax, pmin)
#make the figure
fig = P.figure()
fig.subplots_adjust(wspace=0.0, hspace=0.01,
bottom=0.07,
right=0.97, top=0.93)
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ims = ax1.imshow(s1, vmin=smin1, vmax=smax1,
origin='lower', cmap=cm.gray,
interpolation=None,
extent=[xmin1, xmax1, ymin1, ymax1],
aspect='auto', alpha=1)
ims = ax2.imshow(s2, vmin=smin2, vmax=smax2,
origin='lower', cmap=cm.gray,
interpolation=None,
extent=[xmin2, xmax2, ymin2, ymax2],
aspect='auto', alpha=1)
#scatter plot
ax2.scatter(x2, ssfr2, c='b', marker='o', s=5)
#percentiles
xbin_midd, y50d, y16d, y84d = dm.percentile_bins(x1, ssfr1,
xmin1, xmax1)
md = (y50d > -99) | (y16d > -99) | (y84d > -99)
xbin_mide, y50e, y16e, y84e = dm.percentile_bins(x2, ssfr2,
xmin2, xmax2)
me = (y50e > -99) | (y16e > -99) | (y84e > -99)
ax1.plot(xbin_midd[md], y50d[md], 'r-')
ax1.plot(xbin_midd[md], y16d[md], 'r--')
ax1.plot(xbin_midd[md], y84d[md], 'r--')
ax2.plot(xbin_mide[me], y50e[me], 'r-')
ax2.plot(xbin_mide[me], y16e[me], 'r--')
ax2.plot(xbin_mide[me], y84e[me], 'r--')
#labels
ax1.set_xlabel(r'$\log_{10}(M_{\star} \ [M_{\odot}])$')
ax2.set_xlabel(r'$\log_{10}(M_{\star} \ [M_{\odot}])$')
ax1.set_ylabel(r'$\log_{10} \left (\frac{\dot{M}_{\star}}{M_{\star}} \ [\mathrm{yr}^{-1}] \right )$')
#limits
ax1.set_ylim(ymin1, ymax1)
ax2.set_ylim(ymin1, ymax1)
ax1.set_xlim(xmin1, xmax1)
ax2.set_xlim(xmin2, xmax2)
#add text
P.text(0.5, 0.94, 'All galaxies\n$2 \leq z < 4$',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
P.text(0.5, 0.97, '$S_{250} > 5$ mJy',
horizontalalignment='center',
verticalalignment='center',
transform=ax2.transAxes)
#yticks
ax2.set_yticks(ax1.get_yticks()[1:-1])
#make grid
ax1.grid()
ax2.grid()
P.savefig(out_folder + 'ssfr.ps')
P.close()
def plot_size_paper(xd1,
xd2,
yd1,
yd2,
xlabel,
ylabel,
output,
out_folder,
pmin=0.05,
pmax=1.0,
xbin1=15,
ybin1=15,
xbin2=15,
ybin2=15,
y1ticks=[1, 3, 5, 7, 9, 12],
y2ticks=[1, 3, 5, 7, 9],
xmin1=7.9,
xmax1=11.7,
xmin2=7.9,
xmax2=11.7,
ymin=0.1,
ymax=12,
scatters=False,
mean=False):
#calculate hess
sd1, sdmin1, sdmax1 = h.hess_plot(xd1,
yd1,
N.ones(len(xd1)),
xmin1,
xmax1,
xbin1,
ymin,
ymax,
ybin1,
pmax=pmax,
pmin=pmin)
sd2, sdmin2, sdmax2 = h.hess_plot(xd2,
yd2,
N.ones(len(xd2)),
xmin2,
xmax2,
xbin2,
ymin,
ymax,
ybin2,
pmax=pmax,
pmin=pmin)
#load obs data
file = os.getenv(
'HOME') + '/Dropbox/Research/Herschel/obs_data/CavaSizesValues.txt'
data = N.loadtxt(file, comments='#', usecols=(0, 1))
#figure
fig = P.figure()
#fig.suptitle('Late-type Galaxies')
fig.subplots_adjust(wspace=0.0,
hspace=0.01,
left=0.08,
bottom=0.07,
right=0.97,
top=0.93)
