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lum.py.bak
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# Luminosity function estimation and plotting
#
# Revision history
#
# 1.0 24-may-10 Evolving Schechter function, Vmax and SWML fits
# 1.1 29-nov-10 Use polynomial fits to K(z) for each galaxy
# 1.2 01-aug-11 Replace bootstrap with jacknife errors
# 1.3 18-nov-11 Pass selection criteria as a dictionary of keyword:range values
# 1.4 21-feb-12 Allow Saunders et al 1990 LF fit
import glob
import math
import numpy as np
import os
import pickle
from astropy.cosmology import FlatLambdaCDM
import astropy.io.fits as pyfits
#from astLib import astCalc
import pdb
import matplotlib
if not('DISPLAY' in os.environ):
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from mpl_toolkits.axes_grid import AxesGrid
from mpl_toolkits.axes_grid.inset_locator import inset_axes
import scipy.integrate
import scipy.optimize
import time
# Catch invalid values in numpy calls
np.seterr(divide='raise')
np.seterr(invalid='raise')
# Global parameters
par = {}
sel_dict = {'post_class': (0, 4)}
cosmo = None
ln10 = math.log(10)
plot = None
# Solar magnitudes from Blanton et al 2003 for ^{0.1}ugriz bands
Msun_ugriz = [6.80, 5.45, 4.76, 4.58, 4.51]
# Not sure where thse came from
Msun_ugrizYJHK = [6.39, 5.07, 4.62, 4.52, 4.48, 4.06, 3.64, 3.32, 3.28]
# Parameter fits constrained within following limits.
parLimits = {'alpha': (-2.5, 1), 'Mstar': (-22, -17), 'sigma': (0.01, 2.0),
'beta': (-2.5, 0.5), 'Mt': (-21, -14),
'Q': (0, 8), 'P': (-10, 5)}
#------------------------------------------------------------------------------
# Driver routines
#------------------------------------------------------------------------------
def lfPCtest(inFile='kcorrz.fits', like=0, jack=0):
"""Test effects of applying post_class cuts on u and r-band LFs"""
likeFile = None
for iband in (0,2):
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (20.3, 19.5, 19.4, 18.7, 18.2)[iband]
absMin = (-23.0, -24.0, -24.0, -25.0, -25.0)[iband]
absMinSTY = absMin
absMax = (-10.0, -10.0, -10.0, -12.0, -12.0)[iband]
absMaxSTY = absMax
absStep = 0.25
zlims = [[0.002, 0.5]]
zRangeSTY = [0.002, 0.5]
schec = {'alpha': -1.1, 'Mstar': -18.4, 'Q': 3, 'P': 1}
colour = 'c'
outFile = 'lf_pc06_%s%s.dat' % (band, colour)
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec, pcRange=(0,6))
outFile = 'lf_pc05_%s%s.dat' % (band, colour)
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec, pcRange=(0,5))
outFile = 'lf_pc03_%s%s.dat' % (band, colour)
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec, pcRange=(0,3))
outFile = 'lf_pc02_%s%s.dat' % (band, colour)
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec, pcRange=(0,2))
def lfEv(inFile='kcorr.fits', nz=4, like=0, jack=0):
"""LF evolution in redshift slices"""
likeFile = None
for iband in range(0,2):
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (20.3, 19.5, 19.4, 18.7, 18.2)[iband]
absMin = (-23.0, -24.0, -24.0, -25.0, -25.0)[iband]
absMinSTY = absMin
absMax = (-15.0, -16.0, -16.0, -17.0, -17.0)[iband]
absMaxSTY = absMax
absStep = 0.25
if nz == 3:
zlims = [[0.002, 0.2], [0.2, 0.4], [0.4, 0.6]]
if nz == 4:
zlims = [[0.002, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.5]]
if nz == 8:
zlims = [[0.002, 0.05], [0.05, 0.1], [0.1, 0.15], [0.15, 0.2],
[0.2, 0.25], [0.25, 0.3], [0.3, 0.4], [0.4, 0.5]]
zRangeSTY = [0.002, 0.5]
schec = {'alpha': -1.1, 'Mstar': -18.4, 'sigma': 0.2, 'Q': 3, 'P': 1}
for colour in 'cbr':
outFile = 'lf_ev%d_%s%s.dat' % (nz, band, colour)
if like:
likeFile = 'like_ev%d_%s%s.dat' % (nz, band, colour)
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec)
def lfEvMorph(inFile='kcorrz.fits', nz=4, like=0, jack=0):
"""LF evolution in redshift slices by morphology (Sersic index)"""
