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original1.group_lf.py
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# Plots for group LF paper
from array import array
import copy
import math
import matplotlib as mpl
import matplotlib.lines as mlines
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import matplotlib.ticker
import mpmath
import numpy as np
import os
import pdb
import pickle
import scipy.optimize
from scipy import stats
from astLib import astSED
from astropy.io import ascii
from astropy.modeling import models, fitting
from astropy import table
from astropy.table import Table, join
import healpy as hp
from sherpa.estmethods import Confidence
from sherpa.plot import RegionProjection
import gal_sample as gs
import lf
from schec import SchecMag, SchecMass
import util
# Global parameters
gama_data = os.environ['GAMA_DATA']
g3cgal = gama_data + 'g3cv9/G3CGalv08.fits'
g3cfof = gama_data + 'g3cv9/G3CFoFGroupv09.fits'
g3cmockfof = gama_data + 'g3cv6/G3CMockFoFGroupv06.fits'
g3cmockhalo = gama_data + 'g3cv6/G3CMockHaloGroupv06.fits'
g3cmockgal = gama_data + 'g3cv6/G3CMockGalv06.fits'
lf_data = os.environ['LF_DATA']
plot_dir = '/Users/loveday/Documents/tex/papers/gama/groupLF/'
mag_label = r'$^{0.1}M_r - 5 \log_{10} h$'
ms_label = r'$\log_{10}\ ({\cal M}_*/{\cal M}_\odot h^{-2})$'
lf_label = r'$\phi(M)\ [h^3\ {\rm Mpc}^{-3}\ {\rm mag}^{-1}]$'
smf_label = r'$\phi({\cal M}_*)\ [h^3\ {\rm Mpc}^{-3}\ {\rm dex}^{-1}]$'
clf_label = r'$\phi_C(M)\ [{\rm group}^{-1}\ {\rm mag}^{-1}]$'
csmf_label = r'$\phi_C({\cal M}_*)\ [{\rm group}^{-1}\ {\rm dex}^{-1}]$'
# Halo mass bin limits and means and cventral red fraction
#mbins = (12.0, 13.1, 13.4, 13.7, 14.0, 14.3, 16)
mbins_def = (12.0, 13.1, 13.3, 13.5, 13.7, 14.0, 15.2)
halomass = (12.91, 13.20, 13.40, 13.59, 13.82, 14.23)
crf = (0.66, 0.69, 0.76, 0.74, 0.74, 0.75)
# Constants
ln10 = math.log(10)
metadata_conflicts = 'silent' # Alternatives are 'warn', 'error'
# Ticks point inwards on all axes
mpl.rcParams['xtick.direction'] = 'in'
mpl.rcParams['ytick.direction'] = 'in'
mpl.rcParams['xtick.top'] = True
mpl.rcParams['ytick.right'] = True
mpl.rcParams['mathtext.fontset'] = 'dejavusans'
def mass_z(grpfile=g3cfof, nmin=5, edge_min=0.9, plot_file='mass_z.pdf',
zrange=(0, 0.5), Mrange=(11.8, 15.4), plot_size=(5, 4)):
"""Halo mass-redshift plot."""
# Read and select groups meeting selection criteria
t = Table.read(grpfile)
if 'sim' in grpfile:
sel = np.array(t['Nfof'] >= nmin)
else:
t['log_mass'] = 13.98 + 1.16*(np.log10(t['LumBfunc']) - 11.5)
sel = (np.array(t['GroupEdge'] > edge_min) *
np.logical_not(t['log_mass'].mask) *
np.array(t['Nfof'] >= nmin))
t = t[sel]
print('mass range of selected groups: {:5.2f} - {:5.2f}'.format(
np.min(t['log_mass']), np.max(t['log_mass'])))
plt.clf()
plt.scatter(t['IterCenZ'], t['log_mass'], s=2, c=t['Nfof'],
norm=mpl.colors.LogNorm())
plt.xlim(zrange)
for m in mbins_def:
plt.plot(zrange, (m, m), 'k-', linewidth=0.5)
plt.ylim(Mrange)
for z in (0.1, 0.2, 0.3):
plt.plot((z, z), Mrange, 'k-', linewidth=0.5)
plt.xlabel('Redshift')
plt.ylabel(r'$\log_{10}({\cal M}_h/{\cal M}_\odot h^{-1})$')
cb = plt.colorbar()
cb.set_label(r'$N_{\rm fof}$')
plt.draw()
fig = plt.gcf()
fig.set_size_inches(plot_size)
plt.savefig(plot_dir + plot_file, bbox_inches='tight')
plt.show()
def mass_comp(infile=g3cfof, nmin=5, edge_min=0.9, plot_file='mass_comp.pdf',
plot_size=(5, 4)):
"""Compare dynamical and luminosity-based halo mass estimates."""
# Read and select groups meeting selection criteria
t = Table.read(infile)
t['log_mass_lum'] = 13.98 + 1.16*(np.log10(t['LumBfunc']) - 11.5)
t['log_mass_dyn'] = np.log10(t['MassAfunc'])
sel = (np.array(t['GroupEdge'] > edge_min) *
np.logical_not(t['log_mass_lum'].mask) *
np.array(t['Nfof'] >= nmin))
t = t[sel]
plt.clf()
plt.scatter(t['log_mass_dyn'], t['log_mass_lum'], s=2,
c=t['IterCenZ'])
plt.axis((12, 15, 12, 15))
# plt.xlabel(r'$\log_{10} {\cal M}_{\rm lum}$')
# plt.ylabel(r'$\log_{10} {\cal M}_{\rm dyn}$')
plt.xlabel(r'$\lg {\cal M}_{\rm dyn}$')
plt.ylabel(r'$\lg {\cal M}_{\rm lum}$')
cb = plt.colorbar()
cb.set_label('Redshift')
plt.draw()
fig = plt.gcf()
fig.set_size_inches(plot_size)
plt.savefig(plot_dir + plot_file, bbox_inches='tight')
plt.show()
def mass_comp_mock(infile=g3cmockhalo, nmin=5, edge_min=0.9,
plot_file='mass_comp_mock.pdf', plot_size=(5, 9.6)):
"""Compare dynamical and luminosity-based halo mass estimates for mock groups."""
