def gasFraction(path, db, label='Bolshoi', output='fgascentral', tpy='.pdf'):
#get data
query = '''select gal_id, halo_id, mbulge, mstar, mcold, mbh from galprop'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
gal_id = data[:, 0]
halo_id = data[:, 1].astype(N.int)
mbulge = data[:, 2]
mstar = data[:, 3]
mcold = data[:, 4]
#get the weights from another table
query = '''select weight from halos'''
d = sq.get_data_sqliteSMNfunctions(path, db, query)
weight = d[:, 0]
fgas = 10.0**mcold / (10.0**mcold + 10.0**mstar)
wgal = weight[halo_id]
btt = 10.0**(mbulge - mstar)
#masking
cdisk = (gal_id == 1) & (btt < 0.4)
#output file
output = output + label + tpy
#call the gas
gasfracHess(mstar[cdisk], fgas[cdisk], wgal[cdisk], label, output)
def mhb(path, db, type='.pdf'):
#get data
query = '''select halo_id, mstar, mbulge, mbh from galprop'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
halo_id = data[:, 0].astype(N.int)
mstar = data[:, 1]
mbulge = data[:, 2]
mbh = data[:, 3]
#get the weights from another table
query = '''select weight from halos'''
d = sq.get_data_sqliteSMNfunctions(path, db, query)
weight = d[:, 0]
wgal = weight[halo_id]
#fitting functions of ??
mbin_fit = 8.0 + N.arange(40) * 0.1
mbh_fit = 8.2 + 1.12 * (mbin_fit - 11.0)
#central = p.gal_id == 1
btt = 10.0 ** (mbulge - mstar)
#These could be changed to 0.4 and 0.6...
early = btt >= 0.5
late = btt <= 0.5
print 'Number of galaxies %i, early types %i and late %i' % (len(mstar), len(mstar[early]), len(mstar[late]))
#mbin = 8.0 + ((11.8 - 8.0) / 19.0) * N.arange(19)
#mbh_hr = 8.2 + 1.12 * (p.mbulge - 11.0)
output = 'mbhBolshoi' + type
mbhHess(mbulge, mbh, wgal, 4.85, 10.0, 40,
mbin_fit, mbh_fit, output)
def gasFraction(path, db,
label='Bolshoi',
output='fgascentral',
tpy='.pdf'):
#get data
query = '''select gal_id, halo_id, mbulge, mstar, mcold, mbh from galprop'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
gal_id = data[:, 0]
halo_id = data[:, 1].astype(N.int)
mbulge = data[:, 2]
mstar = data[:, 3]
mcold = data[:, 4]
#get the weights from another table
query = '''select weight from halos'''
d = sq.get_data_sqliteSMNfunctions(path, db, query)
weight = d[:, 0]
fgas = 10.0 ** mcold / (10.0 ** mcold + 10.0 ** mstar)
wgal = weight[halo_id]
btt = 10.0 ** (mbulge - mstar)
#masking
cdisk = (gal_id == 1) & (btt < 0.4)
#output file
output = output + label + tpy
#call the gas
gasfracHess(mstar[cdisk], fgas[cdisk], wgal[cdisk], label, output)
def mhb(path, db, type='.pdf'):
#get data
query = '''select halo_id, mstar, mbulge, mbh from galprop'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
halo_id = data[:, 0].astype(N.int)
mstar = data[:, 1]
mbulge = data[:, 2]
mbh = data[:, 3]
#get the weights from another table
query = '''select weight from halos'''
d = sq.get_data_sqliteSMNfunctions(path, db, query)
weight = d[:, 0]
wgal = weight[halo_id]
#fitting functions of ??
mbin_fit = 8.0 + N.arange(40) * 0.1
mbh_fit = 8.2 + 1.12 * (mbin_fit - 11.0)
#central = p.gal_id == 1
btt = 10.0**(mbulge - mstar)
#These could be changed to 0.4 and 0.6...
early = btt >= 0.5
late = btt <= 0.5
print 'Number of galaxies %i, early types %i and late %i' % (
len(mstar), len(mstar[early]), len(mstar[late]))
#mbin = 8.0 + ((11.8 - 8.0) / 19.0) * N.arange(19)
#mbh_hr = 8.2 + 1.12 * (p.mbulge - 11.0)
output = 'mbhBolshoi' + type
mbhHess(mbulge, mbh, wgal, 4.85, 10.0, 40, mbin_fit, mbh_fit, output)
def ssfrWrapper(path, db):
#get data
query = '''select gal_id, mbulge, mstar from galprop'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
gal_id = data[:, 0]
mbulge = data[:, 1]
mstar = data[:, 2]
#make masks for all, central, and satellite galaxies
all = gal_id >= 0
c = gal_id == 1
s = gal_id != 1
#early vs. late type galaxies
btt = 10.0 ** (mbulge - mstar)
early = btt >= 0.4
late = btt < 0.4
label = 'ssfrBolshoi'
ssfr(path, db, all, label)
ssfr(path, db, c, label + '_cent')
ssfr(path, db, s, label + '_sat')
ssfr(path, db, early, label + '_sph')
ssfr(path, db, late, label + '_disk')
def plotColourProperties(query,
xlabel,
ylabel,
output,
out_folder,
ymin=-12,
ymax=2,
xmin=-6,
xmax=3.2,
title='',
clabel='$\mathrm{Redshift}$'):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x1 = data[:, 0]
x2 = co.ABMagnitudeToJansky(data[:, 1])
x = N.log10(x1 / x2)
y = N.log10(data[:, 2] * 1e3)
z = data[:, 3]
#make the figure
# fig = P.figure()
fig = P.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.09, bottom=0.08, right=0.92, top=0.94)
ax1 = fig.add_subplot(111)
#plot scatters
s1 = ax1.scatter(x,
y,
s=0.5,
marker='o',
c=z,
cmap=cm.get_cmap('jet'),
alpha=0.25,
edgecolor='none')
s1 = ax1.scatter(x,
y,
s=0.1,
marker='o',
c=z,
cmap=cm.get_cmap('jet'),
edgecolor='none',
visible=False)
c1 = fig.colorbar(s1, ax=ax1, shrink=0.7, fraction=0.05, pad=0.01)
c1.set_label(clabel)
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#limits
ax1.set_ylim(ymin, ymax)
ax1.set_xlim(xmin, xmax)
#add annotate
P.text(0.5,
1.03,
title,
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#make grid
ax1.grid()
#legend and save
#P.legend(loc = 'upper left', scatterpoints = 1)
P.savefig(out_folder + output)
def ssfrWrapper(path, db):
#get data
query = '''select gal_id, mbulge, mstar from galprop'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
gal_id = data[:, 0]
mbulge = data[:, 1]
mstar = data[:, 2]
#make masks for all, central, and satellite galaxies
all = gal_id >= 0
c = gal_id == 1
s = gal_id != 1
#early vs. late type galaxies
btt = 10.0**(mbulge - mstar)
early = btt >= 0.4
late = btt < 0.4
label = 'ssfrBolshoi'
ssfr(path, db, all, label)
ssfr(path, db, c, label + '_cent')
ssfr(path, db, s, label + '_sat')
ssfr(path, db, early, label + '_sph')
ssfr(path, db, late, label + '_disk')
def massMet(path, db, typ='.pdf'):
#get data
query = '''select halo_id, mstar, zstar from galprop where zstar > -50 and zstar < 50'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
halo_id = data[:, 0].astype(N.int)
mstar = data[:, 1]
zstar = N.log10(data[:, 2])
#get the weights from another table
query = '''select weight from halos'''
d = sq.get_data_sqliteSMNfunctions(path, db, query)
weight = d[:, 0]
wgal = weight[halo_id]
output = 'massMetStarBolshoi' + typ
#mbar = (10.0 ** mcold + 10.0 ** mstar)
#fg = 10.0 ** p.mcold / mbar
#massmet_hess
massMetHess(mstar, zstar, wgal, output)
def massMet(path, db, typ='.pdf'):
#get data
query = '''select halo_id, mstar, zstar from galprop where zstar > -50 and zstar < 50'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
halo_id = data[:, 0].astype(N.int)
mstar = data[:, 1]
zstar = N.log10(data[:, 2])
#get the weights from another table
query = '''select weight from halos'''
d = sq.get_data_sqliteSMNfunctions(path, db, query)
weight = d[:, 0]
wgal = weight[halo_id]
output = 'massMetStarBolshoi' + typ
#mbar = (10.0 ** mcold + 10.0 ** mstar)
#fg = 10.0 ** p.mcold / mbar
#massmet_hess
massMetHess(mstar, zstar, wgal, output)
def ssfr(path, db, mask, output, typ='.pdf'):
'''
Specific star formation rate plots.