likeFile = None
for iband in range(5):
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (20.3, 19.5, 19.4, 18.7, 18.2)[iband]
absMin = (-23.0, -24.0, -24.0, -25.0, -25.0)[iband]
absMinSTY = absMin
absMax = (-15.0, -16.0, -16.0, -17.0, -17.0)[iband]
absMaxSTY = absMax
absStep = 0.25
if nz == 3:
zlims = [[0.002, 0.2], [0.2, 0.4], [0.4, 0.6]]
if nz == 4:
zlims = [[0.002, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.5]]
if nz == 8:
zlims = [[0.002, 0.05], [0.05, 0.1], [0.1, 0.15], [0.15, 0.2],
[0.2, 0.25], [0.25, 0.3], [0.3, 0.4], [0.4, 0.5]]
zRangeSTY = [0.002, 0.5]
schec = {'alpha': -1.1, 'Mstar': -18.4, 'Q': 3, 'P': 1}
colour = 'c'
for morph in 'es':
## for mass_limits in ((0, 1e13), (1e13, 1e17)):
outFile = 'lf_ev%d_%s%s.dat' % (nz, band, morph)
if like:
likeFile = 'like_ev%d_%s%s.dat' % (nz, band, morph)
if (morph == 'e'): sersic_limits = (1.9, 15)
if (morph == 's'): sersic_limits = (0.08, 1.9)
sel_dict['gal_index_r'] = sersic_limits
## sel_dict['group_mass'] = (0, 1e13)
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec)
def lfEvBPT(inFile='../kcorrz.fits', nz=4, like=0, jack=0):
"""LF evolution in redshift slices by BPT class"""
likeFile = None
for iband in range(5):
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (20.3, 19.5, 19.4, 18.7, 18.2)[iband]
absMin = (-23.0, -24.0, -24.0, -25.0, -25.0)[iband]
absMinSTY = absMin
absMax = (-15.0, -16.0, -16.0, -17.0, -17.0)[iband]
absMaxSTY = absMax
absStep = 0.25
if nz == 3:
zlims = [[0.002, 0.2], [0.2, 0.4], [0.4, 0.6]]
if nz == 4:
zlims = [[0.002, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.5]]
if nz == 8:
zlims = [[0.002, 0.05], [0.05, 0.1], [0.1, 0.15], [0.15, 0.2],
[0.2, 0.25], [0.25, 0.3], [0.3, 0.4], [0.4, 0.5]]
zRangeSTY = [0.002, 0.5]
schec = {'alpha': -1.1, 'Mstar': -18.4, 'Q': 3, 'P': 1}
colour = 'c'
for bpt_code in 'uqsca':
outFile = 'lf_ev%d_%s%s.dat' % (nz, band, bpt_code)
if like:
likeFile = 'like_ev%d_%s%s.dat' % (nz, band, bpt_code)
if (bpt_code == 'u'): bpt = (' ', ' _')
if (bpt_code == 'q'): bpt = ('BLANK','BLANK_')
if (bpt_code == 's'): bpt = ('Star Forming', 'Star Forming_')
if (bpt_code == 'c'): bpt = ('Composite', 'Composite_')
if (bpt_code == 'a'): bpt = ('LINER', 'Seyfert_')
sel_dict['BPT'] = bpt
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec)
def lfEvWHAN(inFile='../kcorrz.fits', nz=4, like=0, jack=0):
"""LF evolution in redshift slices by WHAN class"""
likeFile = None
for iband in range(5):
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (20.3, 19.5, 19.4, 18.7, 18.2)[iband]
absMin = (-23.0, -24.0, -24.0, -25.0, -25.0)[iband]
absMinSTY = absMin
absMax = (-15.0, -16.0, -16.0, -17.0, -17.0)[iband]
absMaxSTY = absMax
absStep = 0.25
if nz == 3:
zlims = [[0.002, 0.2], [0.2, 0.4], [0.4, 0.6]]
if nz == 4:
zlims = [[0.002, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.5]]
if nz == 8:
zlims = [[0.002, 0.05], [0.05, 0.1], [0.1, 0.15], [0.15, 0.2],
[0.2, 0.25], [0.25, 0.3], [0.3, 0.4], [0.4, 0.5]]
zRangeSTY = [0.002, 0.5]
schec = {'alpha': -1.1, 'Mstar': -18.4, 'Q': 3, 'P': 1}
colour = 'c'
## for whan_code in ('sf', 'sa', 'wa', 'r', 'p'):
for whan_code in ('a',):
outFile = 'lf_ev%d_%s%s.dat' % (nz, band, whan_code)
if like:
likeFile = 'like_ev%d_%s%s.dat' % (nz, band, whan_code)
if (whan_code == 'u'): whan = (' ', ' _')
if (whan_code == 'sf'): whan = ('Star Forming', 'Star Forming_')
if (whan_code == 'sa'): whan = ('Strong AGN', 'Strong AGN_')
if (whan_code == 'wa'): whan = ('Weak AGN', 'Weak AGN_')
if (whan_code == 'a'): whan = ('Strong AGN', 'Weak AGN_')
if (whan_code == 'r'): whan = ('Retired', 'Retired_')
if (whan_code == 'p'): whan = ('Passive', 'Passive_')
sel_dict['WHAN'] = whan
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec)
def lfSDSSEv(inFile='kcorrz.fits', nz=4, like=0, jack=0):
