def PlotDR8urHa(output, out_folder):
query = '''select d.petroMag_u - d.petroMag_r,
d.petroMagErr_u*d.petroMagErr_u + d.petroMagErr_r*d.petroMagErr_r,
d.h_alpha_flux, d.h_alpha_flux_err
from observations as o, RESOLVEmasterfile as r, dr8data as d
where o.flag = "C" and
d.petroMagErr_r < 5 and
d.petroMagErr_u < 5 and
o.name = r.name and
r.dr8specobjid = d.specobjid'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
xcomp = data[:, 0]
xcomperr = np.sqrt(data[:, 1])
ycomp = data[:, 2]
ycomperr = data[:, 3]
query = '''select d.petroMag_u - d.petroMag_r,
d.petroMagErr_u*d.petroMagErr_u + d.petroMagErr_r*d.petroMagErr_r,
d.h_alpha_flux, d.h_alpha_flux_err
from observations as o, RESOLVEmasterfile as r, dr8data as d
where o.flag = "TS" and
d.petroMagErr_r < 5 and
d.petroMagErr_u < 5 and
o.name = r.name and
r.dr8specobjid = d.specobjid'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
xsalt = data[:, 0]
xsalterr = np.sqrt(data[:, 1])
ysalt = data[:, 2]
ysalterr = data[:, 3]
#make the figure
fig = P.figure()
ax1 = fig.add_subplot(111)
ax1.errorbar(xcomp, ycomp, xerr=xcomperr, yerr=ycomperr, ls='None', c='b',
marker='s', ms=3, label='Completed (C)')
ax1.errorbar(xsalt, ysalt, xerr=xsalterr, yerr=ysalterr, ls='None', c='r',
marker='o', ms=3, label='SALT? (TS)')
ax1.set_xlabel(r'U - r from DR8 [mag]')
ax1.set_ylabel(r'$H_{\alpha}$ [1e-17 erg/s/cm$^{2}$]')
ax1.set_yscale('log')
ax1.set_ylim(2, 1e4)
ax1.set_xlim(0.5, 3.0)
P.legend(shadow=True, fancybox=True, numpoints=1)
P.savefig(out_folder + output)
def PlotDR8ug(output, out_folder):
query = '''select d.petroMag_u - d.petroMag_g,
d.petroMagErr_u*d.petroMagErr_u + d.petroMagErr_g*d.petroMagErr_g, sum(k.exptime)
from kindata as k, observations as o, RESOLVEmasterfile as r, dr8data as d
where o.flag = "C" and
d.petroMagErr_g < 5 and
d.petroMagErr_u < 5 and
k.paflag = 1 and k.obsshift = "Z" and
k.name = o.name and
k.name = r.name and
r.dr8specobjid = d.specobjid
group by k.name'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
xcomp = data[:, 0]
xcomperr = np.sqrt(data[:, 1])
ycomp = data[:, 2]
query = '''select d.petroMag_u - d.petroMag_g,
d.petroMagErr_u*d.petroMagErr_u + d.petroMagErr_g*d.petroMagErr_g, sum(k.exptime)
from kindata as k, observations as o, RESOLVEmasterfile as r, dr8data as d
where o.flag = "TS" and
d.petroMagErr_g < 5 and
d.petroMagErr_u < 5 and
k.paflag = 1 and k.obsshift = "Z" and
k.name = o.name and
k.name = r.name and
r.dr8specobjid = d.specobjid
group by k.name'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
xsalt = data[:, 0]
xsalterr = np.sqrt(data[:, 1])
ysalt = data[:, 2]
#make the figure
fig = P.figure()
ax1 = fig.add_subplot(111)
ax1.errorbar(xcomp, ycomp, xerr=xcomperr, ls='None', c='b',
marker='s', ms=3, label='Completed (C)')
ax1.errorbar(xsalt, ysalt, xerr=xsalterr, ls='None', c='r',
marker='o', ms=3, label='SALT? (TS)')
ax1.set_xlabel(r'U - g from DR8 [mag]')
ax1.set_ylabel(r'EXPTIME [min]')
ax1.set_xlim(0.5, 2.5)
P.legend(shadow=True, fancybox=True, numpoints=1)
P.savefig(out_folder + output)
def _getAndProcessData(home, outfile='SubDists2.pkl'):
"""
Retrieve data.
"""
path = home + '/Research/CANDELS/v2s/'
db = 'sams.db'
#SQL query where all the magic happens.. or doesn't
query = """select l1.redshift, l1.ra, l1.dec, l1.halo_id, l1.gal_id, l1.mhalo from testing l1
inner join
(
select l2.halo_id, l2.gal_id from testing l2 where
l2.halo_id in (select halo_id from testing group by halo_id having count(*) > 1)
intersect
select l3.halo_id, l3.gal_id from testing l3
group by l3.halo_id, l3.gal_id having count(*) == 1
)
using (halo_id, gal_id) where
l1.redshift > 0.1 and
l1.redshift < 6.0
"""
#pull out data
data = sq.get_data_sqliteSMNfunctions(path, db, query)
#group data
grouped = _findValues(data)
#dump it to a picked file
wr.cPickleDumpDictionary(grouped, outfile)
return grouped
def grR23(output, out_folder):
query = '''select d.petroMag_g - d.petroMag_r,
d.petroMagErr_g*d.petroMagErr_g + d.petroMagErr_r*d.petroMagErr_r,
(d.oii_3726_flux + d.oiii_4959_flux + d.oiii_5007_flux)/d.h_beta_flux
from dr8data as d where
d.petroMagErr_r > 0 and d.petroMagErr_r < 0.2 and
d.petroMagErr_g > 0 and d.petroMagErr_g < 0.2 and
d.oii_3726_flux > 0 and d.oiii_4959_flux > 0 and
d.oiii_5007_flux > 0 and d.h_beta_flux > 0 and
d.oii_3726_flux < 1e6 and d.oiii_4959_flux < 1e6 and
d.oiii_5007_flux < 1e6 and d.h_beta_flux < 1e6'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x = data[:, 0]
xerr = np.sqrt(data[:, 1])
y = np.log10(data[:, 2])
#yerr = data[:, 3]
#make the figure
fig = P.figure()
ax1 = fig.add_subplot(111)
ax1.errorbar(y, x, yerr=xerr, ls='None', c='r', marker='o', ms=1.5)
ax1.set_ylabel(r'g - r')
ax1.set_xlabel(r'log (R$_{23}$)')
P.savefig(out_folder + output)
def grmetallicity(output, out_folder):
query = '''select d.petroMag_g - d.petroMag_r,
d.petroMagErr_g*d.petroMagErr_g + d.petroMagErr_r*d.petroMagErr_r,
(d.oii_3726_flux + d.oiii_4959_flux + d.oiii_5007_flux)/d.h_beta_flux
from dr8data as d where
d.petroMagErr_r > 0 and d.petroMagErr_r < 0.15 and
d.petroMagErr_g > 0 and d.petroMagErr_g < 0.15 and
d.oii_3726_flux > 0 and d.oiii_4959_flux > 0 and
d.oiii_5007_flux > 0 and d.h_beta_flux > 0 and
d.oii_3726_flux < 1e7 and d.oiii_4959_flux < 1e7 and
d.oiii_5007_flux < 1e7 and d.h_beta_flux < 1e7'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x = data[:, 0]
xerr = np.sqrt(data[:, 1])
y = np.log10(data[:, 2])
#from Tremonti et al. 2004, ApJ, 613, 898
met = 9.185 - 0.313 * y - -0.264 * y**2 - 0.321 * y**3
#make the figure
fig = P.figure()
ax1 = fig.add_subplot(111)
ax1.errorbar(x, met, xerr=xerr, ls='None', c='r', marker='o', ms=1.5)
ax1.set_xlabel(r'g - r')
ax1.set_ylabel(r'12 + log(O/H)')
ax1.set_ylim(6.8, 9.4)
ax1.set_xlim(0.0, 1.1)
P.savefig(out_folder + output)
def NUVgHa(output, out_folder):
query = '''select g.nuv_mag - d.petroMag_r,