'''
#get data
query = '''select halo_id, mstar, sfr_ave from galprop'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
halo_id = data[:, 0].astype(N.int)
mstar = data[:, 1]
sfr_ave = data[:, 2]
#get the weights from another table
query = '''select weight from halos'''
d = sq.get_data_sqliteSMNfunctions(path, db, query)
weight = d[:, 0]
#set weighting
wgal = weight[halo_id]
log_ssfr = N.log10(sfr_ave / 10.0 ** mstar)
log_ssfr[log_ssfr <= -14.0] = -13.8
ssfrHess(mstar[mask], log_ssfr[mask], wgal[mask], output + typ)
def ssfr(path, db, mask, output, typ='.pdf'):
'''
Specific star formation rate plots.
'''
#get data
query = '''select halo_id, mstar, sfr_ave from galprop'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
halo_id = data[:, 0].astype(N.int)
mstar = data[:, 1]
sfr_ave = data[:, 2]
#get the weights from another table
query = '''select weight from halos'''
d = sq.get_data_sqliteSMNfunctions(path, db, query)
weight = d[:, 0]
#set weighting
wgal = weight[halo_id]
log_ssfr = N.log10(sfr_ave / 10.0**mstar)
log_ssfr[log_ssfr <= -14.0] = -13.8
ssfrHess(mstar[mask], log_ssfr[mask], wgal[mask], output + typ)
def plotSFRFractions(query,
xlabel,
ylabel,
output,
out_folder,
xmin=8.0,
xmax=11.5,
xbins=15,
ymin=-0.01,
ymax=1.01,
logscale=False):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x = data[:, 0]
mstar = data[:, 1]
burst = data[:, 2]
mids, fract = dm.binAndReturnFractions(x,
burst,
mstar,
xmin,
xmax,
xbins,
logscale=False)
print mids, fract
#make the figure
# fig = P.figure()
fig = P.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.08, bottom=0.07, right=0.97, top=0.93)
ax1 = fig.add_subplot(111)
#draw lines
ax1.plot(mids, fract, 'k-', lw=2.6, label='In Bursts')
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#limits
ax1.set_ylim(ymin, ymax)
#add annotate
P.text(0.5,
0.93,
'All galaxies\n$2 \leq z < 4$',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#make grid
ax1.grid()
#legend and save
P.legend(loc='upper right')
P.savefig(out_folder + output)
def plotSFRFractions(query,
xlabel, ylabel,
output, out_folder,
xmin = 8.0,
xmax = 11.5,
xbins = 15,
ymin = -0.01,
ymax = 1.01,
logscale = False):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x = data[:,0]
mstar = data[:,1]
burst = data[:,2]
mids, fract = dm.binAndReturnFractions(x,
burst,
mstar,
xmin,
xmax,
xbins,
logscale = False)
print mids, fract
#make the figure
# fig = P.figure()
fig = P.figure(figsize=(10,10))
fig.subplots_adjust(left = 0.08, bottom = 0.07,
right = 0.97, top = 0.93)
ax1 = fig.add_subplot(111)
#draw lines
ax1.plot(mids, fract, 'k-', lw = 2.6, label = 'In Bursts')
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#limits
ax1.set_ylim(ymin, ymax)
#add annotate
P.text(0.5, 0.93,'All galaxies\n$2 \leq z < 4$',
horizontalalignment='center',
verticalalignment='center',
transform = ax1.transAxes)
#make grid
ax1.grid()
#legend and save
P.legend(loc = 'upper right')
P.savefig(out_folder + output)
def plot_correlation(query, xlabel, ylabel, zmin, zmax, out_folder):
#get data
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x = data[:,0]
y = data[:,1]*10**3
#make the figure
P.figure()
P.title('$%.1f \leq z < %.1f$' % (zmin, zmax))
P.scatter(x, y, c = 'k', marker = 'o', s = 9)
#P.yscale('log')
# if 'meanage' in xlabel:
# P.xlim(0.0, 8.0)
# if 'mstar' in xlabel:
# P.xlim(7, 12)
# if 'mstardot' in xlabel:
# P.xlim(0, 800)
if 'sfr_ave' in xlabel:
P.xlim(0, 500)
if 'sfr_bur' in xlabel:
P.xlim(-1, 750)
if 'tmerge' in xlabel:
P.xlim(-0.1, 3)
if 'pacs70' in ylabel:
P.ylim(0, 3)
if 'pacs100' in ylabel:
P.ylim(0, 12)
if 'pacs160' in ylabel:
P.ylim(0, 37)
if 'spire250' in ylabel:
P.ylim(0, 50)
if 'spire350' in ylabel:
P.ylim(0, 45)
if 'spire500' in ylabel:
P.ylim(0, 26)
if 'tmajmerge' in xlabel:
P.xlim(0, 2)
if 'r_disk' in xlabel:
P.xlim(0, 23)
if 'sigma_bul' in xlabel:
P.xlim(0, 1000)
P.xlabel(xlabel.replace('_', '\_'))
P.ylabel(ylabel.replace('_', '\_'))
P.savefig(out_folder + '%s_%s.png' % (xlabel, ylabel[:-7]))
P.close()
print '\nSpearmann R-test (rho, P) for %s and %s:' % (xlabel, ylabel[:-7])
print SS.spearmanr(x, y)
def plotProperties(query,
xlabel,
ylabel,
output,
out_folder,
ymin=-12,
ymax=2,
xmin=-6,
xmax=3.2,
title='',
clabel='$\mathrm{Redshift}$'):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x = N.log10(data[:, 0])
y = N.log10(data[:, 1] * 1e3)
z = data[:, 2]
noMerge = z < 0.0
merged = z > 0.0
#make the figure
# fig = P.figure()
fig = P.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.09, bottom=0.08, right=0.92, top=0.94)
ax1 = fig.add_subplot(111)
#plot scatters
s1 = ax1.scatter(x[noMerge],
y[noMerge],
s=1,
marker='o',
c='black',
edgecolor='none',
label='Never Merged')
s1 = ax1.scatter(x[merged],
y[merged],
s=1,
marker='o',
c=z[merged],
cmap=cm.get_cmap('jet'),
alpha=0.1,
edgecolor='none',
label='Merged')
s1 = ax1.scatter(x[merged],
y[merged],
s=0.1,
marker='o',
c=z[merged],
cmap=cm.get_cmap('jet'),
edgecolor='none',
visible=False)
c1 = fig.colorbar(s1, ax=ax1, shrink=0.7, fraction=0.05, pad=0.01)
c1.set_label(clabel)
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#limits
ax1.set_ylim(ymin, ymax)
ax1.set_xlim(xmin, xmax)
#add annotate
P.text(0.5,
1.03,
title,
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#make grid
ax1.grid()
#legend and save
P.legend(loc='upper left',
scatterpoints=1,
markerscale=10,
shadow=True,
fancybox=True)
P.savefig(out_folder + output)
def plotColourFlux(query,
xlabel,
ylabel,
output,
out_folder,
ymin=-0.5,
ymax=2,
xmin=0.0,
xmax=60,
title='$S_{250} > 5 \ \mathrm{mJy}$',
clabel='$\mathrm{Redshift}$'):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x = data[:, 0] * 1e3
y = data[:, 1]
z = data[:, 2]
#make the figure
# fig = P.figure()
fig = P.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.09, bottom=0.08, right=0.92, top=0.94)
ax1 = fig.add_subplot(111)
#plot scatters
s1 = ax1.scatter(x,
y,
s=15,
marker='o',
c=z,
cmap=cm.get_cmap('jet'),
alpha=0.5,
edgecolor='none')
s1 = ax1.scatter(x,
y,
s=0.1,
marker='o',
c=z,
cmap=cm.get_cmap('jet'),
edgecolor='none',
visible=False)
c1 = fig.colorbar(s1, ax=ax1, shrink=0.7, fraction=0.05, pad=0.01)