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ims = ax1.imshow(sd1,
vmin=sdmin1,
vmax=sdmax1,
origin='lower',
cmap=cm.gray,
interpolation=None,
extent=[xmin1, xmax1, ymin, ymax],
aspect='auto',
alpha=1)
ims = ax2.imshow(sd2,
vmin=sdmin2,
vmax=sdmax2,
origin='lower',
cmap=cm.gray,
interpolation=None,
extent=[xmin2, xmax2, ymin, ymax],
aspect='auto',
alpha=1)
ax2.scatter(xd2,
yd2,
s=1,
marker='o',
color='blue',
label='$S_{250} > 5$ mJy')
ax1.scatter(data[:, 0], data[:, 1], s=45, marker='s', color='green')
ax2.scatter(data[:, 0],
data[:, 1],
s=45,
marker='s',
color='green',
label='Cava et al 2010')
#percentiles
xbin_midd1, y50d1, y16d1, y84d1 = dm.percentile_bins(xd1,
yd1,
xmin1,
xmax1,
nxbins=2 * xbin1,
limit=25)
md1 = (y50d1 > 0) & (y16d1 > 0) & (y84d1 > 0)
xbin_midd2, y50d2, y16d2, y84d2 = dm.percentile_bins(xd2,
yd2,
xmin2,
xmax2,
nxbins=xbin2,
limit=12)
md2 = (y50d2 > 0) | (y16d2 > 0) | (y84d2 > 0)
ax1.plot(xbin_midd1[md1], y50d1[md1], 'r-')
ax1.plot(xbin_midd1[md1], y16d1[md1], 'r--')
ax1.plot(xbin_midd1[md1], y84d1[md1], 'r--')
ax2.plot(xbin_midd2[md2], y50d2[md2], 'r-')
ax2.plot(xbin_midd2[md2], y16d2[md2], 'r--')
ax2.plot(xbin_midd2[md2], y84d2[md2], 'r--')
#add text
P.text(0.5,
0.95,
'Late-type galaxies\n$2 \leq z < 4$',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
# P.text(0.5, 0.95,'$S_{250} > 5$ mJy',
# horizontalalignment='center',
# verticalalignment='center',
# transform = ax2.transAxes)
#labels
ax1.set_xlabel(xlabel)
ax2.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#yticks
ax2.set_yticks([])
ax1.set_xticks(ax1.get_xticks()[:-1])
ax2.set_xticks(ax2.get_xticks()[1:])
ax1.set_yticks(y1ticks)
ax2.set_yticks(y2ticks)
#limits
ax1.set_ylim(ymin, ymax)
ax2.set_ylim(ymin, ymax)
ax1.set_xlim(xmin1, xmax1)
ax2.set_xlim(xmin2, xmax2)
#make grid
#ax1.grid()
#ax2.grid()
#write a legend
#ax1.legend(shadow = True, fancybox = True,
# numpoints = 1, loc = 'upper left',
# scatterpoints = 1)
ax2.legend(shadow=True,
fancybox=True,
numpoints=1,
loc='upper left',
scatterpoints=1,
markerscale=1.5)
P.savefig(out_folder + output)
def plot_size_paperKDE(xd1,
xd2,
yd1,
yd2,
xlabel,
ylabel,
output,
out_folder,
xbin1=15,
ybin1=15,
xbin2=15,
ybin2=15,
y1ticks=[1, 3, 5, 7, 9, 12],
y2ticks=[1, 3, 5, 7, 9],
xmin1=7.9,
xmax1=11.7,
xmin2=7.9,
xmax2=11.7,
ymin=0.1,
ymax=12,
scatters=False,
mean=False,
lvls=None):
#calclate KDEs
mu1 = M.AnaKDE([xd1, yd1])
x_vec1, y_vec1, zm1, lvls1, d01, d11 = mu1.contour(
N.linspace(xmin1, xmax1, xbin1),
N.linspace(ymin, ymax, ybin1),
return_data=True)
mu2 = M.AnaKDE([xd2, yd2])
x_vec2, y_vec2, zm2, lvls2, d02, d12 = mu2.contour(