g.nuv_magerr*g.nuv_magerr + d.petroMagErr_r*d.petroMagErr_r,
d.h_alpha_flux, d.h_alpha_flux_err
from dr8data as d, galex as g, RESOLVEmasterfile as r where
d.h_alpha_flux_err > 0 and d.h_alpha_flux_err < 1e2 and
g.nuv_magerr > 0 and g.nuv_magerr < 2 and d.petroMagErr_r > 0 and
d.petroMagErr_r < 2 and
r.dr8specobjid = d.specobjid and r.dr7objid = g.sdssid'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x = data[:, 0]
xerr = np.sqrt(data[:, 1])
y = data[:, 2]
yerr = data[:, 3]
#make the figure
fig = P.figure()
ax1 = fig.add_subplot(111)
ax1.errorbar(x,
y,
xerr=xerr,
yerr=yerr,
ls='None',
c='r',
marker='o',
ms=1.5)
ax1.set_xlabel(r'NUV - r')
ax1.set_ylabel(r'$H_{\alpha}$ [1e-17 erg/s/cm$^{2}$/AA]')
ax1.set_yscale('log')
P.savefig(out_folder + output)
def grHa(output, out_folder):
query = '''select d.petroMag_g - d.petroMag_r,
d.petroMagErr_g*d.petroMagErr_g + d.petroMagErr_r*d.petroMagErr_r,
d.h_alpha_flux, d.h_alpha_flux_err
from dr8data as d where
d.h_alpha_flux_err > 0 and d.h_alpha_flux_err < 1e2 and
d.petroMagErr_r > 0 and d.petroMagErr_r < 0.1 and
d.petroMagErr_g > 0 and d.petroMagErr_g < 0.1'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
x = data[:, 0]
xerr = np.sqrt(data[:, 1])
y = data[:, 2]
yerr = data[:, 3]
#make the figure
fig = P.figure()
ax1 = fig.add_subplot(111)
ax1.errorbar(x,
y,
xerr=xerr,
yerr=yerr,
ls='None',
c='r',
marker='o',
ms=1.5)
ax1.set_xlabel(r'g - r')
ax1.set_ylabel(r'$H_{\alpha}$ [1e-17 erg/s/cm$^{2}$/AA]')
ax1.set_yscale('log')
ax1.set_xlim(-0.1, 1.8)
P.savefig(out_folder + output)
def PlotDR8ug50u(output, out_folder):
query = '''select d.petroR50_u - d.petroR50_g,
d.petroR50Err_u*d.petroR50Err_u* + d.petroR50Err_g*d.petroR50Err_g,
d.petroMag_u, d.petroMagErr_u
from observations as o, RESOLVEmasterfile as r, dr8data as d
where o.flag = "C" and
o.name = r.name and
petroR50Err_u > 0 and petroR50Err_r > 0 and d.petroMagErr_u < 5 and
r.dr8specobjid = d.specobjid'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
xcomp = data[:, 0]
xcomperr = np.sqrt(data[:, 1])
ycomp = data[:, 2]
ycomperr = data[:, 3]
query = '''select d.petroR50_u - d.petroR50_g,
d.petroR50Err_u*d.petroR50Err_u* + d.petroR50Err_g*d.petroR50Err_g,
d.petroMag_u, d.petroMagErr_u
from observations as o, RESOLVEmasterfile as r, dr8data as d
where o.flag = "TS" and
o.name = r.name and
petroR50Err_u > 0 and petroR50Err_r > 0 and d.petroMagErr_u < 5 and
r.dr8specobjid = d.specobjid'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
xsalt = data[:, 0]
xsalterr = np.sqrt(data[:, 1])
ysalt = data[:, 2]
ysalterr = data[:, 3]
#make the figure
fig = P.figure()
ax1 = fig.add_subplot(111)
ax1.errorbar(xcomp, ycomp, xerr=xcomperr, yerr=ycomperr, ls='None', c='b',
marker='s', ms=3, label='Completed (C)')
ax1.errorbar(xsalt, ysalt, xerr=xsalterr, yerr=ysalterr, ls='None', c='r',
marker='o', ms=3, label='SALT? (TS)')
ax1.set_xlabel(r'R50u - R50r from DR8 [arcsec]')
ax1.set_ylabel(r'u [mag]')
#ax1.set_ylim(0, 30)
ax1.set_xlim(-5, 10)
P.legend(shadow=True, fancybox=True, numpoints=1)
P.savefig(out_folder + output)
def PlotGALEXNUV(output, out_folder):
query = '''select g.nuv_mag, g.nuv_magerr, sum(k.exptime)
from kindata as k, observations as o, RESOLVEmasterfile as r, galex as g, galex_distinct as gt
where o.flag = "C" and
k.paflag = 1 and k.obsshift = "Z" and
k.name = o.name and k.name = r.name and
r.dr7objid = gt.sdssid and
gt.sdssid = g.sdssid
group by k.name'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
xcomp = data[:, 0]
ycomp = data[:, 2]
xcomperr = data[:, 1]
query = '''select g.nuv_mag, g.nuv_magerr, sum(k.exptime)
from kindata as k, observations as o, RESOLVEmasterfile as r, galex as g, galex_distinct as gt
where o.flag = "TS" and
k.paflag = 1 and k.obsshift = "Z" and
k.name = o.name and k.name = r.name and
r.dr7objid = gt.sdssid and
gt.sdssid = g.sdssid
group by k.name'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
xsalt = data[:, 0]
ysalt = data[:, 2]
xsalterr = data[:, 1]
#make the figure
fig = P.figure()
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(111)
ax1.errorbar(xcomp, ycomp, xerr=xcomperr, ls='None', c='b',
marker='s', ms=3, label='Completed (C)')
ax1.errorbar(xsalt, ysalt, xerr=xsalterr, ls='None', c='r',
marker='o', ms=3, label='SALT? (TS)')
ax1.set_xlabel(r'GALEX NUV [mag]')
ax1.set_ylabel(r'EXPTIME [min]')
ax1.set_ylim(5, 85)
P.legend(shadow=True, fancybox=True, numpoints=1)
P.savefig(out_folder + output)
def PlotHaflux(output, out_folder):
query = '''select d.h_alpha_flux, d.h_alpha_flux_err, sum(k.exptime)
from kindata as k, observations as o, RESOLVEmasterfile as r, dr8data as d
where o.flag = "C" and
k.paflag = 1 and k.obsshift = "Z" and
k.name = o.name and
k.name = r.name and
r.dr8specobjid = d.specobjid
group by k.name'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
xcomp = data[:, 0]
xcomperr = data[:, 1]
ycomp = data[:, 2]
query = '''select d.h_alpha_flux, d.h_alpha_flux_err, sum(k.exptime)
from kindata as k, observations as o, RESOLVEmasterfile as r, dr8data as d
where o.flag = "TS" and
k.paflag = 1 and k.obsshift = "Z" and
k.name = o.name and
k.name = r.name and
r.dr8specobjid = d.specobjid
group by k.name'''
data = sq.get_data_sqliteSMNfunctions(path, db, query)
xsalt = data[:, 0]
xsalterr = data[:, 1]
ysalt = data[:, 2]
#make the figure
fig = P.figure()
ax1 = fig.add_subplot(111)
ax1.errorbar(xcomp, ycomp, xerr=xcomperr, ls='None', c='b',
marker='s', ms=3, label='Completed (C)')
ax1.errorbar(xsalt, ysalt, xerr=xsalterr, ls='None', c='r',
marker='o', ms=3, label='SALT? (TS)')
ax1.set_xlabel(r'$H_{\alpha}$ [1e-17 erg/s/cm$^{2}$]')
ax1.set_ylabel(r'EXPTIME [min]')
ax1.set_xscale('log')
ax1.set_xlim(5, 1e4)
P.legend(shadow=True, fancybox=True, numpoints=1)
P.savefig(out_folder + output)
def getIds(column, database='catalogs.db'):
"""
Recover ids from RESOLVEmasterfile table.