# axins = zoomed_inset_axes(ax1, 3.2, loc=1) # zoom = 3.2
# axins.scatter(x, y, s = 20, marker = 'o',
# c=z, cmap = cm.get_cmap('jet'), alpha = 0.45,
# edgecolor = 'none')
# #
# ax1.axvline(5, linestyle = ':', color = 'green', lw = 2)
# # sub region of the original image
# x1, x2, y1, y2 = 4.8, 15.2, -0.2, 0.18
# axins.set_xlim(x1, x2)
# axins.set_ylim(y1, y2)
# mark_inset(ax1, axins, loc1=2, loc2=4, fc="none", ec="0.5")
# P.xticks(visible=False)
# P.yticks(visible=False)
c1.set_label(clabel)
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#limits
ax1.set_ylim(ymin, ymax)
ax1.set_xlim(xmin, xmax)
#add annotate
P.text(0.5,
1.03,
title,
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#make grid
ax1.grid()
#legend and save
#P.legend(loc = 'upper left', scatterpoints = 1)
P.savefig(out_folder + output)
def plotColourProperties4(query, output, out_folder):
#get data, all galaxies
#FIR.spire250_obs, FIR.irac_ch4_obs, galprop.mstardot,
#galprop.sfr_burst, galprop.sfr_ave, FIR.z, galprop.tmerge
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x1 = data[:, 0]
x2 = data[:, 1]
x = N.log10(x1 / x2)
sfr = data[:, 2]
burst = data[:, 3]
avera = data[:, 4]
z = data[:, 5]
tmerge = data[:, 6]
#masks
noMerge = tmerge < 0.0
recent = (tmerge < 0.25) & (tmerge > 0.0)
mergers = tmerge > 0.0
#make the figure
# fig = P.figure()
fig = P.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.085,
bottom=0.08,
right=0.99,
top=0.99,
wspace=0.21,
hspace=0.0)
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
#plot scatters
s1 = ax1.scatter(x[noMerge],
N.log10(sfr[noMerge]),
s=3.5,
marker='<',
c=z[noMerge],
cmap=cm.get_cmap('jet'),
alpha=0.15,
edgecolor='none',
label='Never Merged')
s1 = ax1.scatter(x[noMerge],
N.log10(sfr[noMerge]),
s=1.0,
marker='<',
c=z[noMerge],
cmap=cm.get_cmap('jet'),
edgecolor='none',
visible=False)
s2 = ax2.scatter(x[recent],
N.log10(sfr[recent]),
s=3.5,
marker='>',
c=z[recent],
cmap=cm.get_cmap('jet'),
alpha=0.15,
edgecolor='none',
label='Recent Mergers $T_{\mathrm{merge}} < 250$ Myr')
s2 = ax2.scatter(x[recent],
N.log10(sfr[recent]),
s=1.0,
marker='>',
c=z[recent],
cmap=cm.get_cmap('jet'),
edgecolor='none',
visible=False)
s3 = ax3.scatter(x[recent],
N.log10(burst[recent]),
s=3.5,
marker='>',
c=z[recent],
cmap=cm.get_cmap('jet'),
alpha=0.15,
edgecolor='none',
label='Recent Mergers $T_{\mathrm{merge}} < 250$ Myr')
s3 = ax3.scatter(x[recent],
N.log10(burst[recent]),
s=1.0,
marker='>',
c=z[recent],
cmap=cm.get_cmap('jet'),
edgecolor='none',
visible=False)
s6 = ax4.scatter(x[noMerge],
N.log10(avera[noMerge]),
s=0.5,
marker='o',
c='red',
alpha=0.1,
edgecolor='none',
label='Never Merged')
s7 = ax4.scatter(x[recent],
N.log10(avera[recent]),
s=0.5,
marker='s',
c='blue',
alpha=0.1,
edgecolor='none',
label='Recent Mergers $T_{\mathrm{merge}} < 250$ Myr')
s6 = ax4.scatter(x[noMerge],
N.log10(avera[noMerge]),
s=2.0,
marker='o',
c='red',
edgecolor='none',
visible=False)
s7 = ax4.scatter(x[recent],
N.log10(avera[recent]),
s=2.0,
marker='s',
c='blue',
edgecolor='none',
visible=False)
#colorbars
cax1 = fig.add_axes([0.11, 0.93, 0.15,
0.15 / 12.]) #[left, bottom, width, height]
c1 = fig.colorbar(s1,
cax=cax1,
orientation='horizontal',
ticks=[2, 2.6, 3.4, 4])
c1.set_label('$\mathrm{Redshift}$', size=13)
cax2 = fig.add_axes([0.6, 0.93, 0.15, 0.15 / 12.])
c2 = fig.colorbar(s2,
cax=cax2,
orientation='horizontal',
ticks=[2, 2.6, 3.4, 4])
c2.set_label('$\mathrm{Redshift}$', size=13)
cax3 = fig.add_axes([0.11, 0.475, 0.15, 0.15 / 12.])
c3 = fig.colorbar(s3,
cax=cax3,
orientation='horizontal',
ticks=[2, 2.6, 3.4, 4])
c3.set_label('$\mathrm{Redshift}$', size=13)
#labels
xlabel = r'$\log_{10} \left ( \frac{S_{250}}{S_{8.0}} \right )$'
ylabel1 = r'$\log_{10}(\dot{M}_{\star} \ [M_{\odot} \ \mathrm{yr}^{-1}])$'
ylabel3 = r'$\log_{10}(\dot{M}_{\mathrm{BURST}} \ [M_{\odot} \ \mathrm{yr}^{-1}])$'
ylabel4 = r'$\log_{10}(< \dot{M}_{\star} > \ [M_{\odot} \ \mathrm{yr}^{-1}])$'
ax1.set_xticklabels([])
ax1.set_ylabel(ylabel1)
ax2.set_xticklabels([])
ax2.set_ylabel(ylabel1)
ax3.set_xlabel(xlabel)
ax3.set_ylabel(ylabel3)
ax4.set_xlabel(xlabel)
ax4.set_ylabel(ylabel4)
#limits
xmin = -1.5
xmax = 3.3
ymin = -3.8
ymax = 3.2
ax1.set_ylim(ymin, ymax)
ax1.set_xlim(xmin, xmax)
ax2.set_ylim(ymin, ymax)
ax2.set_xlim(xmin, xmax)
ax3.set_ylim(ymin, ymax)
ax3.set_xlim(xmin, xmax)
ax4.set_ylim(ymin, ymax)
ax4.set_xlim(xmin, xmax)
#make grid
ax1.grid()
ax2.grid()
ax3.grid()
ax4.grid()
#legends and save
ax1.legend(loc='upper left', scatterpoints=1, markerscale=7)
ax2.legend(loc='upper left', scatterpoints=1, markerscale=7)
ax2.legend(loc='upper left', scatterpoints=1, markerscale=7)
ax3.legend(loc='upper left', scatterpoints=1, markerscale=7)
ax4.legend(loc='upper left', scatterpoints=1, markerscale=18)
P.savefig(out_folder + output)
def plotMergerFractions(query,
xlabel, ylabel,
output, out_folder,
mergetimelimit=0.25,
mstarmin=8.0,
mstarmax=11.5,
mbins=15,
ymin=-0.01,
ymax=1.01,
logscale=False):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
if logscale:
mstar = N.log10(data[:, 0])
logscale = False
else:
mstar = data[:, 0]
print N.min(mstar), N.max(mstar)
tmerge = data[:, 1]
tmajor = data[:, 2]
#masks
nomergeMask = tmerge < 0.0
majorsMask = (tmajor > 0.0) & (tmajor <= mergetimelimit)
majorsMask2 = (tmajor > mergetimelimit)
mergersMask = (tmerge > 0.0) & (tmerge <= mergetimelimit) &\
(majorsMask == False) & (majorsMask2 == False)
mergersMask2 = (nomergeMask == False) & (majorsMask == False) &\
(mergersMask == False) & (majorsMask2 == False)
#bin the data
mids, numbs = dm.binAndReturnMergerFractions2(mstar,
nomergeMask,
mergersMask,
majorsMask,
mergersMask2,
majorsMask2,
mstarmin,
mstarmax,
mbins,
logscale)
#the fraction of mergers
noMergerFraction = N.array([float(x[1]) / x[0] for x in numbs])
mergerFraction = N.array([float(x[2]) / x[0] for x in numbs])
majorMergerFraction = N.array([float(x[3]) / x[0] for x in numbs])
mergerFraction2 = N.array([float(x[4]) / x[0] for x in numbs])
majorMergerFraction2 = N.array([float(x[5]) / x[0] for x in numbs])
#sanity check
for a, b, c, d, e in zip(noMergerFraction, mergerFraction, majorMergerFraction,