# Read and select groups meeting selection criteria
thalo = Table.read(g3cmockhalo)
thalo = thalo['HaloMass', 'IterCenRA', 'IterCenDEC']
tfof = Table.read(g3cmockfof)
t = join(thalo, tfof, keys=('IterCenRA', 'IterCenDEC'),
metadata_conflicts=metadata_conflicts)
t['log_mass_lum'] = 13.98 + 1.16*(np.log10(t['LumBfunc']) - 11.5)
t['log_mass_dyn'] = np.log10(t['MassAfunc'])
sel = (np.array(t['GroupEdge'] > edge_min) *
np.logical_not(t['log_mass_lum'].mask) *
np.array(t['Nfof'] >= nmin))
t = t[sel]
lgMh = np.log10(t['HaloMass'])
plt.clf()
fig, axes = plt.subplots(2, 1, sharex=True, sharey=True, num=1)
fig.subplots_adjust(hspace=0, wspace=0)
fig.set_size_inches(plot_size)
ax = axes[0]
# ax.scatter(lgMh, t['log_mass_dyn'], s=2, c=t['IterCenZ'])
ax.scatter(lgMh, t['log_mass_dyn'], s=1, c=t['Nfof'], vmax=10,
norm=mpl.colors.LogNorm())
ax.set_ylabel(r'$\lg {\cal M}_{\rm dyn}$')
ax = axes[1]
# sc = ax.scatter(lgMh, t['log_mass_lum'], s=2, c=t['IterCenZ'])
sc = ax.scatter(lgMh, t['log_mass_lum'], s=1, c=t['Nfof'], vmax=10,
norm=mpl.colors.LogNorm())
ax.set_ylabel(r'$\lg {\cal M}_{\rm lum}$')
ax.set_xlabel(r'$\lg {\cal M}_{\rm halo}$')
ax.axis((11.8, 15, 11.8, 15))
# ax.set_aspect('equal')
fig.subplots_adjust(top=0.93)
cbar_ax = fig.add_axes([0.13, 0.97, 0.75, 0.02])
cb = fig.colorbar(sc, cax=cbar_ax, orientation='horizontal')
# cb.set_label('Redshift')
# cbar_ax.set_title('Redshift')
cbar_ax.set_title('Nfof')
# cb = plt.colorbar()
plt.draw()
# fig = plt.gcf()
# fig.set_size_inches(plot_size)
plt.savefig(plot_dir + plot_file, bbox_inches='tight')
plt.show()
print('nmin lgMh - lgMl')
for nlo in range(2, 10):
sel = t['Nfof'] >= nlo
print(nlo, np.mean(lgMh[sel] - t['log_mass_lum'][sel]),
np.std(lgMh[sel] - t['log_mass_lum'][sel]))
def group_mass_tab(nmin=5, edge_min=0.9, mbins=mbins_def,
out_file='group_mass.tex'):
"""Tabulate group mass statistics."""
def group_stats(infile):
t = Table.read(infile)
t['log_mass'] = 13.98 + 1.16*(np.log10(t['LumBfunc']) - 11.5)
sel = (np.array(t['GroupEdge'] > edge_min) *
np.logical_not(t['log_mass'].mask) *
np.array(t['Nfof'] >= nmin))
t = t[sel]
nmbin = len(mbins) - 1
ngrp = []
zmean = []
for i in range(nmbin):
sel = (mbins[i] <= t['log_mass']) * (t['log_mass'] < mbins[i+1])
n = len(t[sel])
if 'Mock' in infile:
n /= 9
ngrp.append('{:d}'.format(int(n)))
zmean.append('{:3.2f}'.format(np.mean(t['IterCenZ'][sel])))
return ngrp, zmean
ngrp_gama, zmean_gama = group_stats(g3cfof)
ngrp_mock, zmean_mock = group_stats(g3cmockfof)
names = []
limits = []
nmbin = len(mbins) - 1
for i in range(nmbin):
names.append(r'${\cal M}' + str(i+1) + r'$')
limits.append('[{}, {}]'.format(mbins[i], mbins[i+1]))
t = {'Name': names, r'$\log_{10}({\cal M}_h/{\cal M}_\odot)$': limits,
r'$N_{\rm GAMA}$': ngrp_gama, r'$\bar{z}_{\rm GAMA}$': zmean_gama,
r'$N_{\rm Mock}$': ngrp_mock, r'$\bar{z}_{\rm Mock}$': zmean_mock}
ascii.write(t, output=plot_dir + out_file, format='latex', overwrite=True,
latexdict={'caption': r'''Group mass bin definitions,
number of groups and mean redshift.
Note that each mock realisation has 20 per cent
smaller volume than the GAMA-II fields.
\label{tab:group_mass_def}''',
'header_start': r'\hline', 'header_end': r'\hline',
'data_end': r'\hline'})
def group_mass_tab_with_mean_mass(nmin=5, edge_min=0.9, mbins=mbins_def,
out_file='group_mass.tex'):
"""Tabulate group mass statistics."""