:param column: name of the id column
:type column: string
:param database: name of the SQLite3 database file
:type database: string
:return: ids
:rtype: ndarray
"""
query = 'SELECT %s from RESOLVEmasterfile where %s > 0' % (column, column)
data = sq.get_data_sqliteSMNfunctions('./', database, query, toNumpy=False)
return data
def getIds(column, database='catalogs.db'):
"""
Recover ids from RESOLVEmasterfile table.
:param column: name of the id column
:type column: string
:param database: name of the SQLite3 database file
:type database: string
:return: ids
:rtype: ndarray
"""
query = 'SELECT %s from RESOLVEmasterfile where %s > 0' % (column, column)
data = sq.get_data_sqliteSMNfunctions('./', database, query, toNumpy=False)
return data
def simplePlot(query, xlabel, ylabel, output, out_folder):
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
col = data[:, 0]
ew = data[:, 1]
ewerr = data[:, 2]
#make the figure
fig = P.figure()
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(111)
ax1.errorbar(col, ew, yerr=ewerr, ls='None', marker='s', ms=2)
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
P.savefig(out_folder + output)
def quickTest(id=1242422,
output='SingleHalo.pdf',
db='database.db',
path='/Users/sammy/Research/CANDELS/v2/'):
"""
A single halo test.
:param id: halo_id of the testable dark matter halo
:type id: int
:param output: name of the output file
:type output: string
:param db: name of the SQLite database file
:type db: string
:param path: full path of the database file
:type path: string
:return: None
"""
#pull out data
query = 'select redshift, ra, dec, mhalo, rhalo from lcone where halo_id = {0:d}'.format(
id)
data = sq.get_data_sqliteSMNfunctions(path, db, query)
#redshift
z = data[0, 0]
#calculate the separations
inp = dict(RA1=data[0, 1],
DEC1=data[0, 2],
RA2=data[1:, 1],
DEC2=data[1:, 2])
result = conv.physicalSeparation(inp, z)
print result
#make a plot
text = r'halo\_id=%i, z=%.3f, mass=%.2f, scale=%.3f' % (id, z, data[0, 3],
result['scale'])
P.figure(figsize=(12, 12))
ax1 = P.subplot(221)
ax2 = P.subplot(222)
ax3 = P.subplot(223)
ax4 = P.subplot(224)
ax1.hist(result['distance'] / (1e3 * data[0, 4]), bins=12)
ax2.hist(result['distance'], bins=12)
RAs, DECs = (inp['RA2'] - inp['RA1']) * 1e4, (inp['DEC2'] -
inp['DEC1']) * 1e4
ax3.scatter(RAs, DECs, c='b', s=100, marker='o', alpha=0.45)
ax3.scatter(0, 0, c='k', s=120, marker='s')
s4 = ax4.scatter(RAs,
DECs,
c=data[1:, 3],
s=80,
marker='o',
alpha=0.6,
cmap=cm.get_cmap('jet'),
vmin=9.5,
vmax=11.0)
ax4.scatter(0, 0, c='k', s=120, marker='s')
inset_axes = inset_locator.inset_axes(ax4,
width='80%',
height='1.5%',
loc=9)
c = P.colorbar(s4, cax=inset_axes, orientation='horizontal')
c.set_label(r'$\log_{10} \left( M_{dm} [\mathrm{M}_{\odot}] \right )$',
fontsize=13)
for j in c.ax.get_xticklabels():
j.set_fontsize(12)
ax1.set_xlabel(r'Projected Distance / Radius of the Main Halo')
ax2.set_xlabel(r'Projected Distance [kpc]')
ax3.set_xlabel(r'$\Delta$RA [$10^{-4}$ deg]')
ax4.set_xlabel(r'$\Delta$RA [$10^{-4}$ deg]')
ax3.set_ylabel(r'$\Delta$DEC [$10^{-4}$ deg]')
P.annotate(text, (0.5, 0.95),
xycoords='figure fraction',
ha='center',
va='center',
fontsize=12)
P.savefig(output)
def plotMergerFractionsMultiplot2(query,
xlabel,
ylabel,
output,
out_folder,
mergetimelimit=0.25,
ymin=-0.1,
ymax=1.1,
xmin=-1.9,
xmax=4.15,
xbins=80,
ybins=80,
title='Simulated Galaxies',
size=4.5,
alpha=0.2):
#get data, all galaxies
"""
Plots merger fraction
"""
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 for simulated galaxies
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(211)
ax2 = fig.add_subplot(212)
#make contours
lv = N.linspace(0.015, 0.95 * N.max(zm), 5)
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')
#plot scatters
s2 = ax1.scatter(N.log10(x[majorsMask]),
uvcolor[majorsMask],
s=size,
c=1000. * tmajor[majorsMask],
marker='o',
cmap=cm.get_cmap('jet'),
edgecolor='none',
label='Major Merger: $T \leq %.0f$ Myr' %
(mergetimelimit * 1000.),
alpha=alpha)
s2 = ax1.scatter(N.log10(x[majorsMask]),
uvcolor[majorsMask],
s=size,
c=1000. * tmajor[majorsMask],
marker='o',
cmap=cm.get_cmap('jet'),
edgecolor='none',
visible=False)
s4 = ax2.scatter(N.log10(x[mergersMask]),
uvcolor[mergersMask],
s=size * 1.8,
c=1000. * tmerge[mergersMask],
marker='^',
cmap=cm.get_cmap('jet'),
edgecolor='none',
label='Minor Merger: $T \leq %.0f$ Myr' %
(mergetimelimit * 1000.),
alpha=alpha)
s4 = ax2.scatter(N.log10(x[mergersMask]),
uvcolor[mergersMask],
s=size * 1.8,
c=1000. * tmerge[mergersMask],
marker='^',
cmap=cm.get_cmap('jet'),
edgecolor='none',
visible=False)
#color bars
c1 = fig.colorbar(s2, ax=ax1, shrink=0.7, fraction=0.05)
c2 = fig.colorbar(s4, ax=ax2, shrink=0.7, fraction=0.05)
c1.set_label('Time since latest merger [Myr]')
c2.set_label('Time since latest merger [Myr]')
#plot dividing line