mergerFraction2, majorMergerFraction2):
print a + b + c + d + e
#make the figure
# fig = P.figure()
fig = P.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.08, bottom=0.07,
right=0.97, top=0.93)
ax1 = fig.add_subplot(111)
#draw lines
ax1.plot(mids, noMergerFraction, 'k-', lw=2.6,
label='Never Merged')
ax1.plot(mids, mergerFraction, ls='--', lw=2.6,
label='Minor Merger: $T \leq 250$ Myr')
ax1.plot(mids, mergerFraction2, ls='-.', lw=2.6,
label='Minor Merger: $T > 250$ Myr')
ax1.plot(mids, majorMergerFraction, ls='--', lw=2.6,
label='Major Merger: $T \leq 250$ Myr')
ax1.plot(mids, majorMergerFraction2, ls='-.', lw=2.6,
label='Major Merger: $T > 250$ Myr')
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#limits
ax1.set_ylim(ymin, ymax)
#add annotate
P.text(0.5, 0.93, 'All galaxies\n$2 \leq z < 4$',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#make grid
ax1.grid()
#legend and save
P.legend(loc='upper right')
P.savefig(out_folder + output)
def plotMergerFractions(query,
xlabel,
ylabel,
output,
out_folder,
mergetimelimit=0.25,
ymin=-0.2,
ymax=1.0,
xmin=-8,
xmax=4.1,
xbins=70,
ybins=70,
title='All galaxies in $2 \leq z < 4$'):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x = data[:, 0]
f775w = data[:, 1]
f850lp = data[:, 2]
uvcolor = f775w - f850lp
tmerge = data[:, 3]
tmajor = data[:, 4]
#masks
nomergeMask = tmerge < 0.0
majorsMask = (tmajor > 0.0) & (tmajor <= mergetimelimit)
majorsMask2 = (tmajor > mergetimelimit)
mergersMask = (tmerge > 0.0) & (tmerge <= mergetimelimit) &\
(majorsMask == False) & (majorsMask2 == False)
mergersMask2 = (nomergeMask == False) & (majorsMask == False) &\
(mergersMask == False) & (majorsMask2 == False)
#KDE
mu = M.AnaKDE([N.log10(x[nomergeMask]), uvcolor[nomergeMask]])
x_vec, y_vec, zm, lvls, d0, d1 = mu.contour(N.linspace(xmin, xmax, xbins),
N.linspace(ymin, ymax, ybins),
return_data=True)
#make the figure
# fig = P.figure()
fig = P.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.09, bottom=0.08, right=0.92, top=0.94)
ax1 = fig.add_subplot(111)
#make contours
cont = ax1.contour(x_vec,
y_vec,
zm,
linewidths=1.3,
colors='black',
levels=N.linspace(0.2, N.max(zm), 5))
#plot scatters
ax1.scatter(N.log10(x[nomergeMask]),
uvcolor[nomergeMask],
s=1,
c='k',
marker='s',
label='Never merged')
s2 = ax1.scatter(N.log10(x[mergersMask]),
uvcolor[mergersMask],
s=18,
c=1000. * tmerge[mergersMask],
marker='^',
cmap=cm.get_cmap('jet'),
edgecolor='none',
label='Minor Merger: $T \leq %.0f$ Myr' %
(mergetimelimit * 1000.),
alpha=0.2)
s1 = ax1.scatter(N.log10(x[majorsMask]),
uvcolor[majorsMask],
s=25,
c=1000. * tmajor[majorsMask],
marker='o',
cmap=cm.get_cmap('jet'),
edgecolor='none',
label='Major Merger: $T \leq %.0f$ Myr' %
(mergetimelimit * 1000.),
alpha=0.2)
s1 = ax1.scatter(N.log10(x[majorsMask]),
uvcolor[majorsMask],
s=1,
c=1000. * tmajor[majorsMask],
marker='o',
cmap=cm.get_cmap('jet'),
edgecolor='none',
alpha=1.0,
visible=False)
c1 = fig.colorbar(s1, shrink=0.8, fraction=0.03)
c1.set_label('Time since merger [Myr]')
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#limits
ax1.set_ylim(ymin, ymax)
ax1.set_xlim(xmin, xmax)
#add annotate
P.text(0.5,
1.03,
title,
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#make grid
ax1.grid()
#legend and save
P.legend(loc='upper left',
scatterpoints=1,
shadow=True,
fancybox=True,
markerscale=2)
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 plotMergerFractions3(query,
xlabel,
ylabel,
output,
out_folder,
mergetimelimit=0.25,
mstarmin=8.0,
mstarmax=11.5,
mbins=15,
ymin=0.0,
ymax=1.0,
logscale=False):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
tmp = data[:, 1]
mstar = data[:, 0]
mstar = N.log10(mstar / 10**tmp)
print N.min(mstar), N.max(mstar)
tmerge = data[:, 2]
tmajor = data[:, 3]
#masks
nomergeMask = tmerge < 0.0
majorsMask = (tmajor > 0.0) & (tmajor <= mergetimelimit)
majorsMask2 = (tmajor > mergetimelimit)
mergersMask = (tmerge > 0.0) & (tmerge <= mergetimelimit) &\
(majorsMask == False) & (majorsMask2 == False)
mergersMask2 = (nomergeMask == False) & (majorsMask == False) &\
(mergersMask == False) & (majorsMask2 == False)
#bin the data
mids, numbs = dm.binAndReturnMergerFractions2(mstar, nomergeMask,
mergersMask, majorsMask,
mergersMask2, majorsMask2,
mstarmin, mstarmax, mbins,
logscale)
#the fraction of mergers
noMergerFraction = N.array([float(x[1]) / x[0] for x in numbs])
mergerFraction = N.array([float(x[2]) / x[0] for x in numbs])
majorMergerFraction = N.array([float(x[3]) / x[0] for x in numbs])
mergerFraction2 = N.array([float(x[4]) / x[0] for x in numbs])
majorMergerFraction2 = N.array([float(x[5]) / x[0] for x in numbs])
#sanity check
for a, b, c, d, e in zip(noMergerFraction, mergerFraction,
majorMergerFraction, mergerFraction2,
majorMergerFraction2):
print a + b + c + d + e
#make the figure
# fig = P.figure()
fig = P.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.08, bottom=0.07, right=0.97, top=0.93)
ax1 = fig.add_subplot(111)
#calculate widths
wd = (mids[1] - mids[0]) * 1.0
#draw bars
ax1.bar(mids,
noMergerFraction,
label='Never Merged',
align='center',
color='grey',
width=wd,
hatch='.')
ax1.bar(mids,
mergerFraction,
bottom=noMergerFraction,
align='center',
label='Minor Merger: $T \leq %.0f$ Myr' % (mergetimelimit * 1000.),
color='red',
width=wd,
hatch='/')
ax1.bar(mids,
mergerFraction2,
align='center',
bottom=noMergerFraction + mergerFraction,
label='Minor Merger: $T > %.0f$ Myr' % (mergetimelimit * 1000.),
color='blue',
width=wd,
hatch='|')
ax1.bar(mids,
majorMergerFraction,
align='center',
bottom=noMergerFraction + mergerFraction + mergerFraction2,
label='Major Merger: $T \leq %.0f$ Myr' % (mergetimelimit * 1000.),
color='magenta',
width=wd,
hatch='x')
ax1.bar(mids,
majorMergerFraction2,
align='center',
bottom=noMergerFraction + mergerFraction + mergerFraction2 +
majorMergerFraction,
label='Major Merger: $T > %.0f$ Myr' % (mergetimelimit * 1000.),
color='green',
width=wd,
hatch='-')
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#limits
ax1.set_ylim(ymin, ymax)
ax1.set_xlim(mids[0] - wd / 2., mids[-1] + wd / 2.)