def group_stats(infile):
t = Table.read(infile)
t['log_mass'] = 13.98 + 1.16*(np.log10(t['LumBfunc']) - 11.5)
sel = (np.array(t['GroupEdge'] > edge_min) *
np.logical_not(t['log_mass'].mask) *
np.array(t['Nfof'] >= nmin))
t = t[sel]
nmbin = len(mbins) - 1
ngrp = []
zmean = []
Mmean = []
for i in range(nmbin):
sel = (mbins[i] <= t['log_mass']) * (t['log_mass'] < mbins[i+1])
n = len(t[sel])
if 'Mock' in infile:
n /= 9
ngrp.append('{:d}'.format(int(n)))
zmean.append('{:3.2f}'.format(np.mean(t['IterCenZ'][sel])))
Mmean.append('{:3.2f}'.format(np.mean(t['log_mass'][sel])))
return ngrp, zmean, Mmean
ngrp_gama, zmean_gama, Mmean_gama = group_stats(g3cfof)
ngrp_mock, zmean_mock, Mmean_mock = group_stats(g3cmockfof)
names = []
limits = []
nmbin = len(mbins) - 1
# for i in range(nmbin):
# names.append(r'${\cal M}' + str(i+1) + r'$')
# limits.append('[{}, {}]'.format(mbins[i], mbins[i+1]))
# t = {'Name': names, r'$\lg {\cal M}_{h, {\rm limits}}$': limits,
# r'$N_G$': ngrp_gama,
# r'$\overline{\lg \cal M}_G$': Mmean_gama, r'$\overline{z}_G$': zmean_gama,
# r'$N_M$': ngrp_mock,
# r'$\overline{\lg \cal M}_M$': Mmean_mock, r'$\overline{z}_M$': zmean_mock,
# }
# ascii.write(t, output=plot_dir + out_file, format='latex', overwrite=True,
# latexdict={'tabletype': 'table',
# 'caption': r'''Group mass bin names and limits,
# with number of groups and mean log-mass and
# redshift for GAMA-II data and mocks.
# Note that each mock realisation has 20 per cent
# smaller volume than the GAMA-II fields.
# \label{tab:group_mass_def}''',
# 'header_start': r'''\hline
# & & \multicolumn{3}{c}{GAMA} &
# \multicolumn{3}{c}{Mock} \\
# % \cline{3-5} \cline{6-8}
# ''',
# 'header_end': r'\hline',
# 'data_end': r'\hline'})
f = open(plot_dir + out_file, 'w')
print(r'''
\begin{table}
\caption{Group bin names and log-mass limits, with number of groups,
mean log-mass, and mean redshift for GAMA-II data and mocks.
Note that each mock realisation has 20 per cent smaller volume than
the GAMA-II fields.
\label{tab:group_mass_def}}
\begin{tabular}{ccccccccc}
\hline
& & \multicolumn{3}{c}{GAMA} & & \multicolumn{3}{c}{Mocks} \\
\cline{3-5} \cline{7-9}
Name & $\lg {\cal M}_{h, {\rm limits}}$ &
$N$ & $\langle \lg {\cal M} \rangle$ & $\langle z \rangle$ & &
$N$ & $\langle \lg {\cal M} \rangle$ & $\langle z \rangle$ \\
\hline''', file=f)
for i in range(nmbin):
print(r'${\cal M}' + str(i+1) + r'$ & ' +
'[{}, {}] & '.format(mbins[i], mbins[i+1]) +
ngrp_gama[i] + ' & ' + Mmean_gama[i] + ' & ' + zmean_gama[i] +
' & & ' +
ngrp_mock[i] + ' & ' + Mmean_mock[i] + ' & ' + zmean_mock[i] +
r'\\', file=f)
print('''
\hline
\end{tabular}
\end{table}
''', file=f)
f.close()
def gal_mass_z(nmin=5, edge_min=0.9, fslim=(0.8, 10), Mmin=8, Mmax=12, nz=10,
pfit_ord=2, nfit=4, nboot=100, z0=0.5,
Mlim_ord=3, plot_file='gal_mass_z.pdf', plot_size=(6, 4)):
"""Grouped galaxy mass-redshift scatterplot."""
def neg(M):
"""Returns -1*kernel(M) for scipy to minimize."""
return -1*kernel(M)
samp = gs.GalSample()
samp.read_gama()
samp.stellar_mass(fslim=fslim)
samp.group_props()
t = samp.t
sel = (np.array(t['GroupEdge'] > edge_min) *
np.logical_not(t['log_mass'].mask) *
np.array(t['Nfof'] >= nmin))
t = t[sel]
# zlims = np.percentile(t['z'], np.linspace(0, 100, nz+1))
zlims = np.linspace(0.0, 0.5, nz+1)
zmean = np.zeros(nz)
Mpeak = np.zeros(nz)
Mstd = np.zeros(nz)
Mbins = np.linspace(Mmin, Mmax, 41)
M = Mbins[:-1] + 0.5*np.diff(Mbins)
Mpboot = np.zeros(nboot)
plt.clf()
# fig, axes = plt.subplots(10, 1, sharex=True, sharey=True, num=1)
fig, axes = plt.subplots(nz, 1, sharex=True, num=1)
fig.set_size_inches(5, 16)
fig.subplots_adjust(left=0.15, bottom=0.05, hspace=0.0, wspace=0.0)
fig.text(0.5, 0.0, ms_label, ha='center', va='center')
fig.text(0.06, 0.5, 'Frequency', ha='center', va='center', rotation='vertical')
for i in range(nz):
sel = (t['z'] >= zlims[i]) * (t['z'] < zlims[i+1])
zmean[i] = np.mean(t[sel]['z'])
ax = axes.flat[i]
lgM = t[sel]['logmstar']
ax.scatter(lgM, 0.1*np.random.random(lgM.shape), s=0.01)
kernel = stats.gaussian_kde(lgM)
den = kernel(M)
ax.plot(M, den)
ipeak = np.argmax(den)
res = scipy.optimize.fmin(neg, M[ipeak], disp=0)
Mpeak[i] = res[0]
# Bootstrap to find uncertainty in peak position
for iboot in range(nboot):
kernel = stats.gaussian_kde(np.random.choice(lgM, size=len(lgM)))
# den = kernel(M)
# ipeak = np.argmax(den)
res = scipy.optimize.fmin(neg, M[ipeak], disp=0)
Mpboot[iboot] = res[0]