x = N.array([2.0, 2.5, 3.0, 4.0])
y = x - 2.4
ax1.plot(x, y, 'g--', lw=1.8)
ax2.plot(x, y, 'g--', lw=1.8)
#add annotate
P.text(0.5,
1.04,
title,
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#labels
ax1.set_ylabel(ylabel)
ax2.set_ylabel(ylabel)
ax2.set_xlabel(xlabel)
ax1.set_xticklabels([])
#limits
ax1.set_ylim(ymin, ymax)
ax1.set_xlim(xmin, xmax)
ax2.set_ylim(ymin, ymax)
ax2.set_xlim(xmin, xmax)
#legend and save
ax1.legend(loc='upper left',
scatterpoints=1,
shadow=True,
fancybox=True,
markerscale=1.5)
ax2.legend(loc='upper left',
scatterpoints=1,
shadow=True,
fancybox=True,
markerscale=1.5)
P.savefig(out_folder + output)
def plotMergerFractionsMultiplot2(query, xlabel, ylabel,
output, out_folder,
mergetimelimit=0.25,
ymin=-0.1, ymax=1.1,
xmin=-1.9, xmax=4.15,
xbins=80, ybins=80,
title='Simulated Galaxies',
size=4.5, alpha=0.2):
#get data, all galaxies
"""
Plots merger fraction
"""
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 for simulated galaxies
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(211)
ax2 = fig.add_subplot(212)
#make contours
lv = N.linspace(0.015, 0.95 * N.max(zm), 5)
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')
#plot scatters
s2 = ax1.scatter(N.log10(x[majorsMask]), uvcolor[majorsMask],
s=size, c=1000. * tmajor[majorsMask], marker='o',
cmap=cm.get_cmap('jet'), edgecolor='none',
label='Major Merger: $T \leq %.0f$ Myr' % (mergetimelimit * 1000.),
alpha=alpha)
s2 = ax1.scatter(N.log10(x[majorsMask]), uvcolor[majorsMask],
s=size, c=1000. * tmajor[majorsMask], marker='o',
cmap=cm.get_cmap('jet'), edgecolor='none',
visible=False)
s4 = ax2.scatter(N.log10(x[mergersMask]), uvcolor[mergersMask],
s=size * 1.8, c=1000. * tmerge[mergersMask], marker='^',
cmap=cm.get_cmap('jet'), edgecolor='none',
label='Minor Merger: $T \leq %.0f$ Myr' % (mergetimelimit * 1000.),
alpha=alpha)
s4 = ax2.scatter(N.log10(x[mergersMask]), uvcolor[mergersMask],
s=size * 1.8, c=1000. * tmerge[mergersMask], marker='^',
cmap=cm.get_cmap('jet'), edgecolor='none',
visible=False)
#color bars
c1 = fig.colorbar(s2, ax=ax1, shrink=0.7, fraction=0.05)
c2 = fig.colorbar(s4, ax=ax2, shrink=0.7, fraction=0.05)
c1.set_label('Time since latest merger [Myr]')
c2.set_label('Time since latest merger [Myr]')
#plot dividing line
x = N.array([2.0, 2.5, 3.0, 4.0])
y = x - 2.4
ax1.plot(x, y, 'g--', lw=1.8)
ax2.plot(x, y, 'g--', lw=1.8)
#add annotate
P.text(0.5, 1.04, title,
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
#labels
ax1.set_ylabel(ylabel)
ax2.set_ylabel(ylabel)
ax2.set_xlabel(xlabel)
ax1.set_xticklabels([])
#limits
ax1.set_ylim(ymin, ymax)
ax1.set_xlim(xmin, xmax)
ax2.set_ylim(ymin, ymax)
ax2.set_xlim(xmin, xmax)
#legend and save
ax1.legend(loc='upper left', scatterpoints=1,
shadow=True, fancybox=True, markerscale=1.5)
ax2.legend(loc='upper left', scatterpoints=1,
shadow=True, fancybox=True, markerscale=1.5)
P.savefig(out_folder + output)
def plotMergerFractionsMultiplot(query,
xlabel,
ylabel,
output,
out_folder,
obs,
mergetimelimit=0.25,
ymin=-0.2,
ymax=1.5,
xmin=-1.9,
xmax=4.15,
xbins=80,
ybins=80,
title='Simulated Galaxies',
size=4.5,
alpha=0.2,
ch=1):
#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 for simulated galaxies
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(0.015, 0.95 * N.max(zm), 5)
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
s2 = ax2.scatter(N.log10(x[majorsMask]),
uvcolor[majorsMask],
s=size,
c=1000. * tmajor[majorsMask],
marker='o',
cmap=cm.get_cmap('jet'),
edgecolor='none',
label='Major Merger: $T \leq %.0f$ Myr' %
(mergetimelimit * 1000.),
alpha=alpha)
s2 = ax2.scatter(N.log10(x[majorsMask]),
uvcolor[majorsMask],
s=size,
c=1000. * tmajor[majorsMask],
marker='o',
cmap=cm.get_cmap('jet'),
edgecolor='none',
visible=False)
s4 = ax4.scatter(N.log10(x[mergersMask]),
uvcolor[mergersMask],
s=size * 1.8,
c=1000. * tmerge[mergersMask],
marker='^',
cmap=cm.get_cmap('jet'),
edgecolor='none',
label='Minor Merger: $T \leq %.0f$ Myr' %
(mergetimelimit * 1000.),
alpha=alpha)
s4 = ax4.scatter(N.log10(x[mergersMask]),
uvcolor[mergersMask],
s=size * 1.8,
c=1000. * tmerge[mergersMask],
marker='^',
cmap=cm.get_cmap('jet'),
edgecolor='none',
visible=False)
#Observed
c = sex.sextractor(
'/Users/sammy/workspace/herschelpaper/Kuang/section5_v5/f250_detect_z2-4_VC.txt'
)
c24 = sex.sextractor(
'/Users/sammy/workspace/herschelpaper/Kuang/section5_v5/goodsh_goodsn_acsmatch_unified_z2-4.cat'
)
VC = []
for i in range(len(c24)):
VC += [N.compress(c.id_zband == c24.id_zband[i], c.vcflag)[0]]
VC = N.array(VC)
# enforce upper limits in ch2 flux
fch2 = c24.ch2_flux_ujy.copy()
for i in range(len(fch2)):
if c24.ch2_flux_ujy[i] < c24.ch2_fluxerr_ujy[i]:
fch2[i] = c24.ch2_fluxerr_ujy[i]
if fch2[i] < 0.:
fch2[
i] = 0.5 # set a upper limit in ch2 of 0.5 uJy (it's the largest flux error in the sample; reasonable?)