#add annotate
P.text(0.5,
1.05,
'All galaxies in $2 \leq z < 4$',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#legend and save
P.legend(loc='upper center')
P.savefig(out_folder + output)
def plotMergerFractionsMultiplot(query,
xlabel,
ylabel,
output,
out_folder,
mergetimelimit=0.25,
ymin=-0.2,
ymax=0.8,
xmin=-9,
xmax=4.1,
xbins=50,
ybins=50,
title=''):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x = data[:, 0]
f775w = data[:, 1]
f850lp = data[:, 2]
uvcolor = f775w - f850lp
tmerge = data[:, 3]
tmajor = data[:, 4]
#masks
nomergeMask = tmerge < 0.0
majorsMask = (tmajor > 0.0) & (tmajor <= mergetimelimit)
majorsMask2 = (tmajor > mergetimelimit)
mergersMask = (tmerge > 0.0) & (tmerge <= mergetimelimit) &\
(majorsMask == False) & (majorsMask2 == False)
mergersMask2 = (nomergeMask == False) & (majorsMask == False) &\
(mergersMask == False) & (majorsMask2 == False)
#KDE
mu = M.AnaKDE([N.log10(x[nomergeMask]), uvcolor[nomergeMask]])
x_vec, y_vec, zm, lvls, d0, d1 = mu.contour(N.linspace(xmin, xmax, xbins),
N.linspace(ymin, ymax, ybins),
return_data=True)
#make the figure
# fig = P.figure()
fig = P.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.09,
bottom=0.08,
right=0.93,
top=0.95,
wspace=0.0,
hspace=0.0)
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
#make contours
lv = N.linspace(0.2, N.max(zm), 4)
cont = ax1.contour(x_vec,
y_vec,
zm,
linewidths=0.9,
levels=lv,
colors='black')
cont = ax2.contour(x_vec,
y_vec,
zm,
linewidths=0.9,
levels=lv,
colors='black')
cont = ax3.contour(x_vec,
y_vec,
zm,
linewidths=0.9,
levels=lv,
colors='black')
cont = ax4.contour(x_vec,
y_vec,
zm,
linewidths=0.9,
levels=lv,
colors='black')
#plot scatters
s1 = ax1.scatter(N.log10(x[majorsMask]),
uvcolor[majorsMask],
s=4,
c=1000. * tmajor[majorsMask],
marker='o',
cmap=cm.get_cmap('jet'),
edgecolor='none',
label='Major Merger: $T \leq %.0f$ Myr' %
(mergetimelimit * 1000.),
alpha=0.25)
s2 = ax2.scatter(N.log10(x[mergersMask]),
uvcolor[mergersMask],
s=6,
c=1000. * tmerge[mergersMask],
marker='^',
cmap=cm.get_cmap('jet'),
edgecolor='none',
label='Minor Merger: $T \leq %.0f$ Myr' %
(mergetimelimit * 1000.),
alpha=0.25)
s2 = ax2.scatter(N.log10(x[mergersMask]),
uvcolor[mergersMask],
s=6,
c=1000. * tmerge[mergersMask],
marker='^',
cmap=cm.get_cmap('jet'),
edgecolor='none',
visible=False)
#masks
mergetimelimit *= 2.
majorsMask = (tmajor > 0.0) & (tmajor <= mergetimelimit)
majorsMask2 = (tmajor > mergetimelimit)
mergersMask = (tmerge > 0.0) & (tmerge <= mergetimelimit) &\
(majorsMask == False) & (majorsMask2 == False)
s3 = ax3.scatter(N.log10(x[majorsMask]),
uvcolor[majorsMask],
s=4,
c=1000. * tmajor[majorsMask],
marker='o',
cmap=cm.get_cmap('jet'),
edgecolor='none',
label='Major Merger: $T \leq %.0f$ Myr' %
(mergetimelimit * 1000.),
alpha=0.25)
s4 = ax4.scatter(N.log10(x[mergersMask]),
uvcolor[mergersMask],
s=6,
c=1000. * tmerge[mergersMask],
marker='^',
cmap=cm.get_cmap('jet'),
edgecolor='none',
label='Minor Merger: $T \leq %.0f$ Myr' %
(mergetimelimit * 1000.),
alpha=0.25)
s4 = ax4.scatter(N.log10(x[mergersMask]),
uvcolor[mergersMask],
s=6,
c=1000. * tmerge[mergersMask],
marker='^',
cmap=cm.get_cmap('jet'),
edgecolor='none',
visible=False)
c1 = fig.colorbar(s2, ax=ax2, shrink=0.7, fraction=0.05)
c2 = fig.colorbar(s4, ax=ax4, shrink=0.7, fraction=0.05)
c1.set_label('Time since merger [Myr]')
c2.set_label('Time since merger [Myr]')
#add annotate
P.text(1.0,
1.04,
title,
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#labels
ax3.set_xlabel(xlabel)
ax4.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
ax3.set_ylabel(ylabel)
ax2.set_yticklabels([])
ax4.set_yticklabels([])
ax1.set_xticklabels([])
ax2.set_xticklabels([])
#limits
ax1.set_ylim(ymin, ymax)
ax1.set_xlim(xmin, xmax)
ax2.set_ylim(ymin, ymax)
ax2.set_xlim(xmin, xmax)
ax3.set_ylim(ymin, ymax)
ax3.set_xlim(xmin, xmax)
ax4.set_ylim(ymin, ymax)
ax4.set_xlim(xmin, xmax)
#make grid
ax1.grid()
ax2.grid()
ax3.grid()
ax4.grid()
#legend and save
ax1.legend(loc='upper left',
scatterpoints=1,
shadow=True,
fancybox=True,
markerscale=3)
ax2.legend(loc='upper left',
scatterpoints=1,
shadow=True,
fancybox=True,
markerscale=3)
ax3.legend(loc='upper left',
scatterpoints=1,
shadow=True,
fancybox=True,
markerscale=3)
ax4.legend(loc='upper left',
scatterpoints=1,
shadow=True,
fancybox=True,
markerscale=3)
P.savefig(out_folder + output)
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 plotMergerFractions(query,
xlabel,
ylabel,
output,
out_folder,
mergetimelimit=0.25,
mstarmin=8.0,
mstarmax=11.5,
mbins=15,
ymin=-0.01,
ymax=1.01,
logscale=False):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
if logscale:
mstar = N.log10(data[:, 0])
logscale = False
else:
mstar = data[:, 0]
print N.min(mstar), N.max(mstar)
tmerge = data[:, 1]
tmajor = data[:, 2]
#masks
nomergeMask = tmerge < 0.0
majorsMask = (tmajor > 0.0) & (tmajor <= mergetimelimit)
majorsMask2 = (tmajor > mergetimelimit)
mergersMask = (tmerge > 0.0) & (tmerge <= mergetimelimit) &\
(majorsMask == False) & (majorsMask2 == False)
mergersMask2 = (nomergeMask == False) & (majorsMask == False) &\
(mergersMask == False) & (majorsMask2 == False)
#bin the data
mids, numbs = dm.binAndReturnMergerFractions2(mstar, nomergeMask,
mergersMask, majorsMask,
mergersMask2, majorsMask2,
mstarmin, mstarmax, mbins,
logscale)
#the fraction of mergers
noMergerFraction = N.array([float(x[1]) / x[0] for x in numbs])
mergerFraction = N.array([float(x[2]) / x[0] for x in numbs])
majorMergerFraction = N.array([float(x[3]) / x[0] for x in numbs])
mergerFraction2 = N.array([float(x[4]) / x[0] for x in numbs])
majorMergerFraction2 = N.array([float(x[5]) / x[0] for x in numbs])
#sanity check
for a, b, c, d, e in zip(noMergerFraction, mergerFraction,
majorMergerFraction, mergerFraction2,
majorMergerFraction2):
print a + b + c + d + e
#make the figure
# fig = P.figure()
fig = P.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.08, bottom=0.07, right=0.97, top=0.93)
ax1 = fig.add_subplot(111)
#draw lines
ax1.plot(mids, noMergerFraction, 'k-', lw=2.6, label='Never Merged')
ax1.plot(mids,
mergerFraction,
ls='--',
lw=2.6,
label='Minor Merger: $T \leq 250$ Myr')
ax1.plot(mids,
mergerFraction2,
ls='-.',
lw=2.6,
label='Minor Merger: $T > 250$ Myr')
ax1.plot(mids,
majorMergerFraction,
ls='--',
lw=2.6,
label='Major Merger: $T \leq 250$ Myr')
