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vide_public/analysis/xcor.py
Guilhem Lavaux 29723fff06 updated copyright notice
2014-04-28 08:52:27 +02:00

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#+
# VIDE -- Void IDentification and Examination -- ./analysis/xcor.py
# Copyright (C) 2010-2014 Guilhem Lavaux
# Copyright (C) 2011-2014 P. M. Sutter
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; version 2 of the License.
#
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#+
#!/usr/bin/env python
#+
# VIDE -- Void IDentification and Examination -- ./crossCompare/analysis/mergerTree.py
# Copyright (C) 2010-2013 Guilhem Lavaux
# Copyright (C) 2011-2013 P. M. Sutter
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; version 2 of the License.
#
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#+
from void_python_tools.backend import *
from void_python_tools.plotting import *
import void_python_tools.xcor as xcorlib
import imp
import pickle
import argparse
import os
import string
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib import rc
from matplotlib.ticker import NullFormatter
import random
import sys
# ------------------------------------------------------------------------------
dataNameBase = "xcor"
parser = argparse.ArgumentParser(description='Analyze.')
parser.add_argument('--parmFile', dest='parmFile', default='datasetsToAnalyze.py',
help='path to parameter file')
args = parser.parse_args()
# ------------------------------------------------------------------------------
filename = args.parmFile
print " Loading parameters from", filename
if not os.access(filename, os.F_OK):
print " Cannot find parameter file %s!" % filename
exit(-1)
parms = imp.load_source("name", filename)
globals().update(vars(parms))
if not os.access(outputDir, os.F_OK):
os.makedirs(outputDir)
for (iSample, sampleDir) in enumerate(sampleDirList):
with open(voidBaseDir+sampleDir+"/sample_info.dat", 'rb') as input:
sample = pickle.load(input)
print " Working with", sample.fullName, "...",
sys.stdout.flush()
sampleName = sample.fullName
# Sim parameters
Lbox = sample.boxLen # Boxlength [h^(-1)Mpc]
Lbox -= 2*Lboxcut # Reduced boxlength [h^(-1)Mpc]
Om = sample.omegaM # Omega_m
Ol = 1.-Om # Omega_l
z = sample.zRange[0] # Redshift
a = 1./(1.+z) # Scale factor
rho_m = Mpart*(Ni/Lbox)**3 # Background density [(h/Mpc)^3]
# Input files
voidDir = voidBaseDir+'/'+sampleDir+'/'
voidFilename1 = 'centers_central_'+sample.fullName+'.out'
voidFilename2 = 'voidDesc_central_'+sample.fullName+'.out'
# Read files
matter_file = open(matterDir+matterFilename,'r')
matter_data = []
count = 0
sep = 1e8
for (i,line) in enumerate(matter_file):