Mstd[i] = np.std(Mpboot)
# freq, _, _ = ax.hist(t[sel]['logmstar'], bins=Mbins)
# freq, Mbins, patches = ax.hist(t[sel]['logmstar'], bins='auto')
# Fit polynomial to nfit bins either side of peak
# icen = np.argmax(freq)
# ilo = max(0, icen-nfit)
# ihi = min(len(M)-1, icen+nfit+1)
# p = np.polynomial.polynomial.polyfit(M[ilo:ihi], freq[ilo:ihi], pfit_ord)
# fit = np.polynomial.polynomial.polyval(M[ilo:ihi], p)
# Mpeak[i] = -p[1]/(2*p[2])
# ax.plot(M[ilo:ihi], fit)
yrange = (0, ax.axis()[3])
ax.plot((Mpeak[i], Mpeak[i]), yrange)
ax.bar(Mpeak[i], yrange[1], 2*Mstd[i], alpha=0.3)
# ax.plot((Mpeak[i]-Mstd[i], Mpeak[i]-Mstd[i]), yrange, 'o--')
# ax.plot((Mpeak[i]+Mstd[i], Mpeak[i]+Mstd[i]), yrange, 'o--')
ax.text(0.9, 0.8, 'z = {:4.3f}'.format(zmean[i]), ha='right',
transform=ax.transAxes)
ax.set_ylim(yrange)
plt.xlim(Mmin, Mmax)
plt.show()
# pdb.set_trace()
plt.clf()
plt.scatter(t['z'], t['logmstar'], s=0.1, c=t['gminusi_stars'],
vmin=0.2, vmax=1.2, cmap='coolwarm')
cb = plt.colorbar()
cb.set_label(r'$(g - i)^*$')
plt.errorbar(zmean, Mpeak, Mstd, fmt='go', color='orange')
# for Mlim_ord in (2):
# p = np.polynomial.polynomial.polyfit(1/(1+zmean), Mpeak, Mlim_ord, w=Mstd**-2)
# fit = np.polynomial.polynomial.polyval(1/(1+zlims), p)
# plt.plot(zlims, fit, '-', label='{}wtd'.format(Mlim_ord))
p = np.polynomial.polynomial.polyfit(1/(1+zmean), Mpeak, Mlim_ord)
fit = np.polynomial.polynomial.polyval(1/(1+zlims), p)
plt.plot(zlims, fit, 'g-', label='{}unwtd'.format(Mlim_ord))
# plt.legend()
plt.xlabel(r'$z$')
plt.ylabel(ms_label)
plt.axis((0, 0.5, 7, 12))
plt.draw()
fig = plt.gcf()
fig.set_size_inches(plot_size)
plt.savefig(plot_dir + plot_file, bbox_inches='tight')
plt.show()
print('Polynomial coeffs for M*[1/(1+z)]', p)
plt.clf()
plt.hist(t['gminusi_stars'], bins=20)
plt.show()
def gal_mass_lum(nmin=5, edge_min=0.9, fslim=(0.8, 10), pc=95,
plot_file='gal_mass_lum.pdf', plot_size=(5, 4)):
"""Galaxy stellar mass-luminosity scatterplot."""
samp = gs.GalSample()
samp.read_gama()
samp.stellar_mass(fslim=fslim)
samp.group_props()
t = samp.t
sel = (np.array(t['GroupEdge'] > edge_min) *
np.logical_not(t['log_mass'].mask) *
np.array(t['Nfof'] >= nmin))
samp.t = t[sel]
mags = np.linspace(-23, -15, 32)
masses = samp.smf_comp(mags, pc=pc)
fitpar = np.polynomial.polynomial.polyfit(mags, masses, 2)
fit = np.polynomial.polynomial.polyval((mags[0], mags[-1]), fitpar)
print('95-percentile mass = {} + {} M_r'.format(*fitpar))
plt.clf()
plt.scatter(samp.abs_mags('r_petro'), samp.t['logmstar'], s=0.1, label=None)
plt.plot(mags, masses, 'g-', label='Binned 95-th percentile')
plt.plot((mags[0], mags[-1]), fit, 'r-', label='Linear fit')
plt.xlabel(r'$^{0.1}M_r - 5 \log\ h$')
plt.ylabel(r'$\log_{10}({\cal M}_*/{\cal M}_\odot h^{-2})$')
plt.axis((-15, -23, 7, 12))
plt.legend()
plt.draw()
fig = plt.gcf()
fig.set_size_inches(plot_size)
plt.savefig(plot_dir + plot_file, bbox_inches='tight')
plt.show()
def sersic_hist(plot_file='sersic_hist.pdf', range=(-1, 1.5), ncut=1.9,
colour='gi_colour', plot_size=(5, 4)):
"""Sersic-index histogram for blue and red galaxies."""
samp = gs.GalSample()
samp.read_gama()
samp.stellar_mass()
samp.add_sersic_index()
plt.clf()
logn = np.log10(samp.t['GALINDEX_r'])
logncut = math.log10(ncut)
blue = samp.t[colour] == 'b'
red = samp.t[colour] == 'r'
plt.hist((logn[blue], logn[red]), bins=20, range=range, color=('b', 'r'))
# plt.hist((logn, logn[blue], logn[red]), bins=20, range=range,
# color=('k', 'b', 'r'))
ymin, ymax = plt.ylim()
plt.plot((logncut, logncut), (ymin, ymax), 'k')
plt.xlabel(r'$\log_{10}\ n_r$')
plt.ylabel(r'Frequency')
ax = plt.subplot(111)
plt.text(0.1, 0.9, 'Disky', transform=ax.transAxes)
plt.text(0.9, 0.9, 'Spheroidal', ha='right', transform=ax.transAxes)
plt.draw()
fig = plt.gcf()
fig.set_size_inches(plot_size)
plt.savefig(plot_dir + plot_file, bbox_inches='tight')
plt.show()
plt.clf()
plt.scatter(samp.t['gminusi_stars'], logn, s=0.1, c='b')
plt.xlabel(r'$(g - i)^*$')
plt.ylabel(r'$\log_{10}\ n_r$')
# plt.draw()
# fig = plt.gcf()
# fig.set_size_inches(plot_size)
# plt.savefig(plot_dir + plot_file, bbox_inches='tight')
plt.show()
def taylor_colour(infile=gama_data+'StellarMassesLambdarv20.fits',
fslim=(0.8, 10), H0=100, limits=(8.5, 12.0, 0.0, 1.3),
zbins=(0.002, 0.1, 0.2, 0.3, 0.65),
plot_file='colour_mass.pdf', plot_size=(5, 5)):
"""Blue/red colour classification using Taylor+2015 dereddened colours."""