"""SDSS LF evolution in ugriz"""
likeFile = None
for iband in range(5):
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (19.0, 18.0, 17.7, 17.2, 16.8)[iband]
absMin = (-23.0, -24.0, -24.0, -25.0, -25.0)[iband]
absMinSTY = absMin
absMax = (-15.0, -16.0, -16.0, -17.0, -17.0)[iband]
absMaxSTY = absMax
absStep = 0.25
if nz == 4:
zlims = [[0.002, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.4]]
if nz == 8:
zlims = [[0.002, 0.05], [0.05, 0.1], [0.1, 0.15], [0.15, 0.2],
[0.2, 0.25], [0.25, 0.3], [0.3, 0.35], [0.35, 0.4]]
zRangeSTY = [0.002, 0.4]
schec = {'alpha': -1.1, 'Mstar': -18.4, 'Q': 3, 'P': 1}
for colour in 'cbr':
outFile = 'lf_ev%d_%s%s.dat' % (nz, band, colour)
if like:
likeFile = 'like_ev%d_%s%s.dat' % (nz, band, colour)
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY,
zRangeSTY, likeFile, jack=jack, colour=colour,
schecInit=schec, pcRange=None, bpt_type=None)
## for bpt_type in 'uqsca':
## outFile = 'lf_ev%d_%s%s%s.dat' % (nz, band, colour, bpt_type)
## if like:
## likeFile = 'like_ev%d_%s%s%s.dat' % (nz, band, colour,
## bpt_type)
## lf(inFile, outFile, iband, appMin, appMax,
## absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY,
## zRangeSTY, likeFile, jack=jack, colour=colour,
## schecInit=schec, pcRange=None, bpt_type=bpt_type)
def lfEv2(jack=0):
"""LF evolution in ugriz with quadratic density evolution fitting."""
inFile = 'kcorrz.fits'
colour = 'c'
for iband in range(1):
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (20.3, 19.5, 19.4, 18.7, 18.2)[iband]
absMin = (-23.0, -24.0, -24.0, -25.0, -25.0)[iband]
absMinSTY = absMin
absMax = (-15.0, -16.0, -16.0, -17.0, -17.0)[iband]
absMaxSTY = absMax
absStep = 0.25
zlims = [[0.002, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.5]]
## zlims = [[0.002, 0.05], [0.05, 0.1], [0.1, 0.15], [0.15, 0.2],
## [0.2, 0.25], [0.25, 0.3], [0.3, 0.4], [0.4, 0.5]]
zRangeSTY = [0.002, 0.5]
schec = {'alpha': -1.1, 'Mstar': -18.4, 'Q': (3, 1), 'P': (1, 1)}
outFile = 'lf_evquad_%s.dat' % band
## likeFile = 'like_ev2_%s.dat' % band
likeFile = None
lf(inFile, outFile, iband, appMin, appMax, absMin, absMax, absStep,
zlims, absMinSTY, absMaxSTY, zRangeSTY, likeFile, jack=jack,
colour=colour, schecInit=schec)
def lfFaint(inFile='kcorrz.fits', absStep=0.25, bands=range(5), colours='cbr',
zmax=0.1, like=0, jack=0):
"""Standard Schechter fits at low redshifts"""
likeFile = None
for iband in bands:
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (20.3, 19.5, 19.4, 18.7, 18.2)[iband]
absMin = (-21.0, -22.0, -23.0, -23.0, -24.0)[iband]
absMax = (-10.0, -10.0, -10.0, -11.0, -12.0)[iband]
absMinSTY = absMin
absMaxSTY = absMax
zlims = [[0.002, zmax]]
zRangeSTY = [0.002, zmax]
schec = {'alpha': -1, 'Mstar': -21, 'sigma': 0.2}
for colour in colours:
outFile = 'lf_faint_%s%s.dat' % (band, colour)
if like:
likeFile = 'like_faint_%s%s.dat' % (band, colour)
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec)
def lfSDSSFaint(inFile='kcorrz.fits', absStep=0.25, bands=range(5),
colours='cbr', like=0, jack=0):
"""Standard Schechter fits at low redshifts"""
likeFile = None
for iband in bands:
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (19.0, 18.0, 17.7, 17.2, 16.8)[iband]
absMin = (-21.0, -22.0, -23.0, -23.0, -24.0)[iband]
absMax = (-10.0, -10.0, -10.0, -11.0, -12.0)[iband]
absMinSTY = absMin
absMaxSTY = absMax
zlims = [[0.002, 0.1]]
zRangeSTY = [0.002, 0.1]
schec = {'alpha': -1, 'Mstar': -21}
for colour in colours:
outFile = 'lf_faint_%s%s.dat' % (band, colour)
if like:
likeFile = 'like_faint_%s%s.dat' % (band, colour)
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec, pcRange=None)
def lfFaintGroup(inFile='kcorrz.fits', absStep=0.5, bands=5, colours='cbr',
like=0, jack=0):
"""Standard Schechter fits at low redshifts in groups"""
likeFile = None