if i/int(sep) == i/sep: print str(round(i*100./Ni**3,1))+' percent of all particles read'
if random.random() > ss: continue
count += 1
matter_data.append(line.split(',')[0:3])
print str(count)+' particles read in total'
matter_data = np.asarray(matter_data,dtype=np.float32)
matter_file.close()
halo_file = open(haloDir+haloFilename,'r')
halo_data = np.reshape(halo_file.read().replace('\n',',').split(',')[0:-1],(-1,12)).astype(np.float32)
halo_file.close()
void_file = open(voidDir+voidFilename1,'r')
void_header1 = void_file.readline().split(",")
void_data1 = np.reshape(void_file.read().split(),(-1,len(void_header))).astype(np.float32)
void_file.close()
void_file = open(voidDir+voidFilename2,'r')
void_header2 = void_file.readline().split(",")+void_file.readline().split(",")
void_data2 = np.reshape(void_file.read().split(),(-1,11)).astype(np.float32)
void_file.close()
# Define arrays
xm = matter_data[:,0:3]
xh = halo_data[:,0:3]
mh = halo_data[:,6]
xv = void_data1[:,0:3]
rv = void_data1[:,4]
vv = void_data1[:,6]
mv = void_data2[:,8]*Mpart
# Interpolate to mesh
dm, wm, ws = xcorlib.cic(xm, Lbox, Lboxcut = Lboxcut, Nmesh = Nmesh)
dh, wm, ws = xcorlib.cic(xh, Lbox, Lboxcut = Lboxcut, Nmesh = Nmesh)
dv, wm, ws = xcorlib.cic(xv, Lbox, Lboxcut = Lboxcut, Nmesh = Nmesh)
# Load dark matter grid
#output = open('dm_'+str(Nmesh)+'_ss'+str(ss)+'_z'+str(z)+'.dat', 'rb')
#dm = pickle.load(output)
#output.close()
# Save dark matter grid
#output = open('dm_'+str(Nmesh)+'_ss'+str(ss)+'_z'+str(z)+'.dat', 'wb')
#pickle.dump(dm,output)
#output.close()
# Power spectra & correlation functions
((Nm, km, Pmm, SPmm),(Nmx, rm, Xmm, SXmm)) = xcorlib.powcor(dm, dm, Lbox, Nmesh = Nmesh, Nbin = Nbin, scale = 'lin', cor = True)
((Nm, km, Pvm, SPvm),(Nmx, rm, Xvm, SXvm)) = xcorlib.powcor(dv, dm, Lbox, Nmesh = Nmesh, Nbin = Nbin, scale = 'lin', cor = True)
((Nm, km, Phm, SPhm),(Nmx, rm, Xhm, SXhm)) = xcorlib.powcor(dh, dm, Lbox, Nmesh = Nmesh, Nbin = Nbin, scale = 'lin', cor = True)
((Nm, km, Pvv, SPvv),(Nmx, rm, Xvv, SXvv)) = xcorlib.powcor(dv, dv, Lbox, Nmesh = Nmesh, Nbin = Nbin, scale = 'lin', cor = True)
((Nm, km, Pvh, SPvh),(Nmx, rm, Xvh, SXvh)) = xcorlib.powcor(dv, dh, Lbox, Nmesh = Nmesh, Nbin = Nbin, scale = 'lin', cor = True)
((Nm, km, Phh, SPhh),(Nmx, rm, Xhh, SXhh)) = xcorlib.powcor(dh, dh, Lbox, Nmesh = Nmesh, Nbin = Nbin, scale = 'lin', cor = True)
# Number densities
nm = np.empty(len(km))
nh = np.empty(len(km))
nv = np.empty(len(km))
nm[:] = Npart/Lbox**3
nh[:] = len(xh)/Lbox**3
nv[:] = len(xv)/Lbox**3
# Number functions
Mbin = 40
Vbin = 40
Nh, Mh = np.histogram(mh, bins = mh.min()*(mh.max()/mh.min())**(np.arange(Mbin+1)/float(Mbin)))
