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 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 )$')
#remove some y ticks
if ii == 0 or ii == 3:
ax.set_yticks(ax.get_yticks()[:-1])
else:
ax.set_yticklabels([])
#remove some x ticks
if ii == 3 or ii == 4 or ii == 5:
#ax.set_xticks(ax.get_xticks()[:-1])
continue
elif ii == 6:
continue
else:
ax.set_xticklabels([])
P.savefig(output_folder + outfile + 'Multi.pdf')
P.close()
def stellarmassfunc_plot(path,
database,
redshifts,
output_folder,
outfile,
mmax=12.5,
mmin=8.0,
nbins=30,
nvolumes=15,
lowlim=-4.9):
'''
Plots stellar mass functions as a function of redshift.
Compares to observations.
'''
#get a colour scheme
cols = pt.give_colours()
#scale the SAM's output masses
multiply = 1e9
#make the figure and axes instance
fig = P.figure()
ax = fig.add_subplot(111)
#get obs constrains
obs, ids = SMF.stellarMfs()
o = []
o.append([
'$z \sim 1$: Perez-Gonzalez et al. 2007',
(obs.id == 1) & (obs.z_low > 0.99) & (obs.z_up < 1.4)
])
#o.append(['$z \sim 1$: Drory et al. 2004', (obs.id == 5) & (obs.z_low > 1.) & (obs.z_up < 1.2)])
o.append([
'$z \sim 2$: Perez-Gonzalez et al. 2007',
(obs.id == 1) & (obs.z_low > 1.99) & (obs.z_up < 2.6)
])
#o.append(['$z \sim 2$: Fontana et al. 2006', (obs.id == 6) & (obs.z_low > 1.99) & (obs.z_up < 3.01)])
#o.append(['$z \sim 2$: Marchesini et al. 2008', (obs.id == 7) & (obs.z_low > 1.99) & (obs.z_up < 3.01)])
o.append([
'$z \sim 3$: Perez-Gonzalez et al. 2007',
(obs.id == 1) & (obs.z_low > 2.99) & (obs.z_up < 3.6)
])
o.append([
'$z \sim 4$: Perez-Gonzalez et al. 2007',
(obs.id == 1) & (obs.z_low > 3.49) & (obs.z_up < 4.1)
])
highred = SMF.highRedshiftMFs()
#make the observational plots
for i, line in enumerate(o):
label, mask = line[0], line[1]
ms = obs.mstar[mask]
mf = obs.mf[mask]
errl = obs.err_low[mask]
errh = obs.err_up[mask]
msk = mf > -15.0
ax.errorbar(ms[msk],
mf[msk],
yerr=[errh[msk], errl[msk]],
color=cols[i],
ls=':',
label=label)
for i, key in enumerate(sorted(highred.iterkeys())):
if key != 'stellar_mass':
ax.plot(highred['stellar_mass'],
highred[key],
color=cols[i + 2],
ls=':',
marker='s',
label='$%s:$ Gonzalez et al. 2011' %
key.replace('=', '\sim'))
#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, gal_id from galpropz where ''' + redshift
query += ' and (mstar_disk >= 0.0 or mbulge >= 0.0)'
query += ' and mstar_disk + mbulge > 0.0'
#get data from the SQLite3 db
dat = db.sqlite.get_data_sqlite(path, database, query)
#rename data for convenience
disk = dat[:, 0]
bulge = dat[:, 1]
mstar = N.log10((disk * multiply) + (bulge * multiply))
#make a dictionary of data
data = {}
data['stellar_mass'] = mstar
data['bulge_mass'] = bulge
data['galaxy_id'] = dat[:, 2]
#debug output
ngal = len(mstar)
logging.debug('%i galaxies found at z = %i' % (ngal, rd))
#calculate the stellar mass functions
mfs = df.stellarMassFunction(
data,
mmin=mmin - 0.2,
#mmax = mmax,
nvols=nvolumes,
nbins=nbins - 2 * rd,
verbose=True)
#plot the simulation data
ax.plot(mfs['mass_bins'],
mfs['mf_stellar_mass'],
color=cols[ii],
label='$z \sim %i$: Bolshoi + SAM' % rd)
# ax.plot(mfs['mass_bins'],
# mfs['mf_central_galaxies'],
# color = cols[ii],
# ls = '--',
# label = '$z \sim %i$: Bolshoi + SAM (CG)' % rd)
#set axes scales and labels
ax.set_xlim(mmin, 12.0)
ax.set_ylim(lowlim, -1.0)
ax.set_xlabel(
r'$\log_{10} \left ( M_{\star} \ [\mathrm{M}_{\odot}] \right )$')
ax.set_ylabel(
r'$\log_{10} \left ( \frac{\mathrm{d}N}{\mathrm{d}\log_{10} M_{\star}} \right ) \quad [\mathrm{Mpc}^{-3}\ \mathrm{dex}^{-1}] $'
)
#set small ticks
m = ax.get_yticks()[1] - ax.get_yticks()[0]
yminorLocator = MultipleLocator(m / 5)
yminorFormattor = NullFormatter()
ax.yaxis.set_minor_locator(yminorLocator)
ax.yaxis.set_minor_formatter(yminorFormattor)
m = ax.get_xticks()[1] - ax.get_xticks()[0]
xminorLocator = MultipleLocator(m / 5)
xminorFormattor = NullFormatter()
ax.xaxis.set_minor_locator(xminorLocator)
ax.xaxis.set_minor_formatter(xminorFormattor)
P.legend(shadow=True, fancybox=True, numpoints=1)
P.savefig(output_folder + outfile + '.pdf')
P.close()
def stellarmassfunc_plot(path, database, redshifts,
output_folder, outfile,
mmax=12.5, mmin=8.0,
nbins=30, nvolumes=15,
lowlim=-4.9):
'''
Plots stellar mass functions as a function of redshift.
