def plot_starburst(path,
db,
reshifts,
out_folder,
xmin=0.0,
xmax=2.0,
fluxlimit=5):
'''
Plots
'''
#figure
fig = P.figure()
ax = fig.add_subplot(111)
for i, reds in enumerate(redshifts):
query = '''select galprop.mstar_burst - galprop.mstar, FIR.pacs160_obs*1000
from FIR, galprop where
%s and
FIR.gal_id = galprop.gal_id and
FIR.halo_id = galprop.halo_id
''' % reds
#tmp
tmp = reds.split()
zz = N.mean(N.array([float(tmp[2]), float(tmp[6])]))
#get data
data = N.array(sq.get_data_sqlitePowerTen(path, db, query))
#set 1
xd = N.log10(data[:, 1])
yd = data[:, 0]
#percentiles
xmaxb = N.max(xd)
nxbins = int(12 * (xmaxb - xmin))
xbin_midd, y50d, y16d, y84d = dm.percentile_bins(xd,
yd,
xmin,
xmaxb,
nxbins=nxbins)
msk = y50d > -10
add = eval('0.0%s' % str(i))
ax.errorbar(xbin_midd[msk] + add,
y50d[msk],
yerr=[y50d[msk] - y16d[msk], y84d[msk] - y50d[msk]],
label='$z = %.1f$' % zz)
ax.axvline(N.log10(fluxlimit), ls=':', color='green')
ax.set_xlabel('$\log_{10}(S_{160} \ [\mathrm{mJy}])$')
ax.set_ylabel(
r'$\log_{10} \left ( \frac{M_{starbust}}{M_{\star}} \right )$')
ax.set_xlim(xmin, xmax)
ax.set_ylim(-3.0, -0.5)
P.legend(loc='lower right')
P.savefig(out_folder + 'mburstratio.ps')
def plot_DMmass(path,
db,
redshifts,
out_folder,
xmin=0.0,
xmax=2.5,
fluxlimit=5):
"""
Generates a dark matter halo mass prediction plot.
"""
lines = ["-", "--", "-.", ":"]
linecycler = cycle(lines)
colour = count(0.1, 0.1)
#figure
fig = P.figure()
ax = fig.add_subplot(111)
for i, reds in enumerate(redshifts):
#query = '''select galprop.mhalo, FIR.spire250_obs*1000
query = '''select galprop.mhalo, FIR.pacs160_obs*1000
from FIR, galprop where
%s and
FIR.gal_id = galprop.gal_id and
FIR.halo_id = galprop.halo_id
''' % reds
#tmp
tmp = reds.split()
zz = N.mean(N.array([float(tmp[2]), float(tmp[6])]))
#get data
data = N.array(sq.get_data_sqlitePowerTen(path, db, query))
#set 1
xd = N.log10(data[:, 1])
yd = data[:, 0]
#percentiles
xmaxb = N.max(xd)
nxbins = int(9 * (xmaxb - xmin))
xbin_midd, y50d, y16d, y84d = dm.percentile_bins(xd,
yd,
xmin,
xmaxb,
nxbins=nxbins,
limit=9)
msk = y50d > -10
add = eval('0.0%s' % str(i))
ax.errorbar(xbin_midd[msk] + add,
y50d[msk],
yerr=[y50d[msk] - y16d[msk], y84d[msk] - y50d[msk]],
ls=next(linecycler),
c=str(colour.next()),
label='$z = %.1f$' % zz)
#ax.axvline(N.log10(fluxlimit), ls=':', color='green')
#BW
ax.axvline(N.log10(fluxlimit), ls=':', color='0.25')
#ax.set_xlabel('$\log_{10}(S_{250} \ [\mathrm{mJy}])$')
ax.set_xlabel('$\log_{10}(S_{160} \ [\mathrm{mJy}])$')
ax.set_ylabel('$\log_{10}(M_{\mathrm{dm}} \ [M_{\odot}])$')
ax.set_xlim(xmin, xmax)
ax.set_ylim(10.98, 13.3)
P.legend(loc='lower right')
P.savefig(out_folder + 'DMmass2.ps')
def plot_sfrs(path,
db,
redshifts,
out_folder,
xmin=0.0,
xmax=2.3,
fluxlimit=5,
obs=True):
'''
Plots SFR
'''
lines = ["-", "--", "-.", ":"]
linecycler = cycle(lines)
colour = count(0.1, 0.1)
#figure
fig = P.figure()
ax = fig.add_subplot(111)
for reds in redshifts:
#query = '''select galprop.mstardot, FIR.pacs160_obs*1000
query = '''select galprop.mstardot, FIR.spire250_obs*1000
from FIR, galprop where
%s and
FIR.gal_id = galprop.gal_id and
FIR.halo_id = galprop.halo_id
''' % reds
#tmp
tmp = reds.split()
zz = N.mean(N.array([float(tmp[2]), float(tmp[6])]))