N.linspace(xmin2, xmax2, xbin2),
N.linspace(ymin, ymax, ybin2),
return_data=True)
#load obs data
file = os.getenv(
'HOME') + '/Dropbox/Research/Herschel/obs_data/CavaSizesValues.txt'
data = N.loadtxt(file, comments='#', usecols=(0, 1))
#make the figure
fig = P.figure()
fig.subplots_adjust(wspace=0.0,
hspace=0.01,
left=0.08,
bottom=0.07,
right=0.97,
top=0.93)
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
#make filled contours
if lvls == None:
cont = ax1.contourf(x_vec1, y_vec1, zm1, cmap=cm.get_cmap('gist_yarg'))
cont = ax2.contourf(x_vec2, y_vec2, zm2, cmap=cm.get_cmap('gist_yarg'))
else:
cont = ax1.contourf(x_vec1,
y_vec1,
zm1,
cmap=cm.get_cmap('gist_yarg'),
levels=lvls)
cont = ax2.contourf(x_vec2,
y_vec2,
zm2,
cmap=cm.get_cmap('gist_yarg'),
levels=lvls)
#plot scatter
ax2.scatter(xd2,
yd2,
s=0.5,
marker='o',
color='blue',
label='$S_{250} > 5$ mJy')
#plot observed data
ax1.scatter(data[:, 0], data[:, 1], s=45, marker='s', color='green')
ax2.scatter(data[:, 0],
data[:, 1],
s=45,
marker='s',
color='green',
label='Cava et al 2010')
#calculate and plot percentiles
xbin_midd1, y50d1, y16d1, y84d1 = dm.percentile_bins(xd1,
yd1,
xmin1,
xmax1,
nxbins=30,
limit=20)
md1 = (y50d1 > 0) & (y16d1 > 0) & (y84d1 > 0)
xbin_midd2, y50d2, y16d2, y84d2 = dm.percentile_bins(xd2,
yd2,
xmin2,
xmax2,
nxbins=15,
limit=10)
md2 = (y50d2 > 0) | (y16d2 > 0) | (y84d2 > 0)
ax1.plot(xbin_midd1[md1], y50d1[md1], 'r-')
ax1.plot(xbin_midd1[md1], y16d1[md1], 'r--')
ax1.plot(xbin_midd1[md1], y84d1[md1], 'r--')
ax2.plot(xbin_midd2[md2], y50d2[md2], 'r-')
ax2.plot(xbin_midd2[md2], y16d2[md2], 'r--')
ax2.plot(xbin_midd2[md2], y84d2[md2], 'r--')
#add text
P.text(0.5,
0.95,
'Late-type galaxies\n$2 \leq z < 4$',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#labels
ax1.set_xlabel(xlabel)
ax2.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#yticks
ax2.set_yticks([])
ax1.set_xticks(ax1.get_xticks()[:-1])
ax2.set_xticks(ax2.get_xticks()[1:])
ax1.set_yticks(y1ticks)
ax2.set_yticks(y2ticks)
#limits
ax1.set_ylim(ymin, ymax)
ax2.set_ylim(ymin, ymax)
ax1.set_xlim(xmin1, xmax1)
ax2.set_xlim(xmin2, xmax2)
#make grid
#ax1.grid()
#ax2.grid()
#write a legend
ax2.legend(shadow=True,
fancybox=True,
numpoints=1,
loc='upper left',
scatterpoints=1,
markerscale=1.5)
P.savefig(out_folder + output)
def plot_size(query,
xlabel,
ylabel,
output,
out_folder,
bulge=0.4,
title='$2.0 < z < 4.0$',
pmin=0.05,
pmax=1.0,
xbin=15,
ybin=15,
y1ticks=[1, 3, 5, 7, 10],
y2ticks=[1, 3, 5, 7],
xmin=7.9,
xmax=11.7,
ymin=0.1,
ymax=10,
scatters=False,
mean=False):
'''
Plots size versus a given quantity.