## for iband in range(5):
for iband in range(2,3):
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (20.3, 19.5, 19.4, 18.7, 18.2)[iband]
absMin = (-21.0, -22.0, -23.0, -23.0, -24.0)[iband]
absMax = (-14.0, -14.0, -14.0, -15.0, -16.0)[iband]
absMinSTY = absMin
absMaxSTY = absMax
zlims = [[0.01255, 0.1]]
zRangeSTY = [0.01255, 0.1]
schec = {'alpha': -1, 'Mstar': -21}
for colour in colours:
outFile = 'lf_faint_%s%s.dat' % (band, colour)
if like:
likeFile = 'like_faint_%s%s.dat' % (band, colour)
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec)
def doGroups():
gama_data = os.environ['GAMA_DATA']
dirs = (gama_data + '/v16/dr6/tonryz01_noimcorr/nogroup',
gama_data + '/v16/dr6/tonryz01_noimcorr/ingroup',
gama_data + '/v16/dr6/tonryz01_noimcorr/ingroup/lomass',
## gama_data + '/v16/dr6/tonryz01_noimcorr/ingroup/mimass',
gama_data + '/v16/dr6/tonryz01_noimcorr/ingroup/himass',
gama_data + '/v16/dr6/tonryz01_noimcorr/ingroup/lodisp',
## gama_data + '/v16/dr6/tonryz01_noimcorr/ingroup/midisp',
gama_data + '/v16/dr6/tonryz01_noimcorr/ingroup/hidisp',
gama_data + '/v16/dr6/tonryz01_noimcorr/ingroup/lorich',
## gama_data + '/v16/dr6/tonryz01_noimcorr/ingroup/mirich',
gama_data + '/v16/dr6/tonryz01_noimcorr/ingroup/hirich',
)
for dir in dirs:
os.chdir(dir)
lfFaintGroup(like=1)
def lfDP(inFile='kcorrz.fits', like=0, jack=0):
"""Double power-law Schechter fit to LF faint-end LF at low redshifts"""
# Initial guess at dp fit parameters [alpha, M*, beta, Mt]
dpPar = {'c': [[-0.1, -18.1, -1.4, -17.7],
[0.1, -19.4, -1.4, -19.3],
[0.1, -20.1, -1.4, -20.0],
[0.1, -20.5, -1.4, -20.2],
[0.1, -20.7, -1.5, -20.3]],
'b': [[-1.4, -18.5, -0.1, -18],
[-1.5, -19.7, -0.1, -15],
[-1.5, -20.4, -0.1, -19.5],
[-1.0, -20.5, -0.1, -19.9],
[-1.3, -21.0, -0.1, -20.5]],
'r': [[0.1, -17.6, -1.2, -15.7],
[0.1, -19.5, -1.2, -17.7],
[0.1, -20.1, -1.3, -18.0],
[-0.1, -20.6, -1.3, -17.4],
[-0.1, -20.9, -1.4, -17.7]]}
likeFile = None
for iband in range(5):
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (20.3, 19.5, 19.4, 18.7, 18.2)[iband]
absMin = (-21.0, -22.0, -23.0, -23.0, -24.0)[iband]
absMax = (-10.0, -10.0, -10.0, -12.0, -12.0)[iband]
absMinSTY = absMin
absMaxSTY = absMax
absStep = 0.25
zlims = [[0.002, 0.1]]
zRangeSTY = [0.002, 0.1]
for colour in 'cbr':
## for colour in 'r':
schec = {}
schec['alpha'], schec['Mstar'], schec['beta'], schec['Mt'] = dpPar[colour][iband]
outFile = 'lf_dp_%s%s.dat' % (band, colour)
if like and colour != 'b':
likeFile = 'like_dp_%s%s.dat' % (band, colour)
else:
likeFile = None
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec)
def lfSDSSDP(inFile='kcorrz.fits', like=0, jack=0):
"""Double power-law Schechter fit to LF faint-end LF at low redshifts"""
# Initial guess at dp fit parameters [alpha, M*, beta, Mt]
dpPar = {'c': [[-0.1, -18.1, -1.4, -17.7],
[0.1, -19.4, -1.4, -19.3],
[0.1, -20.1, -1.4, -20.0],
[0.1, -20.5, -1.4, -20.2],
[0.1, -20.7, -1.5, -20.3]],
'b': [[-1.4, -18.5, -0.1, -18],
[-1.5, -19.7, -0.1, -15],
[-1.5, -20.4, -0.1, -19.5],
[-1.0, -20.5, -0.1, -19.9],
[-1.3, -21.0, -0.1, -20.5]],
'r': [[0.1, -17.6, -1.2, -15.7],
[0.1, -19.5, -1.2, -17.7],
[0.1, -20.1, -1.3, -18.0],
[-0.1, -20.6, -1.3, -17.4],
[-0.1, -20.9, -1.4, -17.7]]}
likeFile = None
for iband in range(5):
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (19.0, 18.0, 17.7, 17.2, 16.8)[iband]
absMin = (-21.0, -22.0, -23.0, -23.0, -24.0)[iband]
absMax = (-10.0, -10.0, -10.0, -12.0, -12.0)[iband]
absMinSTY = absMin
absMaxSTY = absMax
absStep = 0.25
zlims = [[0.002, 0.1]]
zRangeSTY = [0.002, 0.1]
for colour in 'cbr':
## for colour in 'r':
schec = {}
schec['alpha'], schec['Mstar'], schec['beta'], schec['Mt'] = dpPar[colour][iband]
outFile = 'lf_dp_%s%s.dat' % (band, colour)
if like and colour != 'b':
likeFile = 'like_dp_%s%s.dat' % (band, colour)
else:
likeFile = None
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY, zRangeSTY,
likeFile, jack=jack, colour=colour, schecInit=schec, pcRange=None)
def lfSim(nz=4, like=0):
"""LF for sims"""
iband = 2
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (20.5, 19.5, 19.4, 18.5, 18.0)[iband]
absMin = (-22.0, -23.0, -24.0, -25.0, -25.0)[iband]
absMax = (-10.0, -10.0, -10.0, -12.0, -12.0)[iband]
absMinSTY = absMin
absMaxSTY = absMax
absStep = 0.25
if nz == 4:
zlims = [[0.002, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.4]]
if nz == 8:
zlims = [[0.002, 0.05], [0.05, 0.1], [0.1, 0.15], [0.15, 0.2],
[0.2, 0.25], [0.25, 0.3], [0.3, 0.35], [0.35, 0.4]]
schec = {'alpha': -1.3, 'Mstar': -20.8, 'Q': 0.1, 'P': 0.1}
for i in range(1, 9):
inFile = 'spSim%d.fits' % i
outFile = 'lf_ev{}_{}.dat'.format(nz, i)
if like:
likeFile = 'like_spSim%d.dat' % i
else:
likeFile = None
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY,
likeFile=likeFile, colour='c', schecInit=schec, pcRange=None)
def lfSimFaint():
"""Faint-end LF for non-evolving r-band sims"""
iband = 2
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (20.5, 19.5, 19.4, 18.5, 18.0)[iband]
absMin = (-22.0, -23.0, -24.0, -25.0, -25.0)[iband]
absMax = (-10.0, -10.0, -10.0, -12.0, -12.0)[iband]
absMinSTY = (-20.0, -22.0, -25.0, -25.0, -25.0)[iband]
absMaxSTY = absMax
absStep = 0.25
zlims = [[0.002, 0.1]]
schec = {'alpha': -1.3, 'Mstar': -20.8}
for i in range(1, 9):
inFile = 'spSim%d.fits' % i
outFile = 'lf_faint_spSim%d.dat' % i
## likeFile = 'like_faint_spSim%d.dat' % i
likeFile = None
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY,
likeFile, colour='c', schecInit=schec, pcRange=None)
def lfSimDP(like=0):
"""Double power-law Schechter fits to non-evolving sims"""
iband = 2
band = ('u', 'g', 'r', 'i', 'z')[iband]
appMin = 14
appMax = (20.5, 19.5, 19.4, 18.5, 18.0)[iband]
absMin = (-22.0, -23.0, -24.0, -25.0, -25.0)[iband]
absMax = (-10.0, -10.0, -10.0, -12.0, -12.0)[iband]
absMinSTY = (-20.0, -22.0, -25.0, -25.0, -25.0)[iband]
absMaxSTY = absMax
absStep = 0.25
zlims = [[0.002, 0.1]]
schec = {'alpha': -1.2, 'Mstar': -20.8, 'beta': 0.1, 'Mt': -20.0}
for i in range(1, 9):
inFile = 'spSim%d.fits' % i
outFile = 'lf_dp_spsim%d.dat' % i
if like:
likeFile = 'like_dp_spSim%d.dat' % i
else:
likeFile = None
lf(inFile, outFile, iband, appMin, appMax,
absMin, absMax, absStep, zlims, absMinSTY, absMaxSTY,
likeFile, colour='c', schecInit=schec, pcRange=None)
def sim_lf(infile):
"Calculate LF for mlum.simcat simulation."
schec = {'alpha': -1.3, 'Mstar': -20.8}
lf(infile, 'sim_lf.dat', 2, 14, 19.8, -23, -12, 22,
[[0.002, 0.5]], -23, -12, schecInit=schec)
def Nltz(inFile, zvals, Mmin, Mmax, colour='c'):
"""Return cumulative N(<z) distribution at given redshifts for generating
random distributions for clustering analyses."""
global par, plot
par = {'idebug': 0, 'iband': 2, 'band': 'r', 'colour': colour,
'appMin': 14.0, 'appMax': 19.4,
'absMin': Mmin, 'absMax': Mmax, 'absStep': 0.5,
'absMinSTY': Mmin, 'absMaxSTY': Mmax}
schec = {'alpha': (-1.23, 0.01), 'Mstar': (-20.70, 0.04), 'Q': 0.7, 'P': 1.8}
ev = Evol(schec['Q'], schec['P'])
zRange = (min(zvals), max(zvals))
plot = plotWindow(2,2)
samp = readGama(inFile, zRange, colour)
gamTab = gammaLookup()
sel = SelFn(schec, ev, gamTab, samp)
cum = np.zeros(len(zvals))
ndif = cosmo.dV(zvals)*sel.sel(zvals)
ncum = np.cumsum(ndif)
return ncum
#------------------------------------------------------------------------------
# Main procedure
#------------------------------------------------------------------------------
def lf(inFile, outFile, iband, appMin, appMax, absMin, absMax, absStep,
zlims, absMinSTY=None, absMaxSTY=None, zRangeSTY=None, likeFile=None,
jack=0, colour='c', schecInit=None):
"""Top-level function for LF estimation."""