ax1.plot(mids,
majorMergerFraction2,
ls='-.',
lw=2.6,
label='Major Merger: $T > 250$ Myr')
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#limits
ax1.set_ylim(ymin, ymax)
#add annotate
P.text(0.5,
0.93,
'All galaxies\n$2 \leq z < 4$',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#make grid
ax1.grid()
#legend and save
P.legend(loc='upper right')
P.savefig(out_folder + output)
def massfunctions(path, db,
mmax=12.5,
mmin=5.0,
nbins=30,
output='SMN',
nvolumes=15):
'''
Plots stellar mass functions, cold gas mass functions, & BH MF
'''
#get data
query = '''select gal_id, halo_id, mbulge, mstar, mcold, mbh from galprop'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
gal_id = data[:, 0]
halo_id = data[:, 1]
mbulge = data[:, 2]
mstar = data[:, 3]
mcold = data[:, 4]
mbh = data[:, 5]
ngal = len(gal_id)
print '%i galaxies found' % ngal
#overrides weight for nvolumes Bolshoi sub-volumes
weight = N.zeros(ngal) + 1. / (nvolumes * (50. / 0.7) ** 3)
#mass bins
dm = (mmax - mmin) / nbins
mbin = mmin + (N.arange(nbins) + 0.5) * dm
#mfit = mmin + N.arange(150) * 0.05
mf_cold = N.zeros(nbins)
mf_star = N.zeros(nbins)
mf_star_central = N.zeros(nbins)
mf_bar = N.zeros(nbins)
mf_bh = N.zeros(nbins)
mf_early = N.zeros(nbins)
mf_late = N.zeros(nbins)
mf_bulge = N.zeros(nbins)
#TODO from IDL, should be done without looping
btt = 10.0 ** (mbulge - mstar)
for i in range(ngal):
ihalo = halo_id[i]
#cold gas
ibin = int(N.floor((mcold[i] - mmin) / dm))
if ibin >= 0 and ibin < nbins:
mf_cold[ibin] += weight[ihalo]
#stellar mass
ibin = int(N.floor((mstar[i] - mmin) / dm))
if ibin >= 0 and ibin < nbins:
mf_star[ibin] += weight[ihalo]
#stellar mass, by type
if btt[i] >= 0.4:
mf_early[ibin] += weight[ihalo]
else:
mf_late[ibin] += weight[ihalo]
#stellar mass, centrals
if gal_id[i] == 1:
if ibin >= 0 and ibin < nbins:
mf_star_central[ibin] += weight[ihalo]
#bulge mass
ibin = int(N.floor((mbulge[i] - mmin) / dm))
if ibin >= 0 and ibin < nbins:
mf_bulge[ibin] += weight[ihalo]
#baryonic mass
mbar = N.log10(10.0 ** mcold[i] + 10.0 ** mstar[i])
ibin = int(N.floor((mbar - mmin) / dm))
if ibin >= 0 and ibin < nbins:
mf_bar[ibin] += weight[ihalo]
#black hole
ibin = int(N.floor((mbh[i] - mmin) / dm))
if ibin >= 0 and ibin < nbins:
mf_bh[ibin] += weight[ihalo]
mf_cold = N.log10(mf_cold / dm)
mf_star = N.log10(mf_star / dm)
#mf_early = N.log10(mf_early / dm)
#mf_late = N.log10(mf_late / dm)
#mf_bulge = N.log10(mf_bulge / dm)
#mf_star_central = N.log10(mf_star_central / dm)
mf_bar = N.log10(mf_bar / dm)
mf_bh = N.log10(mf_bh / dm)
#stellar mass function plot
#obs constrains
mg, phig, phi_lowg, phi_highg = stellarMFs.bellG()
mk, phik, phi_lowk, phi_highk = stellarMFs.bellK()
#ml, phil, phi_lowl, phi_hiugl = mstar_lin(obs + 'sdss_mf/SDSS_SMF.dat')
m, n, nlow, nhigh = stellarMFs.panter()
fig = P.figure()
ax = fig.add_subplot(111)
ax.plot(mbin, mf_star, label='Stellar Mass Function')
ax.errorbar(mg, phig, yerr=[phig - phi_highg, phi_lowg - phig], label='$G$-band from Bell et al.')
ax.errorbar(mk, phik, yerr=[phik - phi_highk, phi_lowk - phik], label='$K$-band from Bell et al.')
#ax.errorbar(ml, phil, yerr = [phi_lowl, phi_highl], label = 'Lin et al. K')
ax.errorbar(m, n, yerr=[nlow - n, n - nhigh], label='$K$-band from Panter et al.')
ax.set_xlim(8.0, 12.5)
ax.set_ylim(-6.1, -1.0)
ax.set_xlabel(r'$\log_{10} \left ( M_{\star} \ [M_{\odot}] \right )$')
ax.set_ylabel(
r'$\frac{\log_{10} \mathrm{d}N}{\mathrm{d}\log_{10} M_{\star}} \quad [\mathrm{Mpc}^{-3} \mathrm{dex}^{-1}]$')
#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()
P.savefig('mfstar_' + output + '.pdf')
P.close()
#cold gas mass function
#obs constrains
#mhi_bell_h70
#mgas_bell_h70
theta = gas.HIMassFunctionZwaan(mbin)
m_hi, phi_hi, phi_low_hi, phi_high_hi = gas.HIMassFunctionBell()
m_h2, phi_h2, phi_low_h2, phi_high_h2 = gas.H2MassFunctionBell()
fig = P.figure()
ax = fig.add_subplot(111)
ax.plot(mbin, mf_cold, label='Cold Gas Mass Function')
ax.plot(mbin, theta, 'g-', lw=1.8, label='Zwaan et al.')
ax.errorbar(m_hi, phi_hi, yerr=[phi_hi - phi_high_hi, phi_low_hi - phi_hi],
c='cyan', ls='--', lw=1.5, label='Bell et al. $H_{I}$')
ax.errorbar(m_h2, phi_h2, yerr=[phi_h2 - phi_high_h2, phi_low_h2 - phi_h2],
c='magenta', ls=':', lw=1.5, label='Bell et al. $H_{2}$')
ax.plot(m_hi, N.log10(10.0 ** phi_hi + 10 ** phi_h2), 'r-', label='$H_{I} + H_{2}$')
ax.set_xlim(8.0, 11.5)
ax.set_ylim(-5.5, -0.8)
ax.set_xlabel(r'$\log_{10} \left ( M_{\mathrm{cold}} \ [M_{\odot}] \right )$')
ax.set_ylabel(r'$\frac{\log_{10} \mathrm{d}N}{\mathrm{d}\log_{10} M} \quad [\mathrm{Mpc}^{-3} \mathrm{dex}^{-1}]$')
#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()
P.savefig('mfcold_' + output + '.pdf')
P.close()
#total baryons
#obs contstrains
m_bar, phi_bar, phi_low_bar, phi_high_bar = baryons.BellBaryonicMassFunction()
fig = P.figure()
ax = fig.add_subplot(111)
ax.plot(mbin, mf_bar, label='Total Baryon Mass')
ax.errorbar(m_bar, phi_bar, yerr=[phi_bar - phi_high_bar, phi_low_bar - phi_bar],
c='g', ls='-', label='Bell et al.')
ax.set_xlim(8.0, 12.5)
ax.set_ylim(-4.9, -0.25)
ax.set_xlabel(r'$\log_{10} \left ( M_{\mathrm{baryons}} \ [M_{\odot}] \right )$')
ax.set_ylabel(
r'$\frac{\log_{10} \mathrm{d}N}{\mathrm{d}\log_{10} M_{\mathrm{baryons}}} \quad [\mathrm{Mpc}^{-3} \mathrm{dex}^{-1}]$')
#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()
P.savefig('mfbar_' + output + '.pdf')
P.close()
#BH mass function
#obs constrains
m_bh, phi_low_bh, phi_med_bh, phi_high_bh = bh.MarconiMassFunction()
fig = P.figure()
ax = fig.add_subplot(111)
ax.plot(mbin, mf_bh, 'k-', label='Black Hole Mass Function')
ax.plot(m_bh, phi_low_bh, 'g--')
ax.plot(m_bh, phi_med_bh, 'g-', label='Marconi et al.')
ax.plot(m_bh, phi_high_bh, 'g--')
ax.set_xlim(6.0, 10.2)
ax.set_ylim(-6.9, -1.5)
ax.set_xlabel(r'$\log_{10} \left ( M_{BH} \ [M_{\odot}] \right )$')
ax.set_ylabel(r'$\frac{\log_{10} \mathrm{d}N}{\mathrm{d}\log_{10} M} \quad [\mathrm{Mpc}^{-3} \mathrm{dex}^{-1}]$')
#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()
P.savefig('mfBH_' + output + '.pdf')
P.close()
def fstar(path, db, output='fstar.png'):
'''
Stellar mass to halo mass ratio as a function of halo mass.
This plot differs slightly from the one in the Somerville 2008:
the plot in the paper is the fraction of baryons in stars.