def colourCut(lgm):
"""Return (g-i)^* colour cut corresponding to log mass"""
return 0.07*lgm - 0.03
m = Table.read(infile)
sel = (m['fluxscale'] >= fslim[0]) * (m['fluxscale'] < fslim[1])
print(len(m[sel]), 'out of', len(m),
'galaxies with fluxscale in range', fslim)
m = m[sel]
logmstar = m['logmstar'] - 2*math.log10(H0/70.0) + np.log10(m['fluxscale'])
z = m['Z_TONRY']
gistar = m['gminusi_stars']
logmstar = m['logmstar']
lgm = np.array(limits[:2])
cutline = colourCut(lgm)
plt.clf()
fig, axes = plt.subplots(2, 2, sharex=True, sharey=True, num=1)
fig.set_size_inches((8, 8))
fig.subplots_adjust(left=0.14, bottom=0.07, hspace=0.0, wspace=0.0)
fig.text(0.5, 0.0, r'$\log_{10}\ ({\cal M}_*/{\cal M}_\odot h^{-2})$',
ha='center', va='center')
fig.text(0.06, 0.5, r'$(g - i)^*$', ha='center', va='center',
rotation='vertical')
for iz in range(4):
zlo, zhi = zbins[iz], zbins[iz+1]
ax = axes.flat[iz]
idx = (zlo <= z) * (z < zhi)
hist, xedges, yedges = np.histogram2d(gistar[idx], logmstar[idx],
50, [limits[2:4], limits[0:2]])
# ax.scatter(logmstar[idx], gistar[idx], s=0.01, c='k', alpha=0.2)
# pdb.set_trace()
ax.contour(hist, 10, extent=limits)
ax.plot(lgm, cutline, 'r-')
ax.axis(limits)
title = r'${:2.1f} < z < {:2.1f}$'.format(zlo, zhi)
ax.text(0.5, 0.9, title, transform=ax.transAxes)
plt.draw()
fig = plt.gcf()
fig.set_size_inches(plot_size)
plt.savefig(plot_dir + plot_file, bbox_inches='tight')
plt.show()
def smf_comp(infile='smf.dat', lf_lims=(6.5, 12, 1e-5, 1), nmin=2,
xlabel=ms_label, ylabel=smf_label,
plot_file='smf_comp.pdf', plot_size=(5, 4)):
"""Compare z < 0.1 field galaxy SMF with Baldry+2012 & Wright+2017 dbl
Schechter fits."""
h = 0.7
lgh = math.log10(h)
# h = 1
wright = (10.78 + 2*lgh, -0.62, -1.50, 2.93e-3/h**3, 0.63e-3/h**3)
baldry = (10.66 + 2*lgh, -0.35, -1.47, 3.96e-3/h**3, 0.79e-3/h**3)
lf_dict = pickle.load(open(infile, 'rb'))
plt.clf()
ax = plt.subplot(111)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.semilogy(basey=10, nonposy='clip')
# phi = lf_dict['all']
# phi.plot(ax=ax, nmin=nmin, show_fit=False, clr='b', label='All GAMA')
phi = lf_dict['z0']
phi.plot(ax=ax, nmin=nmin, show_fit=False, clr='b', label='This paper')
bdat = np.loadtxt(lf_data + '/Baldry2012/table1.txt')
logM = bdat[:, 0] + 2*lgh
phi = bdat[:, 2]/1000/h**3
phi_err = bdat[:, 3]/1000/h**3
plt.errorbar(logM, phi, phi_err, fmt='gs', label='Baldry+2012')
logM = np.linspace(lf_lims[0], lf_lims[1], 100)
# phi = lf.Schechter_dbl_mass(logM, baldry)
# print(phi)
# plt.plot(logM, phi, label='Baldry 2012')
phi = lf.Schechter_dbl_mass(logM, wright)
plt.plot(logM, phi, 'r', label='Wright+2017')
plt.axis(lf_lims)
plt.legend()
# plt.draw()
# plt.savefig(plot_dir + plot_file, bbox_inches='tight')
plt.draw()
fig = plt.gcf()
fig.set_size_inches(plot_size)
plt.savefig(plot_dir + plot_file, bbox_inches='tight')
plt.show()
def mass_limit(samp):
"""Apply stellar mass completeness limit determined in gal_mass_z()"""
p = [1.17442222, 29.68880365, -22.58489171]
a = 1/(1 + samp.t['z'])
Mt = np.polynomial.polynomial.polyval(a, p)
sel = samp.t['logmstar'] > Mt
samp.t = samp.t[sel]
samp.t['zlo'] = samp.zlimits[0]*np.ones(len(samp.t))
a = (-p[1] - (p[1]**2 - 4*p[2]*(p[0]-samp.t['logmstar']))**0.5)/2/p[2]
samp.t['zhi'] = np.minimum(samp.zlimits[1], 1/a - 1)
def central_f_red(mbins=mbins_def, clrname='gi_colour', zlimits=(0.002, 0.65)):
"""Calculate fraction of red central galaxies in each halo mass bin as
needed for Yang et al central CLF/CSMF comparison.."""