global par, plot
par = {'progName': 'lum.py', 'version': '1.4', 'inFile': inFile,
'idebug': 1, 'iband': iband,
'band': ('u', 'g', 'r', 'i', 'z')[iband],
'colour': colour,
'nz': len(zlims), 'appMin': appMin, 'appMax': appMax,
'nbin': int(math.ceil((absMax - absMin)/absStep)),
'absMin': absMin, 'absMax': absMax, 'absStep': absStep,
'absMinSTY': absMinSTY, 'absMaxSTY': absMaxSTY,
'nNormMin': 10, 'J3fac': 30000.0, 'normType': 'vmax'}
zRange = [zlims[0][0], zlims[-1][1]]
if zRangeSTY is None:
zRangeSTY = zRange
plot = plotWindow(2,2)
print '\n************************\nlum.py version ', par['version']
print 'Band ', par['band'], 'colour ', par['colour'], sel_dict
schec = schecInit.copy()
fullSamp = readGama(inFile, zRange, colour)
galSamp = fullSamp.sub(zRangeSTY, par['absMinSTY'], par['absMaxSTY'])
ev, lumdens = parFit(galSamp, zlims, schec, likeFile, errorEst=1)
phiList = []
for i in range(len(zlims)):
phi = BinnedPhi(zlims[i], fullSamp.kmean)
galSamp = fullSamp.sub(zlims[i], phi.absMin, phi.absMax)
if galSamp.ngal > 0:
phi.zmean = np.mean(galSamp.z)
else:
phi.zmean = 0.0
phi.ntot = galSamp.ngal
phi.kmean = galSamp.kmean.tolist()
if galSamp.ngal > 0:
vmax(galSamp, phi)
phi.sty = schecBin(schec, ev, phi)
swml(galSamp, phi, schec)
phiList.append(phi)
f = open(outFile, 'w')
print >> f, par
print >> f, schec
print >> f, lumdens
for phi in phiList:
phi.save(f)
f.close()
plot.save()
# Jacknife resampling
if jack:
for ijack in range(9):
print 'Jacknife ', ijack
jackFile = outFile.split('.')[0] + '_j%d' % ijack + '.dat'
schec = schecInit.copy()
jackSamp = fullSamp.jacknife(ijack)
galSamp = jackSamp.sub(zRangeSTY, par['absMinSTY'], par['absMaxSTY'])
ev, lumdens = parFit(galSamp, zlims, schec, None, errorEst=0)
phiList = []
for i in range(len(zlims)):
phi = BinnedPhi(zlims[i], fullSamp.kmean)
galSamp = jackSamp.sub(zlims[i], phi.absMin, phi.absMax)
if galSamp.ngal > 0:
phi.zmean = np.mean(galSamp.z)
else:
phi.zmean = 0.0
phi.ntot = galSamp.ngal
phi.kmean = galSamp.kmean.tolist()
if galSamp.ngal > 0:
vmax(galSamp, phi)
phi.sty = schecBin(schec, ev, phi)
swml(galSamp, phi, schec)
phiList.append(phi)
f = open(jackFile, 'w')
print >> f, par
print >> f, schec
print >> f, lumdens
for phi in phiList:
phi.save(f)
f.close()
plot.save()
def readGama(file, zRange, colour):
"""Read GAMA data from fits file."""