# enforce upper limits in i-band flux
f_i = c24.i_flux_ujy.copy()
for i in range(len(c24)):
if c24.i_flux_ujy[i] < c24.i_fluxerr_ujy[i]:
f_i[i] = c24.i_fluxerr_ujy[i]
f_z = c24.z_flux_ujy.copy()
imag = ujy_to_abmag(f_i)
zmag = ujy_to_abmag(f_z)
imz = imag - zmag
l1 = ax1.scatter(N.compress(VC == 'a',
N.log10(c24.f250_mjy * 1000. / fch2)),
N.compress(VC == 'a', imz),
marker='x',
s=35,
color='black',
label='Regulars')
l2 = ax1.scatter(N.compress(VC == 'b',
N.log10(c24.f250_mjy * 1000. / fch2)),
N.compress(VC == 'b', imz),
marker='o',
s=35,
color='red',
label='Mergers')
l3 = ax1.scatter(N.compress(VC == 'c',
N.log10(c24.f250_mjy * 1000. / fch2)),
N.compress(VC == 'c', imz),
marker='^',
s=35,
color='gray',
label='Ambiguous')
l4 = ax1.scatter(N.compress(VC == 'd',
N.log10(c24.f250_mjy * 1000. / fch2)),
N.compress(VC == 'd', imz),
marker='s',
s=35,
color='blue',
label='Compact core')
zlim = 25.7
if sum(zmag > zlim) > 0:
l5 = ax1.scatter(N.compress(zmag > zlim,
N.log10(c24.f250_mjy * 1000. / fch2)),
N.compress(zmag > zlim, imz),
marker='o',
facecolor='None',
edgecolor='black',
s=64,
label=r'$z_{850}>%.1f$' % zlim)
l1 = ax3.scatter(N.compress(VC == 'a',
N.log10(c24.f250_mjy * 1000. / fch2)),
N.compress(VC == 'a', imz),
marker='x',
s=35,
color='black',
label='Regulars')
l2 = ax3.scatter(N.compress(VC == 'b',
N.log10(c24.f250_mjy * 1000. / fch2)),
N.compress(VC == 'b', imz),
marker='o',
s=35,
color='red',
label='Mergers')
l3 = ax3.scatter(N.compress(VC == 'c',
N.log10(c24.f250_mjy * 1000. / fch2)),
N.compress(VC == 'c', imz),
marker='^',
s=35,
color='gray',
label='Ambiguous')
l4 = ax3.scatter(N.compress(VC == 'd',
N.log10(c24.f250_mjy * 1000. / fch2)),
N.compress(VC == 'd', imz),
marker='s',
s=35,
color='blue',
label='Compact core')
zlim = 25.7
if sum(zmag > zlim) > 0:
l5 = ax3.scatter(N.compress(zmag > zlim,
N.log10(c24.f250_mjy * 1000. / fch2)),
N.compress(zmag > zlim, imz),
marker='o',
facecolor='None',
edgecolor='black',
s=64,
label=r'$z_{850}>%.1f$' % zlim)
#color bars
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 latest merger [Myr]')
c2.set_label('Time since latest merger [Myr]')
#plot dividing line
# y = x - 2.5
x = N.array([2.0, 2.5, 3.0, 4.0])
y = x - 2.7
ax1.plot(x, y, 'g--')
ax2.plot(x, y, 'g--')
ax3.plot(x, y, 'g--')
ax4.plot(x, y, 'g--')
#add annotate
P.text(0.5,
1.04,
title,
horizontalalignment='center',
verticalalignment='center',
transform=ax2.transAxes)
P.text(0.5,
1.04,
'Observed Galaxies',
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 right',
scatterpoints=1,
shadow=True,
fancybox=True,
markerscale=1.5)
ax2.legend(loc='upper left',
scatterpoints=1,
shadow=True,
fancybox=True,
markerscale=1.5)
ax3.legend(loc='upper right',
scatterpoints=1,
shadow=True,
fancybox=True,
markerscale=1.5)
ax4.legend(loc='upper left',
scatterpoints=1,
shadow=True,
fancybox=True,
markerscale=1.5)
P.savefig(out_folder + output)
def plotMergerFractions3(query,
xlabel, ylabel,
output, out_folder,
mergetimelimit=0.4,
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)
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=0.1,
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='0.5', width=wd)#, hatch = '/')
ax1.bar(mids, mergerFraction2, align='center',
bottom=noMergerFraction,
label='Minor Merger: $T > %.0f$ Myr' % (mergetimelimit * 1000.),
color='0.7', width=wd)#, hatch = '|')
ax1.bar(mids, majorMergerFraction2, align='center',
bottom=noMergerFraction + mergerFraction2,
label='Major Merger: $T > %.0f$ Myr' % (mergetimelimit * 1000.),
color='0.9', width=wd)#, hatch = '-')
ax1.bar(mids, majorMergerFraction, align='center',
bottom=noMergerFraction + mergerFraction2 + majorMergerFraction2,
label='Major Merger: $T \leq %.0f$ Myr' % (mergetimelimit * 1000.),
color='green', width=wd)#, hatch = '.')
ax1.bar(mids, mergerFraction,
bottom=noMergerFraction + mergerFraction2 + majorMergerFraction2 + majorMergerFraction,
align='center',
label='Minor Merger: $T \leq %.0f$ Myr' % (mergetimelimit * 1000.),
color='red', width=wd)#, hatch = 'x')
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
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='lower left')
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=-0.001,
ymax=1.01,
logscale=False):
"""
Plot merger fractions.