samp = gs.GalSample(zlimits=zlimits)
samp.read_gama()
samp.stellar_mass()
samp.group_props()
samp.add_sersic_index()
nmbin = len(mbins) - 1
for i in range(nmbin):
sel_dict = {'log_mass': (mbins[i], mbins[i+1]),
'RankIterCen': [1, 2]}
samp.select(sel_dict)
ncen = len(samp.t[samp.use])
sel_dict = {'log_mass': (mbins[i], mbins[i+1]),
'RankIterCen': [1, 2],
clrname: ('r', 's')}
samp.select(sel_dict)
ncenred = len(samp.t[samp.use])
print('mass', mbins[i], mbins[i+1], 'central red frac', ncenred/ncen)
def clf_broad(mbins=mbins_def, clrname='gi_colour', Vmax='Vmax_grp',
bins=(-24, -21, -19, -17), colname='r_petro', error='jackknife',
outfile='clf_broad.pkl'):
"""Conditional LF in broad mag bins."""
clf(mbins=mbins, clrname=clrname, Vmax=Vmax,
bins=bins, colname=colname, error=error, outfile=outfile)
def clf_vol(mbins=mbins_def, clrname='gi_colour', Vmax='Vmax_grp',
bins=np.linspace(-24, -16, 18), zlimits=(0.002, 0.1), colname='r_petro',
error='jackknife', outfile='clf_vol.pkl'):
"""Conditional LF for volume-limited sample."""
clf(mbins=mbins, clrname=clrname, Vmax=Vmax,
bins=bins, zlimits=zlimits, colname=colname, error=error, outfile=outfile)
def clf_vol_vmax(mbins=mbins_def, clrname='gi_colour', Vmax='Vmax_dec',
bins=np.linspace(-24, -16, 18), zlimits=(0.002, 0.1), colname='r_petro',
error='jackknife', outfile='clf_vol_vmax.pkl'):
"""Vmax LF for volume-limited sample."""
clf(mbins=mbins, clrname=clrname, Vmax=Vmax,
bins=bins, zlimits=zlimits, colname=colname, error=error, outfile=outfile)
def clf_vmax(Vmax='Vmax_dec', outfile='clf_vmax.pkl'):
"""Vmax LF."""
clf(Vmax=Vmax, outfile=outfile)
def clf_mock(galfile=g3cmockgal, grpfile=g3cmockfof, Vmax='Vmax_raw',
error='mock', outfile='clf_mock_vmax.pkl'):
"""Vmax LF."""
clf(galfile=galfile, grpfile=grpfile, Vmax=Vmax, error=error,
outfile=outfile)
def clf(galfile=g3cgal, grpfile=g3cfof, mbins=mbins_def, clrname='gi_colour',
Vmax='Vmax_grp',
bins=np.linspace(-24, -16, 17), zlimits=(0.002, 0.65), vol_z=None,
colname='r_petro', error='jackknife', outfile='clf.pkl'):
"""Conditional LF by central/satellite, galaxy colour and Sersic index.
Normalised to number of groups if Vmax='Vmax_grp', otherwise using
density-corrected Vmax and normalised to total grouped galaxy sample."""
samp = gs.GalSample(zlimits=zlimits)
samp.read_gama_groups(galfile=galfile, grpfile=grpfile)
if error != 'mock':
samp.stellar_mass()
samp.add_sersic_index()
if vol_z:
samp.vol_limit_z(vol_z)
samp.group_limit(nmin)
grps = table.unique(samp.t, keys='GroupID')
plt.clf()
plt.scatter(grps['IterCenZ'], grps['log_mass'], s=2,
c=grps['Nfof'], norm=mpl.colors.LogNorm())
plt.xlabel('Redshift')
plt.ylabel(r'$\log_{10}({\cal M}_h/{\cal M}_\odot h^{-1})$')
cb = plt.colorbar()
cb.set_label(r'$N_{\rm fof}$')
plt.show()
# samp.vis_calc((lf.sel_gama_mag_lo, lf.sel_gama_mag_hi))
samp.vmax_calc()
lf_dict = {}
nmbin = len(mbins) - 1
for i in range(nmbin):
sel_dict = {'log_mass': (mbins[i], mbins[i+1])}
samp.select(sel_dict)
if Vmax == 'Vmax_grp':
samp.vmax_group(mbins[i], mbins[i+1])
# norm = 1.0/len(np.unique(samp.tsel()['GroupID']))
norm = 1
else:
norm = len(samp.t)/len(samp.tsel())
phi = lf.LF(samp, colname, bins, norm=norm, Vmax=Vmax, error=error,
sel_dict=sel_dict)
Mkey = 'M{}'.format(i)
lf_dict[Mkey] = phi
for lbl, rank_lims, p0 in zip(['cen', 'sat'], [[1, 2], [2, 500]],
((5, -19.5, -2.5), (-1, -20, 1))):
sel_dict = {'log_mass': (mbins[i], mbins[i+1]),
'RankIterCen': rank_lims}
samp.select(sel_dict)
phi = lf.LF(samp, colname, bins, norm=norm, Vmax=Vmax, error=error,
sel_dict=sel_dict)
# if lbl == 'cen':
# phi.fn_fit(fn=lf.gaussian, p0=(-22, 1, -2),
# Mmin=Mmin_fit, Mmax=Mmax_fit)
# else:
lf_dict[Mkey + lbl] = phi
if error != 'mock':
for colour in 'br':
clr_limits = ('a', 'z')
if (colour == 'b'):
clr_limits = ('b', 'c')
if (colour == 'r'):
clr_limits = ('r', 's')
sel_dict = {'log_mass': (mbins[i], mbins[i+1]), clrname: clr_limits}
samp.select(sel_dict)
phi = lf.LF(samp, colname, bins, norm=norm, Vmax=Vmax, error=error,
sel_dict=sel_dict)
lf_dict[Mkey + colour] = phi
for lbl, sersic_lims in zip(['nlo', 'nhi'], [[0, 1.9], [1.9, 30]]):
sel_dict = {'log_mass': (mbins[i], mbins[i+1]),
'GALINDEX_r': sersic_lims}
samp.select(sel_dict)
phi = lf.LF(samp, colname, bins, norm=norm, Vmax=Vmax, error=error,
sel_dict=sel_dict)
lf_dict[Mkey + lbl] = phi
pickle.dump(lf_dict, open(outfile, 'wb'))
def csmf_vmax(Vmax='Vmax_dec', outfile='csmf_vmax.pkl'):
"""Vmax SMF."""