#get data
data = sq.get_data_sqlitePowerTen(path, db, query)
#set 1
xd = N.log10(data[:, 1])
yd = N.log10(data[:, 0])
#percentiles
xmaxb = N.max(xd)
nxbins = int(11 * (xmaxb - xmin))
xbin_midd, y50d, y16d, y84d = dm.percentile_bins(xd,
yd,
xmin,
xmaxb,
nxbins=nxbins)
msk = y50d > -10
ax.errorbar(xbin_midd[msk],
y50d[msk],
yerr=[y50d[msk] - y16d[msk], y84d[msk] - y50d[msk]],
ls=next(linecycler),
c=str(colour.next()),
label='$z = %.1f$' % zz)
print '\nStraight line fit for z = %.1f' % zz
tmp = fit.linearregression(xbin_midd[msk], y50d[msk], report=True)
#tmp = fit.linearregression(xd, yd,
# report = True)
#ax.axvline(N.log10(fluxlimit), ls=':', color='green')
ax.axvline(N.log10(fluxlimit), ls=':', color='0.25')
if obs:
data = N.loadtxt('/Users/sammy/Research/Herschel/LaceySFRs.txt')
data[:, 2] = N.log10(10**data[:, 2] / (1. / (1. / 0.7)))
msk1 = data[:, 0] < 15
msk2 = (data[:, 0] > 15) & (data[:, 0] < 35)
msk3 = (data[:, 0] > 35) & (data[:, 0] < 55)
msk4 = (data[:, 0] > 55) & (data[:, 0] < 70)
msk5 = (data[:, 0] > 70) & (data[:, 0] < 80)
ax.plot(data[msk1][:, 1],
data[msk1][:, 2],
c='0.1',
ls='--',
label='$\mathrm{L10:}\ z = 0.25$')
ax.plot(data[msk2][:, 1],
data[msk2][:, 2],
c='0.2',
ls='--',
label='$\mathrm{L10:}\ z = 1.0$')
ax.plot(data[msk3][:, 1],
data[msk3][:, 2],
c='0.3',
ls='--',
label='$\mathrm{L10:}\ z = 2.0$')
ax.plot(data[msk4][:, 1],
data[msk4][:, 2],
c='0.4',
ls='--',
label='$\mathrm{L10:}\ z = 3.0$')
ax.plot(data[msk5][:, 1],
data[msk5][:, 2],
c='0.5',
ls='--',
label='$\mathrm{L10:}\ z = 4.0$')
#ax.set_xlabel('$\log_{10}(S_{160} \ [\mathrm{mJy}])$')
ax.set_xlabel('$\log_{10}(S_{250} \ [\mathrm{mJy}])$')
ax.set_ylabel('$\log_{10}(\dot{M}_{\star} \ [M_{\odot}\mathrm{yr}^{-1}])$')
ax.set_xlim(xmin, xmax)
ax.set_ylim(-1.2, 3)
P.legend(loc='lower right')
if obs:
P.savefig(out_folder + 'sfrOBSBW.ps')
else:
P.savefig(out_folder + 'sfrBW.ps')
def plot_ssfr(path,
db,
redshifts,
out_folder,
xmin=0.0,
xmax=2.3,
fluxlimit=5):
"""
Plots SSFR.
"""
lines = ["-", "--", "-.", ":"]
linecycler = cycle(lines)
colour = count(0.1, 0.1)
#figure
fig = P.figure()
ax = fig.add_subplot(111)
for reds in redshifts:
query = '''select galprop.mstardot, galprop.mstar, FIR.pacs160_obs
from FIR, galprop where
%s and
FIR.gal_id = galprop.gal_id and
FIR.halo_id = galprop.halo_id
''' % reds
#tmp
tmp = reds.split()
zz = N.mean(N.array([float(tmp[2]), float(tmp[6])]))
#get data
data = sq.get_data_sqlitePowerTen(path, db, query)
#set 1
xd = N.log10(data[:, 2] * 1e3)
yd = N.log10(data[:, 0] / 10**data[:, 1])
#percentiles
xmaxb = N.max(xd)
nxbins = int(9 * (xmaxb - xmin))
xbin_midd, y50d, y16d, y84d = dm.percentile_bins(xd,
yd,
xmin,
xmaxb,
nxbins=nxbins)
msk = y50d > -20
ax.errorbar(xbin_midd[msk],
y50d[msk],
yerr=[y50d[msk] - y16d[msk], y84d[msk] - y50d[msk]],
ls=next(linecycler),
c=str(colour.next()),
label='$z = %.1f$' % zz)
#ax.axvline(N.log10(fluxlimit), ls=':', color='green')
ax.axvline(N.log10(fluxlimit), ls=':', color='0.25')
ax.set_xlabel('$\log_{10}(S_{160} \ [\mathrm{mJy}])$')
ax.set_ylabel(
r'$\log_{10} \left (\frac{\dot{M}_{\star}}{M_{\star}} \ [\mathrm{yr}^{-1}] \right )$'
)
ax.set_xlim(xmin, xmax)
ax.set_ylim(-11.1, -7.8)
P.legend() #loc = 'lower right')
P.savefig(out_folder + 'ssfr.ps')