"""
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
#mstar = N.log10(data[:, 0])
mstar = data[:, 0]
tmerge = data[:, 1]
tmajor = data[:, 2]
print N.min(mstar), N.max(mstar)
#masks
nomergeMask = tmerge < 0.0
majorsMask = (tmajor > 0.0) & (tmajor <= mergetimelimit)
majorsMask2 = (tmajor > mergetimelimit) & (tmajor <= 0.5)
mergersMask = (tmerge > 0.0) & (tmerge <= mergetimelimit) &\
(majorsMask == False) & (majorsMask2 == False)
mergersMask2 = (tmerge > mergetimelimit) & (tmerge <= 0.5) &\
(majorsMask2 == False)
#bin the data
mids, numbs = dm.binAndReturnMergerFractions2(mstar,
nomergeMask,
mergersMask,
majorsMask,
mergersMask2,
majorsMask2,
mstarmin,
mstarmax,
mbins,
logscale)
#the fraction of mergers
noMergerFraction = [float(x[1]) / x[0] for x in numbs]
mergerFraction = [float(x[2]) / x[0] for x in numbs]
majorMergerFraction = [float(x[3]) / x[0] for x in numbs]
mergerFraction2 = [float(x[4]) / x[0] for x in numbs]
majorMergerFraction2 = [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
if 'ps' in output:
fig = P.figure()
else:
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: $250 < T \leq 500$ 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: $250 < T \leq 500$ Myr')
ax1.plot(mids, majorMergerFraction, ls='-', lw=2.6, c='0.3',
label='Young Major Merger')
ax1.plot(mids, mergerFraction, ls='-.', lw=2.6, c='0.2',
label='Young Minor Merger')
ax1.plot(mids, majorMergerFraction2, ls='--', lw=2.6, c='0.4',
label='Old Major Merger')
ax1.plot(mids, mergerFraction2, ls=':', lw=2.6, c='0.5',
label='Old Minor Merger')
#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)
#legend and save
P.legend(loc='upper left')
P.savefig(out_folder + output)
def plot_mergerfraction(path,
db,
reshifts,
out_folder,
outname,
xmin=-0.01,
xmax=2.3,
fluxlimit=5,
png=True,
mergetimelimit=0.25,
xbin=[10, 8, 9, 7, 7, 5],
neverMerged=False,
obs=True):
'''
Plots
'''
#figure
if png:
fig = P.figure(figsize=(10, 10))
type = '.png'
else:
fig = P.figure()
type = '.ps'
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)
#loop over all the redshifts
for i, reds in enumerate(redshifts):
if obs:
query = '''select FIR.pacs160_obs, galprop.tmerge, galprop.tmajmerge
from FIR, galprop where
%s and
FIR.pacs160_obs > 5e-4 and
FIR.pacs160_obs < 1e6 and
FIR.gal_id = galprop.gal_id and
FIR.halo_id = galprop.halo_id
''' % reds
else:
query = '''select FIR.pacs160, galprop.tmerge, galprop.tmajmerge
from FIR, galprop where
%s and
FIR.pacs160 > 8.8 and
FIR.pacs160 < 13.0 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_sqliteSMNfunctions(path, db, query)
if obs:
x = N.log10(data[:, 0] * 1e3)
else:
x = data[:, 0]
tmerge = data[:, 1]
tmajor = data[:, 2]
print N.min(x), N.max(x)
#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)
print xbin[i]
mids, numbs = dm.binAndReturnMergerFractions2(x, nomergeMask,
mergersMask, majorsMask,
mergersMask2,
majorsMask2, N.min(x),
N.max(x), xbin[i], False)
#the fraction of mergers
noMergerFraction = [float(x[1]) / x[0] for x in numbs]
mergerFraction = [float(x[2]) / x[0] for x in numbs]
majorMergerFraction = [float(x[3]) / x[0] for x in numbs]
mergerFraction2 = [float(x[4]) / x[0] for x in numbs]
majorMergerFraction2 = [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
#plots
ax1.plot(mids, majorMergerFraction, label='$z = %.1f$' % zz)
ax2.plot(mids, majorMergerFraction2, label='$z = %.1f$' % zz)
ax3.plot(mids, mergerFraction, label='$z = %.1f$' % zz)
if neverMerged:
ax4.plot(mids, noMergerFraction, label='$z = %.1f$' % zz)
else:
ax4.plot(mids, mergerFraction2, label='$z = %.1f$' % zz)
#set obs limit
if obs:
ax1.axvline(N.log10(fluxlimit), ls=':', color='green')
ax2.axvline(N.log10(fluxlimit), ls=':', color='green')
ax3.axvline(N.log10(fluxlimit), ls=':', color='green')
ax4.axvline(N.log10(fluxlimit), ls=':', color='green')
#labels
if obs:
ax3.set_xlabel('$\log_{10}(S_{160} \ [\mathrm{mJy}])$')
ax4.set_xlabel('$\log_{10}(S_{160} \ [\mathrm{mJy}])$')
else:
ax3.set_xlabel('$\log_{10}(L_{160} \ [L_{\odot}])$')
ax4.set_xlabel('$\log_{10}(L_{160} \ [L_{\odot}])$')
ax1.set_ylabel('$\mathrm{Merger\ Fraction}$')
ax3.set_ylabel('$\mathrm{Merger\ Fraction}$')
ax2.set_yticklabels([])
ax4.set_yticklabels([])
ax1.set_xticklabels([])
ax2.set_xticklabels([])
#texts
P.text(
0.5,
0.94,
'$\mathrm{Major\ mergers:}\ T_{\mathrm{merge}} \leq %i \ \mathrm{Myr}$'
% (mergetimelimit * 1000.),
horizontalalignment='center',
verticalalignment='center',
transform=ax1.transAxes)
P.text(
0.5,
0.94,
'$\mathrm{Major\ mergers:}\ T_{\mathrm{merge}} > %i \ \mathrm{Myr}$' %
(mergetimelimit * 1000.),
horizontalalignment='center',
verticalalignment='center',
transform=ax2.transAxes)
P.text(
0.5,
0.94,
'$\mathrm{Minor\ mergers:}\ T_{\mathrm{merge}} \leq %i \ \mathrm{Myr}$'
% (mergetimelimit * 1000.),
horizontalalignment='center',
verticalalignment='center',
transform=ax3.transAxes)
if neverMerged:
P.text(0.5,
0.94,
'$\mathrm{Never\ Merged}$',
horizontalalignment='center',
verticalalignment='center',
transform=ax4.transAxes)
else:
P.text(
0.5,
0.94,
'$\mathrm{Minor\ mergers:}\ T_{\mathrm{merge}} > %i \ \mathrm{Myr}$'
% (mergetimelimit * 1000.),
horizontalalignment='center',
verticalalignment='center',
transform=ax4.transAxes)
#set limits
ax1.set_xlim(xmin, xmax)
ax1.set_ylim(-0.01, 0.95)
ax2.set_xlim(xmin, xmax)
ax2.set_ylim(-0.01, 0.95)
ax3.set_xlim(xmin, xmax)
ax3.set_ylim(-0.01, 0.95)
ax4.set_xlim(xmin, xmax)
ax4.set_ylim(-0.01, 0.95)
#make legend and save the figure
ax3.legend(loc='center left')
P.savefig(out_folder + outname + type)
mergetimelimit = 0.5
query2 = '''select galprop.tmerge, galprop.tmajmerge
from FIR, galprop, galphotdust where
galphotdust.f775w - galphotdust.f850lp < 0.2 and
FIR.spire250_obs > 5e-3 and
FIR.z >= 2.0 and
FIR.z < 4.0 and
FIR.spire250_obs < 1e6 and
FIR.gal_id = galprop.gal_id and
FIR.halo_id = galprop.halo_id and
FIR.gal_id = galphotdust.gal_id and
FIR.halo_id = galphotdust.halo_id
'''
#get data
data = sq.get_data_sqliteSMNfunctions(path, db, query2)
tmerge = data[:, 0]
tmajor = data[:, 1]
#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)
#the fraction of no mergers?
nm2 = len(tmerge[tmerge < 0.0]) / float(len(tmerge)) * 100.
nm3 = len(tmajor[tmajor < 0.0]) / float(len(tmajor)) * 100.
nm4 = len(tmajor[majorsMask]) / float(len(tmajor)) * 100.