'''
#get data
data = sq.get_data_sqlitePowerTen(path, db, query)
#sphericals
mask = data[:, 2] > bulge
xe = data[:, 0][mask]
ye = data[:, 1][mask]
# disks
disks = data[:, 2] <= bulge
xd = data[:, 0][disks]
yd = data[:, 1][disks]
#hess
sd, sdmin, sdmax = h.hess_plot(xd,
yd,
N.ones(len(xd)),
xmin,
xmax,
xbin,
ymin,
ymax,
ybin,
pmax=pmax,
pmin=pmin)
se, semin, semax = h.hess_plot(xe,
ye,
N.ones(len(xe)),
xmin,
xmax,
xbin,
ymin,
ymax,
ybin,
pmax=pmax,
pmin=pmin)
#figure
# fig = P.figure(figsize = (12, 12))
fig = P.figure()
fig.suptitle(title)
fig.subplots_adjust(wspace=0.0,
hspace=0.01,
left=0.08,
bottom=0.07,
right=0.97,
top=0.93)
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
#contours
# ax1.contourf(sd, origin = 'lower', colors = 'b',
# linewidths = 0.8, linestyles = '-',
# extent = [xmin, xmax, ymin, ymax],
# rightside_up = True, alpha = 0.7)#,
# #levels = [0.01, 0.3, 0.6, 0.9])
# ax2.contourf(se, origin = 'lower', colors = 'r',
# linewidths = 0.8, linestyles = '-',
# extent = [xmin, xmax, ymin, ymax],
# rightside_up = True, alpha = 0.7)
ims = ax1.imshow(sd,
vmin=sdmin,
vmax=sdmax,
origin='lower',
cmap=cm.gray,
interpolation=None,
extent=[xmin, xmax, ymin, ymax],
aspect='auto',
alpha=1)
ims = ax2.imshow(se,
vmin=semin,
vmax=semax,
origin='lower',
cmap=cm.gray,
interpolation=None,
extent=[xmin, xmax, ymin, ymax],
aspect='auto',
alpha=1)
#percentiles
xbin_midd, y50d, y16d, y84d = dm.percentile_bins(xd, yd, xmin, xmax)
md = (y50d > 0) & (y16d > 0) & (y84d > 0)
xbin_mide, y50e, y16e, y84e = dm.percentile_bins(xe, ye, xmin, xmax)
me = (y50e > 0) & (y16e > 0) & (y84e > 0)
ax1.plot(xbin_midd[md], y50d[md], 'r-')
ax1.plot(xbin_midd[md], y16d[md], 'r--')
ax1.plot(xbin_midd[md], y84d[md], 'r--')
ax2.plot(xbin_mide[me], y50e[me], 'r-')
ax2.plot(xbin_mide[me], y16e[me], 'r--')
ax2.plot(xbin_mide[me], y84e[me], 'r--')
#dots
if scatters:
ax1.scatter(xd, yd, c='b', s=10, marker='h', label='Late-type')
ax2.scatter(xe, ye, c='r', s=10, marker='o', label='Early-type')
#hlines
if mean:
#mean size
mean_disk = N.mean(yd)
mean_early = N.mean(ye)
ax1.axhline(mean_disk, c='b', label='Mean')
ax2.axhline(mean_early, c='r', label='Mean')
#add text
P.text(0.5,
0.95,
'Late-type galaxies',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
P.text(0.5,
0.95,
'Early-type galaxies',
horizontalalignment='center',
verticalalignment='center',
transform=ax2.transAxes)
#labels
ax1.set_xlabel(xlabel)
ax2.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
ax2.set_yticks([])
#limits
ax1.set_ylim(ymin, ymax)
ax2.set_ylim(ymin, ymax)
ax1.set_xlim(xmin, xmax)
ax2.set_xlim(xmin, xmax)
#yticks
ax1.set_yticks(y1ticks)
ax2.set_yticks(y2ticks)
#make grid
#ax1.grid()
#ax2.grid()
#write a legend
ax1.legend(shadow=True,
fancybox=True,
numpoints=1,
loc='upper left',
scatterpoints=1)
ax2.legend(shadow=True,
fancybox=True,
numpoints=1,
loc='upper left',
scatterpoints=1)
P.savefig(out_folder + output)
def plot_size2(query,
xlabel,
ylabel,
output,
out_folder,
bulge=0.4,
title='$2.0 < z < 4.0$',
pmin=0.05,
pmax=1.0,
xbin=15,
ybin=15,
xmin=7.9,
xmax=11.7,
ymin=0.1,
ymax=10):
'''
Plots size versus a given quantity.