csmf(Vmax=Vmax, outfile=outfile)
def csmf(fslim=(0.8, 10), mbins=mbins_def, masscomp=True,
Vmax='Vmax_grp', p0=(-1.5, 10.5, 1), bins=np.linspace(7, 12, 20),
Mmin_fit=8.5, Mmax_fit=12, clrname='gi_colour',
colname='logmstar', error='jackknife', outfile='csmf.pkl'):
"""Conditional SMF by central/satellite, galaxy colour and Sersic index."""
samp = gs.GalSample()
samp.read_gama()
samp.group_props()
samp.add_sersic_index()
samp.stellar_mass(fslim=fslim)
# Apply stellar mass completeness limit determined in gal_mass_z()
if masscomp:
mass_limit(samp)
else:
samp.vis_calc((lf.sel_gama_mag_lo, lf.sel_gama_mag_hi))
samp.vmax_calc()
lf_dict = {}
nmbin = len(mbins) - 1
for i in range(nmbin):
sel_dict = {'log_mass': (mbins[i], mbins[i+1])}
samp.select(sel_dict)
if Vmax == 'Vmax_grp':
samp.vmax_group(mbins[i], mbins[i+1])
norm = 1
else:
norm = len(samp.t)/len(samp.tsel())
phi = lf.LF(samp, colname, bins, norm=norm, Vmax=Vmax, error=error,
sel_dict=sel_dict)
# phi.fn_fit(fn=lf.Schechter_mass, p0=p0, Mmin=Mmin_fit, Mmax=Mmax_fit)
# p0 = phi.fit_par
Mkey = 'M{}'.format(i)
lf_dict[Mkey] = phi
for colour in 'br':
clr_limits = ('a', 'z')
if (colour == 'b'):
clr_limits = ('b', 'c')
if (colour == 'r'):
clr_limits = ('r', 's')
sel_dict = {'log_mass': (mbins[i], mbins[i+1]), clrname: clr_limits}
samp.select(sel_dict)
phi = lf.LF(samp, colname, bins, norm=norm, Vmax=Vmax, error=error,
sel_dict=sel_dict)
# phi.fn_fit(fn=lf.Schechter_mass, p0=p0,
# Mmin=Mmin_fit, Mmax=Mmax_fit)
lf_dict[Mkey + colour] = phi
for lbl, sersic_lims in zip(['nlo', 'nhi'], [[0, 1.9], [1.9, 30]]):
sel_dict = {'log_mass': (mbins[i], mbins[i+1]),
'GALINDEX_r': sersic_lims}
samp.select(sel_dict)
phi = lf.LF(samp, colname, bins, norm=norm, Vmax=Vmax, error=error,
sel_dict=sel_dict)
# phi.fn_fit(fn=lf.Schechter_mass, p0=p0,
# Mmin=Mmin_fit, Mmax=Mmax_fit)
lf_dict[Mkey + lbl] = phi
for lbl, rank_lims, p0 in zip(['cen', 'sat'], [[1, 2], [2, 500]],
((2, 10.5, -2.5), (-1.5, 10.5, -2.5))):
sel_dict = {'log_mass': (mbins[i], mbins[i+1]),
'RankIterCen': rank_lims}
samp.select(sel_dict)
phi = lf.LF(samp, colname, bins, norm=norm, Vmax=Vmax, error=error,
sel_dict=sel_dict)
# phi.fn_fit(fn=lf.Schechter_mass, p0=p0,
# Mmin=Mmin_fit, Mmax=Mmax_fit)
lf_dict[Mkey + lbl] = phi
pickle.dump(lf_dict, open(outfile, 'wb'))
def csmf_alp_comp(nmin=2, fslim=(0.8, 10), Vmax='Vmax_grp',
mbins=(12, 13, 14, 16), mass_est='lum',
smfile=gama_data + 'StellarMassesv19.fits',
p0=(-1.5, 10.5, -2.5), bins=np.linspace(7, 12, 20),
Mmin_fit=8.5, Mmax_fit=12, clrname='gi_colour',
colname='logmstar', error='jackknife',
outfile='csmf_alp_comp.pkl'):
"""Low-z conditional SMF in same mass bins as Alpaslan+2015."""
samp = gs.GalSample(Q=0, P=0, zlimits=(0.013, 0.1))
samp.read_gama()
samp.group_props(mass_est=mass_est, nmin=nmin)
samp.add_sersic_index()
samp.stellar_mass(smf=smfile, fslim=fslim)
samp.vis_calc((lf.sel_gama_mag_lo, lf.sel_gama_mag_hi))
samp.vmax_calc()
lf_dict = {}
nmbin = len(mbins) - 1
for i in range(nmbin):
sel_dict = {'log_mass': (mbins[i], mbins[i+1])}
samp.select(sel_dict)
if Vmax == 'Vmax_grp':
samp.vmax_group(mbins[i], mbins[i+1])
norm = 1
# else:
# norm = len(samp.t)/len(samp.tsel())
phi = lf.LF(samp, colname, bins, Vmax=Vmax,
norm=1, error=error, sel_dict=sel_dict)
phi.fn_fit(fn=lf.Schechter_mass, p0=p0, Mmin=Mmin_fit, Mmax=Mmax_fit)
p0 = phi.fit_par
Mkey = 'M{}'.format(i)
lf_dict[Mkey] = phi
pickle.dump(lf_dict, open(outfile, 'wb'))
def csmf_alp_comp_plot(infile='csmf_alp_comp.pkl', nmin=2,
xlabel=ms_label, ylabel=smf_label,
plot_file='csmf_alp_comp.pdf', plot_size=(5, 4)):
"""Low-z conditional SMF in same mass bins as Alpaslan+2015."""