Compares to observations.
'''
#get a colour scheme
cols = pt.give_colours()
#scale the SAM's output masses
multiply = 1e9
#make the figure and axes instance
fig = P.figure()
ax = fig.add_subplot(111)
#get obs constrains
obs, ids = SMF.stellarMfs()
o = []
o.append(['$z \sim 1$: Perez-Gonzalez et al. 2007', (obs.id == 1) & (obs.z_low > 0.99) & (obs.z_up < 1.4)])
#o.append(['$z \sim 1$: Drory et al. 2004', (obs.id == 5) & (obs.z_low > 1.) & (obs.z_up < 1.2)])
o.append(['$z \sim 2$: Perez-Gonzalez et al. 2007', (obs.id == 1) & (obs.z_low > 1.99) & (obs.z_up < 2.6)])
#o.append(['$z \sim 2$: Fontana et al. 2006', (obs.id == 6) & (obs.z_low > 1.99) & (obs.z_up < 3.01)])
#o.append(['$z \sim 2$: Marchesini et al. 2008', (obs.id == 7) & (obs.z_low > 1.99) & (obs.z_up < 3.01)])
o.append(['$z \sim 3$: Perez-Gonzalez et al. 2007', (obs.id == 1) & (obs.z_low > 2.99) & (obs.z_up < 3.6)])
o.append(['$z \sim 4$: Perez-Gonzalez et al. 2007', (obs.id == 1) & (obs.z_low > 3.49) & (obs.z_up < 4.1)])
highred = SMF.highRedshiftMFs()
#make the observational plots
for i, line in enumerate(o):
label, mask = line[0], line[1]
ms = obs.mstar[mask]
mf = obs.mf[mask]
errl = obs.err_low[mask]
errh = obs.err_up[mask]
msk = mf > - 15.0
ax.errorbar(ms[msk],
mf[msk],
yerr=[errh[msk], errl[msk]],
color=cols[i],
ls=':',
label=label)
for i, key in enumerate(sorted(highred.iterkeys())):
if key != 'stellar_mass':
ax.plot(highred['stellar_mass'],
highred[key],
color=cols[i + 2],
ls=':',
marker='s',
label='$%s:$ Gonzalez et al. 2011' % key.replace('=', '\sim'))
#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, gal_id from galpropz where ''' + redshift
query += ' and (mstar_disk >= 0.0 or mbulge >= 0.0)'
query += ' and mstar_disk + mbulge > 0.0'
#get data from the SQLite3 db
dat = db.sqlite.get_data_sqlite(path, database, query)
#rename data for convenience
disk = dat[:, 0]
bulge = dat[:, 1]
mstar = N.log10((disk * multiply) + (bulge * multiply))
#make a dictionary of data
data = {}
data['stellar_mass'] = mstar
data['bulge_mass'] = bulge
data['galaxy_id'] = dat[:, 2]
#debug output
ngal = len(mstar)
logging.debug('%i galaxies found at z = %i' % (ngal, rd))
#calculate the stellar mass functions
mfs = df.stellarMassFunction(data,
mmin=mmin - 0.2,
#mmax = mmax,
nvols=nvolumes,
nbins=nbins - 2 * rd,
verbose=True)
#plot the simulation data
ax.plot(mfs['mass_bins'],
mfs['mf_stellar_mass'],
color=cols[ii],
label='$z \sim %i$: Bolshoi + SAM' % rd)
# ax.plot(mfs['mass_bins'],
# mfs['mf_central_galaxies'],
# color = cols[ii],
# ls = '--',
# label = '$z \sim %i$: Bolshoi + SAM (CG)' % rd)
#set axes scales and labels
ax.set_xlim(mmin, 12.0)
ax.set_ylim(lowlim, -1.0)
ax.set_xlabel(r'$\log_{10} \left ( M_{\star} \ [\mathrm{M}_{\odot}] \right )$')
ax.set_ylabel(
r'$\log_{10} \left ( \frac{\mathrm{d}N}{\mathrm{d}\log_{10} M_{\star}} \right ) \quad [\mathrm{Mpc}^{-3}\ \mathrm{dex}^{-1}] $')
#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()