'''
#get data
data = N.array(sq.get_data_sqlitePowerTen(path, db, query))
# disks
disks = data[:, 2] <= bulge
xd = data[:, 0][disks]
yd = data[:, 1][disks]
#hess
sd, sdmin, sdmax = h.hess_plot(xd,
yd,
N.ones(len(xd)),
xmin,
xmax,
xbin,
ymin,
ymax,
ybin,
pmax=pmax,
pmin=pmin)
#figure
fig = P.figure()
fig.suptitle(title)
ax1 = fig.add_subplot(111)
ims = ax1.imshow(sd,
vmin=sdmin,
vmax=sdmax,
origin='lower',
cmap=cm.gray,
interpolation=None,
extent=[xmin, xmax, ymin, ymax],
aspect='auto',
alpha=1)
#percentiles
xbin_midd, y50d, y16d, y84d = dm.percentile_bins(xd, yd, xmin, xmax)
md = (y50d > 0) & (y16d > 0) & (y84d > 0)
ax1.plot(xbin_midd[md], y50d[md], 'r-')
ax1.plot(xbin_midd[md], y16d[md], 'r--')
ax1.plot(xbin_midd[md], y84d[md], 'r--')
#add text
P.text(0.5,
0.95,
'Late-type galaxies',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#limits
ax1.set_ylim(ymin, ymax)
ax1.set_xlim(xmin, xmax)
#yticks
#ax1.set_yticks(y1ticks)
#make grid
#ax1.grid()
#write a legend
ax1.legend(shadow=True,
fancybox=True,
numpoints=1,
loc='upper left',
scatterpoints=1)
P.savefig(out_folder + output)
def stellarHaloMFRatio(path, database, redshifts,
output_folder, outfile,
xmin=10.0, xmax=14.4,
ymin=-3, ymax=-1,
nbins=15, h=0.7):
'''
Plots stellar to dark matter halo mass ratios as a function of redshift.