global par, cosmo
iband = par['iband']
appMin = par['appMin']
appMax = par['appMax']
absMin = par['absMin']
absMax = par['absMax']
absStep = par['absStep']
## fname = '/Users/loveday/Data/gama/' + file
hdulist = pyfits.open(file)
header = hdulist[1].header
try:
par['H0'] = header['H0']
except:
par['H0'] = 100.0
par['omega_l'] = header['OMEGA_L']
par['z0'] = header['Z0']
par['area'] = header['AREA']
par['area'] *= (math.pi/180.0)*(math.pi/180.0)
print 'H0, omega_l, z0, area/Sr = ', par['H0'], par['omega_l'], par['z0'], par['area']
# Look for any simulation parameters in header
try:
par['sim_alpha'] = header['ALPHA']
par['sim_Mstar'] = header['MSTAR']
par['sim_phistar'] = header['PHISTAR']
par['sim_Q'] = header['Q']
is_sim = 1
except:
is_sim = 0
# Look for any group selection parameters in header
try:
par['grpsel'] = header['GRPSEL']
except:
pass
cosmo = CosmoLookup(par['H0'], par['omega_l'], zRange, is_sim=is_sim)
tbdata = hdulist[1].data
ra = tbdata.field('ra')
z = tbdata.field('z')
zConf = tbdata.field('zConf')
appMag = tbdata.field('appMag')[:,iband]
absMag = tbdata.field('absMag')[:,iband]
kc = tbdata.field('kcorr')[:,iband]
weight = tbdata.field('weight')[:,iband]
idx = (zRange[0] < z) * (z < zRange[1]) * (zConf > 0.8) * \
(appMin < appMag) * (appMag < appMax) * \
(absMin < absMag) * (absMag < absMax) * \
(np.isfinite(weight))
if colour != 'c':
grcut = colourCut(tbdata.field('absMag')[:,2])
gr = (tbdata.field('modelMagCor_g') - tbdata.field('kcorr')[:,1]) - \
(tbdata.field('modelMagCor_r') - tbdata.field('kcorr')[:,2])
if colour == 'b':
idx *= (gr < grcut)
else:
idx *= (gr >= grcut)
for key, limits in sel_dict.iteritems():
print key, limits
idx *= (tbdata.field(key) >= limits[0])*(tbdata.field(key) < limits[1])
par[key] = limits
## # If post_class file specified, select objects within pcRange
## if par['pcRange']:
## fin = open(pcFile, 'r')
## first = 1
## pcd = {}
## for line in fin:
## if first:
## first = 0
## else:
## data = line.split()
## id = int(data[0])
## pcd[id] = int(data[3])
## fin.close()
## cataid = tbdata.field('cataid')
## pca = np.array([pcd[id] for id in cataid], dtype=int)
## idx *= (pca >= par['pcRange'][0])*(pca <= par['pcRange'][1])
## if bpt_type:
## bpt = tbdata.field('bpt_type')
## idx *= (bpt == bpt_type)
ra = ra[idx]
z = z[idx]
absMag = absMag[idx]
kc = kc[idx]
weight = weight[idx]
if iband == 0: kcoeff = tbdata.field('kcoeffu')[idx,:]
if iband == 1: kcoeff = tbdata.field('kcoeffg')[idx,:]
if iband == 2: kcoeff = tbdata.field('kcoeffr')[idx,:]
if iband == 3: kcoeff = tbdata.field('kcoeffi')[idx,:]
if iband == 4: kcoeff = tbdata.field('kcoeffz')[idx,:]
hdulist.close()
ngal = z.size
print ngal, ' galaxies selected'
# Check K(z) reconstruction
zbin = np.linspace(zRange[0], zRange[1], 50)
for igal in range(4):
print kcoeff[igal,:]
kcz = np.polyval(kcoeff[igal], zbin-par['z0'])
plot.window()
plt.plot(zbin, kcz)
plt.xlabel('z')
plt.ylabel('K(z)')
plt.draw()
nv = 20
Vhist = np.zeros(nv)
phiBin = np.floor((absMag - absMin)/absStep).astype(np.int32)
dm = 25 + 5*np.log10(cosmo.dl(z)) + kc
Mmin = np.clip(appMin - dm, absMin, absMax)
Mmax = np.clip(appMax - dm, absMin, absMax)
r = cosmo.dm(z)
z1 = map(lambda i: zdm(appMin - absMag[i], kcoeff[i], zRange), xrange(ngal))
r1 = cosmo.dm(z1)
z2 = map(lambda i: zdm(appMax - absMag[i], kcoeff[i], zRange), xrange(ngal))
r2 = cosmo.dm(z2)
vol = r*r*r - r1*r1*r1
Vm = r2*r2*r2 - r1*r1*r1
VVm = vol/Vm
plot.window()
plt.hist(VVm, bins=20, range=(0,1), weights=weight)
plt.xlabel('V/Vmax')
plt.ylabel('Frequency')
plt.draw()
idx = VVm < 0
print len(VVm[idx]), ' galaxies with V/Vmax < 0'
idx = VVm > 1.01
print len(VVm[idx]), ' galaxies with V/Vmax > 1.01'
fullSamp = Sample(zRange, par['area'], z, weight, absMag,
Mmin, Mmax, r1, r2, phiBin, kcoeff, ra)
return fullSamp
#------------------------------------------------------------------------------
# Parametric fitting procedures
#------------------------------------------------------------------------------
def parFit(samp, zlims, schec, likeFile, errorEst=0):
"""Parametric fit to LF. Type of fit (standard Schechter, DP schechter
or evolving is determined from the keys in the schec dictionary."""
print "Parametric fit..."
schec['zRangeSty'] = samp.zRange
schec['ngal'] = samp.ngal
## if not(schec.has_key('sigma')):
print "Loading Gamma fn lookup table..."