#print out some statistics
mergetimelimit = 0.5
query2 = '''select galprop.tmerge, galprop.tmajmerge
from FIR, galprop, galphotdust where
galphotdust.f775w - galphotdust.f850lp < 0.2 and
FIR.spire250_obs > 5e-3 and
FIR.z >= 2.0 and
FIR.z < 4.0 and
FIR.spire250_obs < 1e6 and
FIR.gal_id = galprop.gal_id and
FIR.halo_id = galprop.halo_id and
FIR.gal_id = galphotdust.gal_id and
FIR.halo_id = galphotdust.halo_id
'''
#get data
data = sq.get_data_sqliteSMNfunctions(path, db, query2)
tmerge = data[:,0]
tmajor = data[:,1]
#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)
#the fraction of no mergers?
nm2 = len(tmerge[tmerge < 0.0]) / float(len(tmerge)) * 100.
nm3 = len(tmajor[tmajor < 0.0]) / float(len(tmajor)) * 100.
nm4 = len(tmajor[majorsMask]) / float(len(tmajor)) * 100.
#print out some statistics
def plotMergerFractionsMultiplot(query, xlabel, ylabel,
output, out_folder, obs,
mergetimelimit=0.25,
ymin=-0.2, ymax=1.5,
xmin=-1.9, xmax=4.15,
xbins=80, ybins=80,
title='Simulated Galaxies',
size=4.5, alpha=0.2, ch=1):
#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 for simulated galaxies
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(0.015, 0.95 * N.max(zm), 5)
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
s2 = ax2.scatter(N.log10(x[majorsMask]), uvcolor[majorsMask],
s=size, c=1000. * tmajor[majorsMask], marker='o',
cmap=cm.get_cmap('jet'), edgecolor='none',
label='Major Merger: $T \leq %.0f$ Myr' % (mergetimelimit * 1000.),
alpha=alpha)
s2 = ax2.scatter(N.log10(x[majorsMask]), uvcolor[majorsMask],
s=size, c=1000. * tmajor[majorsMask], marker='o',
cmap=cm.get_cmap('jet'), edgecolor='none',
visible=False)
s4 = ax4.scatter(N.log10(x[mergersMask]), uvcolor[mergersMask],
s=size * 1.8, c=1000. * tmerge[mergersMask], marker='^',
cmap=cm.get_cmap('jet'), edgecolor='none',
label='Minor Merger: $T \leq %.0f$ Myr' % (mergetimelimit * 1000.),
alpha=alpha)
s4 = ax4.scatter(N.log10(x[mergersMask]), uvcolor[mergersMask],
s=size * 1.8, c=1000. * tmerge[mergersMask], marker='^',
cmap=cm.get_cmap('jet'), edgecolor='none',
visible=False)
#Observed
c = sex.sextractor('/Users/sammy/workspace/herschelpaper/Kuang/section5_v5/f250_detect_z2-4_VC.txt')
c24 = sex.sextractor(
'/Users/sammy/workspace/herschelpaper/Kuang/section5_v5/goodsh_goodsn_acsmatch_unified_z2-4.cat')
VC = []
for i in range(len(c24)):
VC += [N.compress(c.id_zband == c24.id_zband[i], c.vcflag)[0]]
VC = N.array(VC)
# enforce upper limits in ch2 flux
fch2 = c24.ch2_flux_ujy.copy()
for i in range(len(fch2)):
if c24.ch2_flux_ujy[i] < c24.ch2_fluxerr_ujy[i]:
fch2[i] = c24.ch2_fluxerr_ujy[i]
if fch2[i] < 0.: fch2[
i] = 0.5 # set a upper limit in ch2 of 0.5 uJy (it's the largest flux error in the sample; reasonable?)
# enforce upper limits in i-band flux
f_i = c24.i_flux_ujy.copy()
for i in range(len(c24)):
if c24.i_flux_ujy[i] < c24.i_fluxerr_ujy[i]:
f_i[i] = c24.i_fluxerr_ujy[i]
f_z = c24.z_flux_ujy.copy()
imag = ujy_to_abmag(f_i)
zmag = ujy_to_abmag(f_z)
imz = imag - zmag
l1 = ax1.scatter(N.compress(VC == 'a', N.log10(c24.f250_mjy * 1000. / fch2)), N.compress(VC == 'a', imz),
marker='x', s=35, color='black', label='Regulars')
l2 = ax1.scatter(N.compress(VC == 'b', N.log10(c24.f250_mjy * 1000. / fch2)), N.compress(VC == 'b', imz),
marker='o', s=35, color='red', label='Mergers')
l3 = ax1.scatter(N.compress(VC == 'c', N.log10(c24.f250_mjy * 1000. / fch2)), N.compress(VC == 'c', imz),
marker='^', s=35, color='gray', label='Ambiguous')
l4 = ax1.scatter(N.compress(VC == 'd', N.log10(c24.f250_mjy * 1000. / fch2)), N.compress(VC == 'd', imz),
marker='s', s=35, color='blue', label='Compact core')
zlim = 25.7
if sum(zmag > zlim) > 0:
l5 = ax1.scatter(N.compress(zmag > zlim, N.log10(c24.f250_mjy * 1000. / fch2)),
N.compress(zmag > zlim, imz), marker='o', facecolor='None', edgecolor='black', s=64,
label=r'$z_{850}>%.1f$' % zlim)
l1 = ax3.scatter(N.compress(VC == 'a', N.log10(c24.f250_mjy * 1000. / fch2)), N.compress(VC == 'a', imz),
marker='x', s=35, color='black', label='Regulars')
l2 = ax3.scatter(N.compress(VC == 'b', N.log10(c24.f250_mjy * 1000. / fch2)), N.compress(VC == 'b', imz),
marker='o', s=35, color='red', label='Mergers')
l3 = ax3.scatter(N.compress(VC == 'c', N.log10(c24.f250_mjy * 1000. / fch2)), N.compress(VC == 'c', imz),
marker='^', s=35, color='gray', label='Ambiguous')
l4 = ax3.scatter(N.compress(VC == 'd', N.log10(c24.f250_mjy * 1000. / fch2)), N.compress(VC == 'd', imz),
marker='s', s=35, color='blue', label='Compact core')
zlim = 25.7
if sum(zmag > zlim) > 0:
l5 = ax3.scatter(N.compress(zmag > zlim, N.log10(c24.f250_mjy * 1000. / fch2)),
N.compress(zmag > zlim, imz), marker='o', facecolor='None', edgecolor='black', s=64,
label=r'$z_{850}>%.1f$' % zlim)
#color bars
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 latest merger [Myr]')
c2.set_label('Time since latest merger [Myr]')
#plot dividing line
# y = x - 2.5
x = N.array([2.0, 2.5, 3.0, 4.0])
y = x - 2.7
ax1.plot(x, y, 'g--')
ax2.plot(x, y, 'g--')
ax3.plot(x, y, 'g--')
ax4.plot(x, y, 'g--')
#add annotate
P.text(0.5, 1.04, title,
horizontalalignment='center',
verticalalignment='center',
transform=ax2.transAxes)
P.text(0.5, 1.04, 'Observed Galaxies',
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 right', scatterpoints=1,
shadow=True, fancybox=True, markerscale=1.5)
ax2.legend(loc='upper left', scatterpoints=1,
shadow=True, fancybox=True, markerscale=1.5)
ax3.legend(loc='upper right', scatterpoints=1,
shadow=True, fancybox=True, markerscale=1.5)
ax4.legend(loc='upper left', scatterpoints=1,
shadow=True, fancybox=True, markerscale=1.5)
P.savefig(out_folder + output)
def getDataSlow(db='database.db', path='/Users/sammy/Research/CANDELS/v2/'):
"""
Get data from the lcone table in db.