'''
#get data
query = '''select mstar, mhalo, gal_id from galprop'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
mstar = data[:, 0]
mhalo = data[:, 1]
gal_id = data[:, 2]
mfit = 10. + N.arange(50) * 0.1
mbin = 10.7 + N.arange(25) * 0.2
f8 = 8. - mfit
#f9 = 9. - mfit
fs_halo = 10.0 ** (mstar - mhalo)
#central and satellite galaxies
central = gal_id == 1
sat = gal_id != 1
mbin_mid, fs_50, fs_10, fs_90 = perc_bin(mbin, mhalo, fs_halo)
mbin_mid, fs_cen_50, fs_cen_10, fs_cen_90 = perc_bin(mbin, mhalo[central], fs_halo[central])
mbin_mid, fs_sat_50, fs_sat_10, fs_sat_90 = perc_bin(mbin, mhalo[sat], fs_halo[sat])
#begin figure
fig = P.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
ax.scatter(mhalo[central], N.log10(fs_halo[central]),
edgecolor='r', s=2, facecolor='r', label='Central galaxies')
ax.plot(mbin_mid, N.log10(fs_50), 'ko', ms=10, label='Median, all galaxies')
ax.plot(mbin_mid, N.log10(fs_10), 'k--')
ax.plot(mbin_mid, N.log10(fs_90), 'k--')
ax.plot(mbin_mid, N.log10(fs_sat_50), 'cD', ms=8, label='Median, satellite galaxies')
ax.plot(mbin_mid, N.log10(fs_cen_50), 'pm', ms=8, label='Median, central galaxies')
#constrains from moster et al.
fstar_med_ben = stellarMFs.fstarBen(mfit, 11.884, 0.0282, 1.057, 0.5560)
fstar_med_ben_scat = stellarMFs.fstarBen(mfit, 11.866, 0.02891, 1.110, 0.648)
ax.plot(mfit, N.log10(fstar_med_ben), 'g-', label='Moster et al.')
ax.plot(mfit, N.log10(fstar_med_ben_scat), c='burlywood', label='Moster et al. (sc)')
#get fstar_behroozi_log
mh, sh = stellarMFs.fstarBehroozi()
ax.plot(mh, sh, 'hg', label='Behroozi et al.')
ax.plot(mfit, f8, 'k--', c='0.25')
#axis scales
ax.set_xlim(9.8, 15.0)
ax.set_ylim(-4.0, -0.75)
#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)
ax.set_ylabel(r'$\log_{10} \left ( \frac{M_{star}}{f_{b}M_{halo}} \right )$')
ax.set_xlabel(r'$\log_{10} \left ( \frac{M_{(sub)halo}}{M_{\odot}} \right )$')
P.legend(numpoints=1, scatterpoints=1)
P.savefig(output)
P.close()
def massfunctions(path,
db,
mmax=12.5,
mmin=5.0,
nbins=30,
output='SMN',
nvolumes=15):
'''
Plots stellar mass functions, cold gas mass functions, & BH MF
'''
#get data
query = '''select gal_id, halo_id, mbulge, mstar, mcold, mbh from galprop'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
gal_id = data[:, 0]
halo_id = data[:, 1]
mbulge = data[:, 2]
mstar = data[:, 3]
mcold = data[:, 4]
mbh = data[:, 5]
ngal = len(gal_id)
print '%i galaxies found' % ngal
#overrides weight for nvolumes Bolshoi sub-volumes
weight = N.zeros(ngal) + 1. / (nvolumes * (50. / 0.7)**3)
#mass bins
dm = (mmax - mmin) / nbins
mbin = mmin + (N.arange(nbins) + 0.5) * dm
#mfit = mmin + N.arange(150) * 0.05
mf_cold = N.zeros(nbins)
mf_star = N.zeros(nbins)
mf_star_central = N.zeros(nbins)
mf_bar = N.zeros(nbins)
mf_bh = N.zeros(nbins)
mf_early = N.zeros(nbins)
mf_late = N.zeros(nbins)
mf_bulge = N.zeros(nbins)
#TODO from IDL, should be done without looping
btt = 10.0**(mbulge - mstar)
for i in range(ngal):
ihalo = halo_id[i]
#cold gas
ibin = int(N.floor((mcold[i] - mmin) / dm))
if ibin >= 0 and ibin < nbins:
mf_cold[ibin] += weight[ihalo]
#stellar mass
ibin = int(N.floor((mstar[i] - mmin) / dm))
if ibin >= 0 and ibin < nbins:
mf_star[ibin] += weight[ihalo]
#stellar mass, by type
if btt[i] >= 0.4:
mf_early[ibin] += weight[ihalo]
else:
mf_late[ibin] += weight[ihalo]
#stellar mass, centrals
if gal_id[i] == 1:
if ibin >= 0 and ibin < nbins:
mf_star_central[ibin] += weight[ihalo]
#bulge mass
ibin = int(N.floor((mbulge[i] - mmin) / dm))
if ibin >= 0 and ibin < nbins:
mf_bulge[ibin] += weight[ihalo]
#baryonic mass
mbar = N.log10(10.0**mcold[i] + 10.0**mstar[i])
ibin = int(N.floor((mbar - mmin) / dm))
if ibin >= 0 and ibin < nbins:
mf_bar[ibin] += weight[ihalo]
#black hole
ibin = int(N.floor((mbh[i] - mmin) / dm))
if ibin >= 0 and ibin < nbins:
mf_bh[ibin] += weight[ihalo]
mf_cold = N.log10(mf_cold / dm)
mf_star = N.log10(mf_star / dm)
#mf_early = N.log10(mf_early / dm)
#mf_late = N.log10(mf_late / dm)
#mf_bulge = N.log10(mf_bulge / dm)
#mf_star_central = N.log10(mf_star_central / dm)
mf_bar = N.log10(mf_bar / dm)
mf_bh = N.log10(mf_bh / dm)
#stellar mass function plot
#obs constrains
mg, phig, phi_lowg, phi_highg = stellarMFs.bellG()
mk, phik, phi_lowk, phi_highk = stellarMFs.bellK()
#ml, phil, phi_lowl, phi_hiugl = mstar_lin(obs + 'sdss_mf/SDSS_SMF.dat')
m, n, nlow, nhigh = stellarMFs.panter()
fig = P.figure()
ax = fig.add_subplot(111)
ax.plot(mbin, mf_star, label='Stellar Mass Function')
ax.errorbar(mg,
phig,
yerr=[phig - phi_highg, phi_lowg - phig],
label='$G$-band from Bell et al.')
ax.errorbar(mk,
phik,
yerr=[phik - phi_highk, phi_lowk - phik],
label='$K$-band from Bell et al.')
#ax.errorbar(ml, phil, yerr = [phi_lowl, phi_highl], label = 'Lin et al. K')
ax.errorbar(m,
n,
yerr=[nlow - n, n - nhigh],
label='$K$-band from Panter et al.')
ax.set_xlim(8.0, 12.5)
ax.set_ylim(-6.1, -1.0)
ax.set_xlabel(r'$\log_{10} \left ( M_{\star} \ [M_{\odot}] \right )$')
ax.set_ylabel(
r'$\frac{\log_{10} \mathrm{d}N}{\mathrm{d}\log_{10} M_{\star}} \quad [\mathrm{Mpc}^{-3} \mathrm{dex}^{-1}]$'
)
#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()
P.savefig('mfstar_' + output + '.pdf')
P.close()
#cold gas mass function
#obs constrains
#mhi_bell_h70
#mgas_bell_h70
theta = gas.HIMassFunctionZwaan(mbin)
m_hi, phi_hi, phi_low_hi, phi_high_hi = gas.HIMassFunctionBell()
m_h2, phi_h2, phi_low_h2, phi_high_h2 = gas.H2MassFunctionBell()
fig = P.figure()
ax = fig.add_subplot(111)
ax.plot(mbin, mf_cold, label='Cold Gas Mass Function')
ax.plot(mbin, theta, 'g-', lw=1.8, label='Zwaan et al.')
ax.errorbar(m_hi,
phi_hi,
yerr=[phi_hi - phi_high_hi, phi_low_hi - phi_hi],
c='cyan',
ls='--',
lw=1.5,
label='Bell et al. $H_{I}$')
ax.errorbar(m_h2,
phi_h2,
yerr=[phi_h2 - phi_high_h2, phi_low_h2 - phi_h2],
c='magenta',
ls=':',
lw=1.5,
label='Bell et al. $H_{2}$')
ax.plot(m_hi,
N.log10(10.0**phi_hi + 10**phi_h2),
'r-',
label='$H_{I} + H_{2}$')
ax.set_xlim(8.0, 11.5)
ax.set_ylim(-5.5, -0.8)
ax.set_xlabel(
r'$\log_{10} \left ( M_{\mathrm{cold}} \ [M_{\odot}] \right )$')
ax.set_ylabel(
r'$\frac{\log_{10} \mathrm{d}N}{\mathrm{d}\log_{10} M} \quad [\mathrm{Mpc}^{-3} \mathrm{dex}^{-1}]$'
)
#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()
P.savefig('mfcold_' + output + '.pdf')
P.close()
#total baryons
#obs contstrains
m_bar, phi_bar, phi_low_bar, phi_high_bar = baryons.BellBaryonicMassFunction(
)
fig = P.figure()
ax = fig.add_subplot(111)
ax.plot(mbin, mf_bar, label='Total Baryon Mass')
ax.errorbar(m_bar,
phi_bar,
yerr=[phi_bar - phi_high_bar, phi_low_bar - phi_bar],
c='g',
ls='-',
label='Bell et al.')