gamTab = gammaLookup()
# First we fit for shape and any luminosity evolution parameters
x0 = [schec['alpha'], schec['Mstar']]
parSet = ['alpha', 'Mstar']
lower = [parLimits['alpha'][0], parLimits['Mstar'][0]]
upper = [parLimits['alpha'][1], parLimits['Mstar'][1]]
cons = [con_alo, con_ahi, con_mslo, con_mshi]
if schec.has_key('sigma'):
x0 += [schec['sigma']]
parSet += ['sigma']
lower += [parLimits['sigma'][0]]
upper += [parLimits['sigma'][1]]
if schec.has_key('Q'):
x0 += [schec['Q']]
parSet += ['Q']
lower += [parLimits['Q'][0]]
upper += [parLimits['Q'][1]]
if schec.has_key('beta'):
x0 += [schec['beta'], schec['Mt']]
parSet += ['beta', 'Mt']
lower += [parLimits['beta'][0], parLimits['Mt'][0]]
upper += [parLimits['beta'][1], parLimits['Mt'][1]]
cons += [con_blo, con_bhi, con_mtlo, con_mthi]
schec['nShapePar'] = len(parSet)
print "Fitting ", parSet, x0
# Call fmin repeatedly until value of minimum changes by less than 1e-6
ftol = 1e-6
fold = 1
df = 1
while df > ftol:
result = scipy.optimize.fmin(
parLike, x0, args=(parSet, gamTab, samp), ftol=ftol,
full_output=1)
xopt = result[0]
fopt = result[1]
print xopt, fopt
df = abs(fopt - fold)/fopt
fold = fopt
x0 = xopt
## xopt = scipy.optimize.fmin_cobyla(parLike, x0, cons,
## args=(parSet, gamTab, samp),
## rhoend=0.00001)
## print 'fmin_cobyla:', xopt
## result = scipy.optimize.anneal(parLike, x0,
## args=(parSet, gamTab, samp),
## full_output=1,
## lower=lower, upper=upper)
## print 'anneal:', result
# Errors on each parameter, marginalising over other parameters
npar = len(parSet)
if errorEst:
for i in range(npar):
imarg = filter(lambda j: j != i, range(npar))
parOrd = [parSet[j] for j in imarg] + [parSet[i]]
schec[parSet[i]] = (xopt[i], likeErr(
lambda marg, x, parOrd, gamTab, samp:
parLike(np.hstack((marg, x)), parOrd, gamTab, samp),
xopt[i], cons, limits=parLimits[parSet[i]],
marg=(xopt[imarg]), args=(parOrd, gamTab, samp)))
print parSet[i], schec[parSet[i]]
else:
for i in range(npar):
schec[parSet[i]] = (xopt[i], [0, 0])
if schec.has_key('P'):
# Now fit for density evolution P
print "Fitting P ..."
ev = Evol(schec['Q'][0])
if par['idebug'] > 1:
dz = (samp.zRange[1] - samp.zRange[0])/100
zarr = np.arange(samp.zRange[0], samp.zRange[1], dz)
rarr = rhofun(zarr, 0.0)
plot.window()
plt.plot(zarr, rarr)
plt.xlabel('z'); plt.ylabel('dV/dz'); plt.draw()
# Redshift limits for each galaxy
Mevol = samp.absMag + ev.lum(samp.z)
z1 = map(lambda i: zdm(par['appMin'] - Mevol[i], samp.kcoeff[i],
samp.zRange, ev), xrange(samp.ngal))
z2 = map(lambda i: zdm(par['appMax'] - Mevol[i], samp.kcoeff[i],
samp.zRange, ev), xrange(samp.ngal))
x0 = [schec['P']]
xopt = scipy.optimize.fmin(rhoLike, x0, (samp, z1, z2),
xtol=0.001, ftol=0.001)
PErr = 0
if len(x0) == 1:
P = xopt[0]
if errorEst:
PErr = likeErr(rhoLike, P, limits=parLimits['P'],
args=(samp, z1, z2))
else:
P = xopt.tolist()
if errorEst:
PErr = []
PErr.append(likeErr(lambda P1, P0, samp, z1, z2:
rhoLike((P0, P1), samp, z1, z2),
P[0], marg=(P[1]), args=(samp, z1, z2)))
PErr.append(likeErr(lambda P0, P1, samp, z1, z2:
rhoLike((P0, P1), samp, z1, z2),
P[1], marg=(P[0]), args=(samp, z1, z2)))
schec['P'] = (P, PErr)
print 'P', schec['P']
ev = Evol(schec['Q'][0], schec['P'][0])
else:
ev = Evol(0, 0)
z1 = z2 = None
# Minimum-variance normalization
if schec.has_key('sigma'):
sel = SaundSelFn(schec, ev, samp)
else:
sel = SelFn(schec, ev, gamTab, samp)
selg = sel.sel(samp.z)
wmean = samp.weight.mean()
plot.window()
plt.plot(sel._z, sel._sel)
plt.xlabel('z')
plt.ylabel('STY S(z)')
plt.draw()
# Simple unweighted normalisation (Lin et al eq 14)
## idx = selg > 0
## sum1 = (samp.weight[idx]/selg[idx]/ev.den(samp.z[idx])).sum()
## rmin = cosmo.dm(np.min(samp.z[idx]))
## rmax = cosmo.dm(np.max(samp.z[idx]))
## vol = (rmax**3 - rmin**3)*samp.area/3.0
## dens = sum1/vol
## dErr = math.sqrt(dens/vol)
dens = 0.1 # first guess
tol = 0.001
err = 1
niter = 0
maxiter = 100