:param db: name of the SQLite database file.
:type db: string
:param path: full path of the database file
:type path: string
:Warning: This is extremely slow way to pull out data from a database.
Should be rewritten using table joins etc. so that less queries
could be performed.
:return: redshift, halo mass and radius, projected distances of subhalo galaxies [kpc],
halo_id of the main halo
:rtype: dict
"""
conversion = 0.000277777778 # degree to arcsecond
redshifts = [
'redshift < 0.5 and', 'redshift >= 0.5 and redshift < 1.0 and',
'redshift >= 1.0 and redshift < 2.0 and',
'redshift >= 2.0 and redshift < 3.0 and',
'redshift >= 3.0 and redshift < 4.0 and',
'redshift >= 4.0 and redshift < 5.0 and',
'redshift >= 5.0 and redshift < 6.0 and',
'redshift >= 6.0 and redshift < 7.0 and'
]
for i, red in enumerate(redshifts):
print red
qr = 'select halo_id from lcone where %s gal_id > 1' % red
#qr = 'select halo_id from lcone where %s mhalo > 12.7' % red
#pull out data
ids = sq.get_data_sqliteSMNfunctions(path, db, qr)
uids = np.unique(ids)
print len(uids), 'haloes'
saveid = []
saveorig = []
savedist = []
savemhalo = []
saverhalo = []
saveredshift = []
#we should now look for each unique id
for id in uids:
query = 'select redshift, ra, dec, halo_id, mhalo, rhalo from lcone where halo_id = {0:d}'.format(
id)
#print query
data = sq.get_data_sqliteSMNfunctions(path, db, query)
#if multiples, then don't take it
if len(set(data[:, 0])) > 1:
print 'skipping', id
continue
if len(data[:, 1]) < 2:
print 'no subhaloes', id, data[:, 1]
continue
#redshift
z = data[0, 0]
#look the diameter distance from the lookup table
dd = cosmocalc(z, 71.0, 0.28)['PS_kpc']
#the first halo, assume it to be the main halo
RADeg1 = data[0, 1]
decDeg1 = data[0, 2]
#the following haloes, assume to be subhaloes
RADeg2 = data[1:, 1]
decDeg2 = data[1:, 2]
#calculate the angular separation on the sky
sep = Coords.calcAngSepDeg(RADeg1, decDeg1, RADeg2, decDeg2)
physical_distance = sep * dd / conversion
#these are all the main halo parameters
saveredshift.append(z)
saveid.append(int(data[0, 3]))
savemhalo.append(data[0, 4])
saverhalo.append(data[0, 5])
savedist.append(physical_distance)
saveorig.append(id)
out = dict(halo_ids=saveid,
distances=savedist,
original=saveorig,
mhalo=savemhalo,
rhalo=saverhalo,
redshift=saveredshift)
wr.cPickleDumpDictionary(out, 'distances%i.pickle' % (i + 1))
def plotMergerFractions3(query,
xlabel,
ylabel,
output,
out_folder,
mergetimelimit=0.4,
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)
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=0.1, 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='0.5',
width=wd) #, hatch = '/')
ax1.bar(mids,
mergerFraction2,
align='center',
bottom=noMergerFraction,
label='Minor Merger: $T > %.0f$ Myr' % (mergetimelimit * 1000.),
color='0.7',
width=wd) #, hatch = '|')
ax1.bar(mids,
majorMergerFraction2,
align='center',
bottom=noMergerFraction + mergerFraction2,
label='Major Merger: $T > %.0f$ Myr' % (mergetimelimit * 1000.),
color='0.9',
width=wd) #, hatch = '-')
ax1.bar(mids,
majorMergerFraction,
align='center',
bottom=noMergerFraction + mergerFraction2 + majorMergerFraction2,
label='Major Merger: $T \leq %.0f$ Myr' % (mergetimelimit * 1000.),
color='green',
width=wd) #, hatch = '.')
ax1.bar(mids,
mergerFraction,
bottom=noMergerFraction + mergerFraction2 + majorMergerFraction2 +
majorMergerFraction,
align='center',
label='Minor Merger: $T \leq %.0f$ Myr' % (mergetimelimit * 1000.),
color='red',
width=wd) #, hatch = 'x')
#labels
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
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='lower left')
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=-0.001,
ymax=1.01,
logscale=False):
"""
Plot merger fractions.
"""
#get data, all galaxies
data = sq.get_data_sqliteSMNfunctions(path, db, query)
#mstar = N.log10(data[:, 0])
mstar = data[:, 0]
tmerge = data[:, 1]
tmajor = data[:, 2]
print N.min(mstar), N.max(mstar)
#masks
nomergeMask = tmerge < 0.0
majorsMask = (tmajor > 0.0) & (tmajor <= mergetimelimit)
majorsMask2 = (tmajor > mergetimelimit) & (tmajor <= 0.5)
mergersMask = (tmerge > 0.0) & (tmerge <= mergetimelimit) &\
(majorsMask == False) & (majorsMask2 == False)
mergersMask2 = (tmerge > mergetimelimit) & (tmerge <= 0.5) &\
(majorsMask2 == False)
#bin the data
mids, numbs = dm.binAndReturnMergerFractions2(mstar, nomergeMask,
mergersMask, majorsMask,
mergersMask2, majorsMask2,
mstarmin, mstarmax, mbins,
logscale)
#the fraction of mergers
noMergerFraction = [float(x[1]) / x[0] for x in numbs]
mergerFraction = [float(x[2]) / x[0] for x in numbs]
majorMergerFraction = [float(x[3]) / x[0] for x in numbs]
mergerFraction2 = [float(x[4]) / x[0] for x in numbs]
majorMergerFraction2 = [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
if 'ps' in output:
fig = P.figure()
else:
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: $250 < T \leq 500$ 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: $250 < T \leq 500$ Myr')
ax1.plot(mids,
majorMergerFraction,
ls='-',
lw=2.6,
c='0.3',
label='Young Major Merger')
ax1.plot(mids,
mergerFraction,
ls='-.',
lw=2.6,
c='0.2',
label='Young Minor Merger')
ax1.plot(mids,
majorMergerFraction2,
ls='--',
lw=2.6,
c='0.4',
label='Old Major Merger')
ax1.plot(mids,
mergerFraction2,
ls=':',
lw=2.6,
c='0.5',
label='Old Minor Merger')
#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)
#legend and save
P.legend(loc='upper left')
P.savefig(out_folder + output)