Nvm, Mv = np.histogram(mv, bins = mv.min()*(mv.max()/mv.min())**(np.arange(Vbin+1)/float(Vbin)))
Nvv, Vv = np.histogram(vv, bins = vv.min()*(vv.max()/vv.min())**(np.arange(Vbin+1)/float(Vbin)))
# Bias
b_hh = np.sqrt(Phh/Pmm)
b_vv = np.sqrt(Pvv/Pmm)
b_hm = Phm/Pmm
b_vm = Pvm/Pmm
b_vh = Pvh/Phh
knl = 0.04 # Wavenumber above which nonlinearities kick in [h/Mpc]
idls = np.where(km <= knl)[0]
bm_hm = np.average(b_hm[idls],weights=Nm[idls])
bm_vm = np.average(b_vm[idls],weights=Nm[idls])
bm_vh = np.average(b_vh[idls],weights=Nm[idls])
# Shot Noise
sn_hh = Phh - Phm**2/Pmm
sn_vh = Pvh - Pvm*Phm/Pmm
sn_vv = Pvv - Pvm**2/Pmm
# Plots
ms = 4
mew = 0.2
margin = 1.2
kmin = km.min()/margin
kmax = km.max()*margin
rmin = rm.min()/margin
rmax = rm.max()*margin
plt.imshow(np.sum(dm+1,2)/Nmesh,extent=[0,Lbox,0,Lbox],aspect='equal',cmap='YlGnBu_r',interpolation='gaussian')
plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
plt.title(r'Dark matter')
plt.colorbar()
plt.savefig(outputDir+'/dm_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
plt.clf()
plt.imshow(np.sum(dv+1,2)/Nmesh,extent=[0,Lbox,0,Lbox],aspect='equal',cmap='YlGnBu_r',interpolation='gaussian')
plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
plt.title(r'Voids')
plt.colorbar()
plt.savefig(outputDir+'/dv_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
plt.clf()
plt.imshow(np.sum(dh+1,2)/Nmesh,extent=[0,Lbox,0,Lbox],aspect='equal',cmap='YlGnBu_r',interpolation='gaussian')
plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
plt.title(r'Halos')
plt.colorbar()
plt.savefig(outputDir+'/dh_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
plt.clf()
pa, = plt.plot(Mh[:-1], Nh, 'ro-', ms=ms, mew=mew)
pb, = plt.plot(Vv[:-1]*1e9, Nvv, 'bo-', ms=ms, mew=mew)
plt.xlabel(r'$M \;[h^{-1}M_{\odot}]$ , $V \;[h^{-3}\mathrm{kpc}^3]$')
plt.ylabel(r'$N(M,V)$')
plt.title(r'Number of halos and voids')
plt.xscale('log')
plt.yscale('log')
plt.xlim(min(10**np.floor(np.log10(Vv.min()))*1e9,10**np.floor(np.log10(Mh.min()))), max(10**np.ceil(np.log10(Mh.max())),10**np.ceil(np.log10(Vv.max()))*1e8))
plt.ylim(10**np.floor(np.log10(Nh.min())), 10**np.ceil(np.log10(Nh.max())))
plt.annotate(r'$\frac{4\pi}{3}\langle r_\mathrm{v}\rangle^3$', xy=(4./3.*np.pi*rv.mean()**3*1e9,1.1), xytext=(-50,235),textcoords='offset points',arrowprops=dict(fc='k',arrowstyle="->",connectionstyle="angle,angleA=0,angleB=90,rad=10"))
plt.legend([pa,pb],['halos','voids'],'best' )
plt.savefig(outputDir+'/number_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
plt.clf()
plt.subplot(211)
plt.subplots_adjust(wspace=0,hspace=0)
plt.plot(np.sort(vv), mv[np.argsort(vv)], 'r-', ms=ms, mew=mew, lw=0.01)
plt.plot(np.sort(vv), np.sort(mv), 'k-', ms=ms, mew=mew)