'''
#get Behroozi et al. fits
BFits = g.glob('/Users/niemi/Dropbox/Research/Observations/behroozi/results/*.dat')
#get a colour scheme
cols = pt.give_colours()
#scale the SAM's output masses with this factor
multiply = 1e9
#make the figure and axes instance
fig = P.figure()
ax = fig.add_subplot(111)
#plot the different redshifts
for ii, redshift in enumerate(redshifts):
#get redshift, add 0.1 so that int/floor returns the closest int
tmp = redshift.split()
rd = int(float(tmp[2]) + 0.1)
#generate the SQL query
query = '''select mstar_disk, mbulge, mhalo from galpropz where ''' + redshift
query += ' and mstar_disk >= 0.0 and mbulge >= 0.0'
query += ' and mstar_disk + mbulge > 0.0'
query += ' and gal_id = 1'
#load Behroozi et al fit data
BF = N.loadtxt(BFits[ii + 1])
ax.plot(BF[:, 0], # - N.log10(h),
BF[:, 1],
color=cols[ii],
ls='--',
label='Behroozi et al.: $z \sim %s$' % (BFits[ii + 1].split('z')[2][:4]))
logging.debug(BFits[ii + 1])
#get data from the SQLite3 db
dat = db.sqlite.get_data_sqlite(path, database, query)
#rename data and calculate the ratio
mstar = dat[:, 0] + dat[:, 1]
mhalo = dat[:, 2]
ratio = N.log10(mstar / mhalo)
mhalo = N.log10(mhalo * multiply)
#print ratio, N.min(ratio), N.max(ratio)
#debug output
ngal = len(mstar)
logging.debug('%i galaxies found at z = %i\n' % (ngal, rd))
#bin the data
xbin_mid, y50, y16, y84 = dm.percentile_bins(mhalo,
ratio,
xmin,
xmax,
nxbins=nbins)
msk = y50 > -10
#plot the binned data
# ax.errorbar(xbin_mid[msk],
# y50[msk],
# yerr = [y50[msk]-y16[msk], y84[msk]-y50[msk]],
# color = cols[ii],
# label = 'Bolshoi+SAM: $z \sim %i$' % rd)
ax.plot(xbin_mid[msk],
y50[msk],
color=cols[ii],
ms='o',
ls='-',
label='Bolshoi+SAM: $z \sim %i$' % rd)
#set axes scales and labels
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
ax.set_xlabel(r'$\log_{10} \left ( M_{\mathrm{dm}} \ [\mathrm{M}_{\odot}] \right )$')
ax.set_ylabel(r'$\log_{10} \left (\frac{M_{\star}}{M_{\mathrm{dm}}} \right )$')
#set small ticks
m = ax.get_yticks()[1] - ax.get_yticks()[0]
yminorLocator = MultipleLocator(m / 5.)
yminorFormattor = NullFormatter()
ax.yaxis.set_minor_locator(yminorLocator)
ax.yaxis.set_minor_formatter(yminorFormattor)
m = ax.get_xticks()[1] - ax.get_xticks()[0]
xminorLocator = MultipleLocator(m / 5.)
xminorFormattor = NullFormatter()
ax.xaxis.set_minor_locator(xminorLocator)
ax.xaxis.set_minor_formatter(xminorFormattor)
P.legend(shadow=True, fancybox=True, numpoints=1)
P.savefig(output_folder + outfile + '.pdf')
P.close()
def stellarHaloMFRatioMultiPanel(path, database, redshifts,
output_folder, outfile,
xmin=10.0, xmax=14.4,
ymin=-3, ymax=-1,
nbins=15):
'''
Plots stellar to dark matter halo mass ratios as a function of redshift.