ax.set_xlim(8.0, 12.5)
ax.set_ylim(-4.9, -0.25)
ax.set_xlabel(
r'$\log_{10} \left ( M_{\mathrm{baryons}} \ [M_{\odot}] \right )$')
ax.set_ylabel(
r'$\frac{\log_{10} \mathrm{d}N}{\mathrm{d}\log_{10} M_{\mathrm{baryons}}} \quad [\mathrm{Mpc}^{-3} \mathrm{dex}^{-1}]$'
)
#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()
P.savefig('mfbar_' + output + '.pdf')
P.close()
#BH mass function
#obs constrains
m_bh, phi_low_bh, phi_med_bh, phi_high_bh = bh.MarconiMassFunction()
fig = P.figure()
ax = fig.add_subplot(111)
ax.plot(mbin, mf_bh, 'k-', label='Black Hole Mass Function')
ax.plot(m_bh, phi_low_bh, 'g--')
ax.plot(m_bh, phi_med_bh, 'g-', label='Marconi et al.')
ax.plot(m_bh, phi_high_bh, 'g--')
ax.set_xlim(6.0, 10.2)
ax.set_ylim(-6.9, -1.5)
ax.set_xlabel(r'$\log_{10} \left ( M_{BH} \ [M_{\odot}] \right )$')
ax.set_ylabel(
r'$\frac{\log_{10} \mathrm{d}N}{\mathrm{d}\log_{10} M} \quad [\mathrm{Mpc}^{-3} \mathrm{dex}^{-1}]$'
)
#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()
P.savefig('mfBH_' + output + '.pdf')
P.close()
def plotMergerFractions3(query,
xlabel, ylabel,
output, out_folder,
mergetimelimit=0.25,
mstarmin=8.0,
mstarmax=11.5,
mbins=15,
ymin=0.0,
ymax=1.0,
logscale=False):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
tmp = data[:, 1]
mstar = data[:, 0]
mstar = N.log10(mstar / 10 ** tmp)
print N.min(mstar), N.max(mstar)
tmerge = data[:, 2]
tmajor = data[:, 3]
#masks
nomergeMask = tmerge < 0.0
majorsMask = (tmajor > 0.0) & (tmajor <= mergetimelimit)
majorsMask2 = (tmajor > mergetimelimit)
mergersMask = (tmerge > 0.0) & (tmerge <= mergetimelimit) &\
(majorsMask == False) & (majorsMask2 == False)
mergersMask2 = (nomergeMask == False) & (majorsMask == False) &\
(mergersMask == False) & (majorsMask2 == False)
#bin the data
mids, numbs = dm.binAndReturnMergerFractions2(mstar,
nomergeMask,
mergersMask,
majorsMask,
mergersMask2,
majorsMask2,
mstarmin,
mstarmax,
mbins,
logscale)
#the fraction of mergers
noMergerFraction = N.array([float(x[1]) / x[0] for x in numbs])
mergerFraction = N.array([float(x[2]) / x[0] for x in numbs])
majorMergerFraction = N.array([float(x[3]) / x[0] for x in numbs])
mergerFraction2 = N.array([float(x[4]) / x[0] for x in numbs])
majorMergerFraction2 = N.array([float(x[5]) / x[0] for x in numbs])
#sanity check
for a, b, c, d, e in zip(noMergerFraction, mergerFraction, majorMergerFraction,
mergerFraction2, majorMergerFraction2):
print a + b + c + d + e
#make the figure
# fig = P.figure()
fig = P.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.08, bottom=0.07,
right=0.97, top=0.93)
ax1 = fig.add_subplot(111)
#calculate widths
wd = (mids[1] - mids[0]) * 1.0
#draw bars
ax1.bar(mids, noMergerFraction,
label='Never Merged', align='center',
color='grey', width=wd, hatch='.')
ax1.bar(mids, mergerFraction,
bottom=noMergerFraction, align='center',
label='Minor Merger: $T \leq %.0f$ Myr' % (mergetimelimit * 1000.),
color='red', width=wd, hatch='/')
ax1.bar(mids, mergerFraction2, align='center',
bottom=noMergerFraction + mergerFraction,
label='Minor Merger: $T > %.0f$ Myr' % (mergetimelimit * 1000.),
color='blue', width=wd, hatch='|')
ax1.bar(mids, majorMergerFraction, align='center',
bottom=noMergerFraction + mergerFraction + mergerFraction2,
label='Major Merger: $T \leq %.0f$ Myr' % (mergetimelimit * 1000.),
color='magenta', width=wd, hatch='x')
ax1.bar(mids, majorMergerFraction2, align='center',
bottom=noMergerFraction + mergerFraction + mergerFraction2 + majorMergerFraction,
label='Major Merger: $T > %.0f$ Myr' % (mergetimelimit * 1000.),
color='green', width=wd, hatch='-')
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
#limits
ax1.set_ylim(ymin, ymax)
ax1.set_xlim(mids[0] - wd / 2., mids[-1] + wd / 2.)
#add annotate
P.text(0.5, 1.05, 'All galaxies in $2 \leq z < 4$',
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#legend and save
P.legend(loc='upper center')
P.savefig(out_folder + output)
def fstar(path, db, output='fstar.png'):
'''
Stellar mass to halo mass ratio as a function of halo mass.
This plot differs slightly from the one in the Somerville 2008:
the plot in the paper is the fraction of baryons in stars.
'''
#get data
query = '''select mstar, mhalo, gal_id from galprop'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
mstar = data[:, 0]
mhalo = data[:, 1]
gal_id = data[:, 2]
mfit = 10. + N.arange(50) * 0.1
mbin = 10.7 + N.arange(25) * 0.2
f8 = 8. - mfit
#f9 = 9. - mfit
fs_halo = 10.0**(mstar - mhalo)
#central and satellite galaxies
central = gal_id == 1
sat = gal_id != 1
mbin_mid, fs_50, fs_10, fs_90 = perc_bin(mbin, mhalo, fs_halo)
mbin_mid, fs_cen_50, fs_cen_10, fs_cen_90 = perc_bin(
mbin, mhalo[central], fs_halo[central])
mbin_mid, fs_sat_50, fs_sat_10, fs_sat_90 = perc_bin(
mbin, mhalo[sat], fs_halo[sat])
#begin figure
fig = P.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
ax.scatter(mhalo[central],
N.log10(fs_halo[central]),
edgecolor='r',
s=2,
facecolor='r',
label='Central galaxies')
ax.plot(mbin_mid,
N.log10(fs_50),
'ko',
ms=10,
label='Median, all galaxies')
ax.plot(mbin_mid, N.log10(fs_10), 'k--')
ax.plot(mbin_mid, N.log10(fs_90), 'k--')
ax.plot(mbin_mid,
N.log10(fs_sat_50),
'cD',
ms=8,
label='Median, satellite galaxies')
ax.plot(mbin_mid,
N.log10(fs_cen_50),
'pm',
ms=8,
label='Median, central galaxies')
#constrains from moster et al.
fstar_med_ben = stellarMFs.fstarBen(mfit, 11.884, 0.0282, 1.057, 0.5560)
fstar_med_ben_scat = stellarMFs.fstarBen(mfit, 11.866, 0.02891, 1.110,
0.648)
ax.plot(mfit, N.log10(fstar_med_ben), 'g-', label='Moster et al.')
ax.plot(mfit,
N.log10(fstar_med_ben_scat),
c='burlywood',
label='Moster et al. (sc)')
#get fstar_behroozi_log
mh, sh = stellarMFs.fstarBehroozi()
ax.plot(mh, sh, 'hg', label='Behroozi et al.')
ax.plot(mfit, f8, 'k--', c='0.25')
#axis scales
ax.set_xlim(9.8, 15.0)
ax.set_ylim(-4.0, -0.75)
#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)
ax.set_ylabel(
r'$\log_{10} \left ( \frac{M_{star}}{f_{b}M_{halo}} \right )$')
ax.set_xlabel(
r'$\log_{10} \left ( \frac{M_{(sub)halo}}{M_{\odot}} \right )$')
P.legend(numpoints=1, scatterpoints=1)
P.savefig(output)
P.close()