plt.plot(np.sort(vv), np.sort(vv)*1e9, 'k--', ms=ms, mew=mew)
plt.title(r'Mass-volume relation of voids')
plt.ylabel(r'$M \;[h^{-1}M_\odot]$')
plt.xscale('log')
plt.yscale('log')
plt.subplot(211).xaxis.set_major_formatter(NullFormatter())
plt.subplot(212)
plt.plot(np.sort(vv), mv[np.argsort(vv)]/np.sort(vv)/rho_m-1., 'b-', ms=ms, mew=mew, lw=0.01)
plt.plot(np.sort(vv), np.sort(mv)/np.sort(vv)/rho_m-1., 'k-', ms=ms, mew=mew)
plt.xlabel(r'$V \;[h^{-3}\mathrm{Mpc}^3]$')
plt.ylabel(r'$\delta$')
plt.xscale('log')
plt.yscale('linear')
plt.ylim(-1.01,-0.861)
plt.savefig(outputDir+'/massvol_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
plt.clf()
pa ,= plt.plot(km, Phh, 'r-', ms=ms, mew=mew)
plt.plot(km, Phh-sn_hh, 'r:', ms=ms, mew=mew)
plt.fill_between(km, Phh+SPhh, abs(Phh-SPhh), color='r', alpha=0.2)
pb ,= plt.plot(km, Phm, 'y-', ms=ms, mew=mew)
plt.fill_between(km, Phm+SPhm, abs(Phm-SPhm), color='y', alpha=0.2)
pc ,= plt.plot(km, Pmm, 'k-', ms=ms, mew=mew)
plt.plot(km, Pmm-1./nm, 'k:', ms=ms, mew=mew)
plt.fill_between(km, Pmm+SPmm, abs(Pmm-SPmm), color='k', alpha=0.2)
pd ,= plt.plot(km, Pvh, 'g-', ms=ms, mew=mew)
plt.plot(km, -Pvh, 'g--', ms=ms, mew=mew)
plt.plot(km, abs(Pvh-sn_vh), 'g:', ms=ms, mew=mew)
plt.fill_between(km, abs(Pvh+SPvh), abs(Pvh-SPvh), color='g', alpha=0.2)
pe ,= plt.plot(km, Pvm, 'm-', ms=ms, mew=mew)
plt.plot(km, -Pvm, 'm--', ms=ms, mew=mew)
plt.fill_between(km, abs(Pvm+SPvm), abs(Pvm-SPvm), color='m', alpha=0.2)
pf ,= plt.plot(km, Pvv, 'b-', ms=ms, mew=mew)
plt.plot(km, Pvv-sn_vv, 'b:', ms=ms, mew=mew)
plt.fill_between(km, Pvv+SPvv, abs(Pvv-SPvv), color='b', alpha=0.2)
plt.annotate(r'$\frac{\pi}{\langle r_\mathrm{v}\rangle}$', xy=(np.pi/(rv.mean()),1.01*10**np.floor(np.log10(abs(Pvh).min()))/margin), xytext=(10,280),textcoords='offset points',arrowprops=dict(fc='k',arrowstyle="->",connectionstyle="angle,angleA=0,angleB=90,rad=10"))
plt.xlabel(r'$k \;[h\mathrm{Mpc}^{-1}]$')
plt.ylabel(r'$P(k) \;[h^{-3}\mathrm{Mpc}^3]$')
plt.title(r'Power spectra')
plt.xscale('log')
plt.yscale('log')
plt.xlim(kmin,kmax)
plt.ylim(10**np.floor(np.log10(abs(Pvh).min()))/margin, max(10**np.ceil(np.log10(Phh.max())),10**np.ceil(np.log10(Pvv.max())))*margin)
plt.legend([pa, pb, pc, pd, pe, pf],['hh', 'hm', 'mm', 'vh', 'vm', 'vv'],'lower left' )
plt.savefig(outputDir+'/power_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
plt.clf()
pa ,= plt.plot(rm, Xhh, 'r-', ms=ms, mew=mew)
plt.fill_between(rm, abs(Xhh+SXhh), abs(Xhh-SXhh), color='r', alpha=0.2)
pb ,= plt.plot(rm, Xhm, 'y-', ms=ms, mew=mew)
plt.fill_between(rm, abs(Xhm+SXhm), abs(Xhm-SXhm), color='y', alpha=0.2)
pc ,= plt.plot(rm, Xmm, 'k-', ms=ms, mew=mew)