alp_par = ((-0.87, 10.86, -2.76),
(-0.87, 10.93, -2.95),
(-1.06, 11.16, -3.57))
lf_lims = (9.5, 12.0, 1e-5, 0.01)
clrs = 'bgr'
lf_dict = pickle.load(open(infile, 'rb'))
plt.clf()
ax = plt.subplot(111)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.semilogy(basey=10, nonposy='clip')
plt.axis(lf_lims)
for i in range(3):
key = 'M{}'.format(i)
phi = lf_dict[key]
Mlo, Mhi = phi.sel_dict['log_mass']
label = r'${\cal M}' + r'= \ [{}, {}]$'.format(Mlo, Mhi)
phi.plot(ax=ax, nmin=nmin, show_fit=False, clr=clrs[i], label=label)
phi.fn_plot(ax, alp_par[i], c=clrs[i])
plt.legend(loc=3)
plt.draw()
fig = plt.gcf()
fig.set_size_inches(plot_size)
plt.savefig(plot_dir + plot_file, bbox_inches='tight')
plt.show()
def lfr_z(nmin=5, edge_min=0.9, colname='r_petro',
bins=np.linspace(-24, -16, 18),
Mmin_fit=-24, Mmax_fit=-19.4, zlims=(0.002, 0.1, 0.2, 0.3),
clrname='gi_colour', error='jackknife', outfile='lfr_z.pkl'):
"""r-band LF in redshift bins (without evolution corrections)."""
samp = gs.GalSample(Q=0, P=0)
samp.read_gama()
samp.stellar_mass()
samp.add_sersic_index()
samp.vis_calc((lf.sel_gama_mag_lo, lf.sel_gama_mag_hi))
samp.group_props()
t = samp.t
ngal = len(t)
sel = (np.array(t['GroupEdge'] > edge_min) *
np.logical_not(t['log_mass'].mask) *
np.array(t['Nfof'] >= nmin))
ngrouped = len(t[sel])
norm = ngrouped/ngal
print('norm =', norm)
lf_dict = {}
for iz in range(3):
zlo, zhi = zlims[iz], zlims[iz+1]
samp.zlimits = (zlo, zhi)
samp.vmax_calc()
sel_dict = {'z': (zlo, zhi)}
samp.select(sel_dict)
phi = lf.LF(samp, colname, bins,
norm=norm, error=error, sel_dict=sel_dict)
phi.comp_limits(samp, zlo, zhi)
# phi.fn_fit(fn=lf.Schechter_mag, Mmin=Mmin_fit, Mmax=Mmax_fit)
Mkey = 'z{}'.format(iz)
lf_dict[Mkey] = phi
for colour in 'br':
clr_limits = ('a', 'z')
if (colour == 'b'):
clr_limits = ('b', 'c')
if (colour == 'r'):
clr_limits = ('r', 's')
sel_dict = {'z': (zlo, zhi), clrname: clr_limits}
samp.select(sel_dict)
phi = lf.LF(samp, colname, bins,
norm=norm, error=error, sel_dict=sel_dict)
phi.comp_limits(samp, zlo, zhi)
# phi.fn_fit(fn=lf.Schechter_mag, Mmin=Mmin_fit, Mmax=Mmax_fit)
lf_dict[Mkey + colour] = phi
for lbl, sersic_lims in zip(['nlo', 'nhi'], [[0, 1.9], [1.9, 30]]):
sel_dict = {'z': (zlo, zhi), 'GALINDEX_r': sersic_lims}
samp.select(sel_dict)
phi = lf.LF(samp, colname, bins,
norm=norm, error=error, sel_dict=sel_dict)
phi.comp_limits(samp, zlo, zhi)
# phi.fn_fit(fn=lf.Schechter_mag, Mmin=Mmin_fit, Mmax=Mmax_fit)
lf_dict[Mkey + lbl] = phi
pickle.dump(lf_dict, open(outfile, 'wb'))
def lfr_z_mock(nmin=5, edge_min=0.9, colname='r_petro',
bins=np.linspace(-24, -16, 18),
Mmin_fit=-24, Mmax_fit=-19.4, zlims=(0.002, 0.1, 0.2, 0.3),
error='mock', outfile='lfr_z_mock.pkl'):
"""mock r-band LF in redshift bins (without evolution corrections)."""
samp = gs.GalSample(Q=0, P=0)
samp.read_gama_group_mocks()
samp.vis_calc((lf.sel_gama_mag_lo, lf.sel_gama_mag_hi))
t = samp.t
ngal = len(t)
sel = (np.array(t['GroupEdge'] > edge_min) *
np.logical_not(t['log_mass'].mask) *
np.array(t['Nfof'] >= nmin))
ngrouped = len(t[sel])
norm = ngrouped/ngal
print('norm =', norm)
lf_dict = {}
for iz in range(3):
zlo, zhi = zlims[iz], zlims[iz+1]
samp.zlimits = (zlo, zhi)
samp.vmax_calc(denfile=None)
sel_dict = {'z': (zlo, zhi)}
samp.select(sel_dict)
phi = lf.LF(samp, colname, bins,
norm=norm, error=error, sel_dict=sel_dict)
phi.comp_limits(samp, zlo, zhi)
# phi.fn_fit(fn=lf.Schechter_mag, Mmin=Mmin_fit, Mmax=Mmax_fit)
Mkey = 'z{}mock'.format(iz)
lf_dict[Mkey] = phi
pickle.dump(lf_dict, open(outfile, 'wb'))
def smf_z(nmin=5, edge_min=0.9, colname='logmstar',
bins=np.linspace(7, 12, 20),
Mmin_fit=8.5, Mmax_fit=12, zlims=(0.002, 0.1, 0.2, 0.3),
fslim=(0.8, 10), pc=95, clrname='gi_colour',
p0=(-0.5, 10.5, -2.5), error='jackknife', outfile='smf_z.dat'):
"""SMF in redshift bins (without evolution corrections)."""