'''
#get Behroozi et al. fits
BFits = g.glob(hm + '/Dropbox/Research/Observations/behroozi/results/*.dat')
#get a colour scheme
cols = pt.give_colours()
#scale the SAM's output masses with this factor
multiply = 1e9
#make the figure and axes instance
fig = P.figure()
P.subplots_adjust(left=0.13,
bottom=0.1,
wspace=0.0,
hspace=0.0)
#plot the different redshifts
for ii, redshift in enumerate(redshifts):
#get redshift, add 0.1 so that int/floor returns the closest int
tmp = redshift.split()
rd = int(float(tmp[2]) + 0.1)
#generate the SQL query
query = '''select mstar_disk, mbulge, mhalo from galpropz where ''' + redshift
query += ' and mstar_disk >= 0.0 and mbulge >= 0.0'
query += ' and mstar_disk + mbulge > 0.0'
query += ' and gal_id = 1'
if ii < 6:
ax = fig.add_subplot(2, 3, ii + 1)
#get data from the SQLite3 db
dat = db.sqlite.get_data_sqlite(path, database, query)
#rename data and calculate the ratio
mstar = dat[:, 0] + dat[:, 1]
mhalo = dat[:, 2]
ratio = N.log10(mstar / mhalo)
mhalo = N.log10(mhalo * multiply)
#print ratio, N.min(ratio), N.max(ratio)
#debug output
ngal = len(mstar)
logging.debug('%i galaxies found at z = %i\n' % (ngal, rd))
#bin the data
xbin_mid, y50, y16, y84 = dm.percentile_bins(mhalo,
ratio,
xmin,
xmax,
nxbins=nbins)
msk = y50 > -10
#plot the binned data
if ii == 6:
ax.plot(xbin_mid[msk],
y50[msk],
'k--',
label='Bolshoi+SAM')
else:
ax.errorbar(xbin_mid[msk],
y50[msk],
yerr=[y50[msk] - y16[msk], y84[msk] - y50[msk]],
color='k',
label='Bolshoi+SAM')
#load Behroozi et al fit data
BF = N.loadtxt(BFits[ii + 1])
#plot Behroozi data
if ii == 6:
ax.plot(BF[:, 0],
BF[:, 1],
color='r',
ls='--',
label='Behroozi et al.')
else:
ax.plot(BF[:, 0],
BF[:, 1],
color='r',
ls='-',
label='Behroozi et al.')
#output some information
logging.debug(BFits[ii + 1])
#mark the redshift
if ii < 5:
P.text(0.5, 0.93,
r'$ z \sim %.1f $' % (rd),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=12)
elif ii == 5:
P.text(0.5, 0.93,
r'$z \sim %.1f (s)\ \& \ %.1f (d)$' % (rd, rd + 1),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=12)
#set axes scales and labels
ax.set_xlim(xmin - 0.1, xmax)
ax.set_ylim(ymin, ymax)
#set small ticks
m = ax.get_yticks()[1] - ax.get_yticks()[0]
yminorLocator = MultipleLocator(m / 5.)
yminorFormattor = NullFormatter()
ax.yaxis.set_minor_locator(yminorLocator)
ax.yaxis.set_minor_formatter(yminorFormattor)
m = ax.get_xticks()[1] - ax.get_xticks()[0]
xminorLocator = MultipleLocator(m / 5.)
xminorFormattor = NullFormatter()
ax.xaxis.set_minor_locator(xminorLocator)
ax.xaxis.set_minor_formatter(xminorFormattor)
#set legend
if ii == 4:
ax.legend(shadow=True,
fancybox=True,
numpoints=1,
loc='lower right')
#set ylabel
if ii == 0 or ii == 3:
ax.set_ylabel(r'$\log_{10} \left (\frac{M_{\star}}{M_{\mathrm{dm}}} \right )$')
#set xlabel
if ii == 4: # or ii == 3 or ii == 5:
ax.set_xlabel(r'$\log_{10} \left ( M_{\mathrm{dm}} \ [\mathrm{M}_{\odot}] \right )$')
#remove some y ticks
if ii == 0 or ii == 3:
ax.set_yticks(ax.get_yticks()[:-1])
else:
ax.set_yticklabels([])
#remove some x ticks
if ii == 3 or ii == 4 or ii == 5:
#ax.set_xticks(ax.get_xticks()[:-1])
continue
elif ii == 6:
continue
else:
ax.set_xticklabels([])
P.savefig(output_folder + outfile + 'Multi.pdf')
P.close()
def plot_ages(query, xlabel, ylabel, output, out_folder,
flux = 5, band = 250, title = '$2.0 < z < 4.0$',
pmin = 0.05, pmax = 1.0,
xbin = 10, ybin = 15,
xmin = 7.9, xmax = 11.7, ymin = -1.5, ymax = 1.8,
scatters = False, mean = False):
'''
Plots age versus a given quantity.