plt.fill_between(rm, abs(Xmm+SXmm), abs(Xmm-SXmm), color='k', alpha=0.2)
pd ,= plt.plot(rm, Xvh, 'g-', ms=ms, mew=mew)
plt.plot(rm, -Xvh, 'g--', ms=ms, mew=mew)
plt.fill_between(rm, abs(Xvh+SXvh), abs(Xvh-SXvh), color='g', alpha=0.2)
pe ,= plt.plot(rm, Xvm, 'm-', ms=ms, mew=mew)
plt.plot(rm, -Xvm, 'm--', ms=ms, mew=mew)
plt.fill_between(rm, abs(Xvm+SXvm), abs(Xvm-SXvm), color='m', alpha=0.2)
pf ,= plt.plot(rm, Xvv, 'b-', ms=ms, mew=mew)
plt.fill_between(rm, abs(Xvv+SXvv), abs(Xvv-SXvv), color='b', alpha=0.2)
plt.annotate(r'$\langle r_\mathrm{v}\rangle$', xy=(rv.mean(),1.01*10**np.floor(np.log10(abs(Xvh).min()))/margin), xytext=(10,300),textcoords='offset points',arrowprops=dict(fc='k',arrowstyle="->",connectionstyle="angle,angleA=0,angleB=90,rad=10"))
plt.xlabel(r'$r \;[h^{-1}\mathrm{Mpc}]$')
plt.ylabel(r'$\xi(r)$')
plt.title(r'Correlation functions')
plt.xscale('log')
plt.yscale('log')
plt.xlim(rmin,rmax)
plt.ylim(10**np.floor(np.log10(abs(Xvh).min()))/margin, max(10**np.ceil(np.log10(Xhh.max())),10**np.ceil(np.log10(Xvv.max())))*margin)
plt.legend([pa, pb, pc, pd, pe, pf],['hh', 'hm', 'mm', 'vh', 'vm', 'vv'],'lower left' )
plt.savefig(outputDir+'/correlation_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
plt.clf()
pa, = plt.plot(km, b_hh, 'r-', ms=ms, mew=mew)
pb, = plt.plot(km, b_hm, 'r--', ms=ms, mew=mew)
pc, = plt.plot(km, b_vv, 'b-', ms=ms, mew=mew)
pd, = plt.plot(km, b_vm, 'b--', ms=ms, mew=mew)
pe, = plt.plot(km, b_vh/bm_vh, 'g-', ms=ms, mew=mew)
plt.plot(km, np.sin(km*rv.mean())/(km*rv.mean()), 'k:', ms=ms, mew=mew)
plt.xlabel(r'$k \;[h\mathrm{Mpc}^{-1}]$')
plt.ylabel(r'$b(k)$')
plt.title(r'Bias')
plt.xscale('log')
plt.yscale('linear')
plt.xlim(kmin,kmax)
plt.ylim(np.floor(b_vm.min()),np.ceil(max(b_hh.max(),b_vv.max())))
plt.legend([pa,pb,pc,pd,pe],['hh', 'hm', 'vv', 'vm', r'$\bar{u}_\mathrm{v}(k)$'],'best' )
plt.savefig(outputDir+'/bias_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
plt.clf()
pa, = plt.plot(km, sn_hh, 'r-', ms=ms, mew=mew)
pb, = plt.plot(km, sn_vh, 'g-', ms=ms, mew=mew)
pc, = plt.plot(km, sn_vv, 'b-', ms=ms, mew=mew)
plt.plot(km, abs(sn_vh), 'g:')
pd, = plt.plot(km, 1/nh, 'r--')
pe, = plt.plot(km, 1/nv, 'b-.')
plt.xlabel(r'$k \;[h\mathrm{Mpc}^{-1}]$')
plt.ylabel(r'$\sigma^2(k)$')
plt.title(r'Shotnoise')
plt.xscale('log')
plt.yscale('log')
plt.xlim(kmin,kmax)
plt.ylim(10**np.floor(np.log10(abs(sn_vh).min())), 10**np.ceil(np.log10(sn_vv.max())))
plt.legend([pa,pb,pc,pd,pe],['hh', 'vh', 'vv', r'$\bar{n}_\mathrm{h}^{-1}$', r'$\bar{n}_\mathrm{v}^{-1}$'],'best' )
plt.savefig(outputDir+'/shotnoise_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
plt.clf()
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