'''
#get data
data = N.array(sq.get_data_sqlitePowerTen(path, db, query))
#observable galaxies
mask = data[:,1] > flux
xe = data[:,0][mask]
ye = N.log10(data[:,1][mask])
#all galaxies
xd = data[:,0]
yd = N.log10(data[:,1])
mask = yd != N.nan
xd = xd[mask]
yd = yd[mask]
#hess
sd, sdmin, sdmax = h.hess_plot(xd, yd, N.ones(len(xd)),
xmin, xmax, xbin,
ymin, ymax, ybin,
pmax = pmax, pmin = pmin)
se, semin, semax = h.hess_plot(xe, ye, N.ones(len(xe)),
xmin, xmax, xbin,
N.log10(flux), ymax, ybin,
pmax = pmax, pmin = pmin)
#figure
fig = P.figure()
fig.suptitle(title)
f, (ax1, ax2) = P.subplots(1, 2, sharey=True)
f.subplots_adjust(wspace = 0.0, hspace = 0.01, left = 0.08, bottom = 0.07,
right = 0.97, top = 0.93)
#contours
ims = ax1.imshow(sd, vmin = sdmin, vmax = sdmax,
origin = 'lower', cmap = cm.gray,
interpolation = None,
extent = [xmin, xmax, ymin, ymax],
aspect = 'auto', alpha = 1)
ims = ax2.imshow(se, vmin = semin, vmax = semax,
origin = 'lower', cmap = cm.gray,
interpolation = None,
extent = [xmin, xmax, N.log10(flux), ymax],
aspect = 'auto', alpha = 1)
#percentiles
xbin_midd, y50d, y16d, y84d = dm.percentile_bins(xd, yd, xmin, xmax)
md = (y50d > 0) & (y16d > 0) & (y84d > 0)
xbin_mide, y50e, y16e, y84e = dm.percentile_bins(xe, ye, xmin, xmax)
me = (y50e > 0) & (y16e > 0) & (y84e > 0)
ax1.plot(xbin_midd[md], y50d[md], 'r-')
ax1.plot(xbin_midd[md], y16d[md], 'r--')
ax1.plot(xbin_midd[md], y84d[md], 'r--')
ax2.plot(xbin_mide[me], y50e[me], 'r-')
ax2.plot(xbin_mide[me], y16e[me], 'r--')
ax2.plot(xbin_mide[me], y84e[me], 'r--')
#dots
if scatters:
ax1.scatter(xd, yd, c='b', s = 10,
marker = 'h', label = 'All galaxies')
ax2.scatter(xe, ye, c='r', s = 10,
marker = 'o', label = r'$S_{%i} > %.1f$ mJy' % (band, flux))
#hlines
if mean:
#mean size
mean_disk = N.mean(yd)
mean_early = N.mean(ye)
ax1.axhline(mean_disk, c = 'b', label = 'Mean')
ax2.axhline(mean_early, c = 'r', label = 'Mean')
#add text
P.text(0.5, 0.95, 'All galaxies',
horizontalalignment='center',
verticalalignment='center',
transform = ax1.transAxes)
P.text(0.5, 0.95, r'$S_{%i} > %.1f$ mJy' % (band, flux),
horizontalalignment='center',
verticalalignment='center',
transform = ax2.transAxes)
#labels
ax1.set_xlabel(xlabel)
ax2.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#ax2.set_yticks([])
#limits
#ax1.set_ylim(ymin, ymax)
#ax2.set_ylim(ymin, ymax)
ax1.set_xlim(xmin, xmax)
ax2.set_xlim(xmin, xmax)
#yticks
#ax1.set_yticks(y1ticks)
#ax2.set_yticks(y2ticks)
#make grid
ax1.grid()
ax2.grid()
#write a legend
ax1.legend(shadow = True, fancybox = True,
numpoints = 1, loc = 'upper left',
scatterpoints = 1)
ax2.legend(shadow = True, fancybox = True,
numpoints = 1, loc = 'upper left',
scatterpoints = 1)
P.savefig(out_folder + output)