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+ radial profiles now full actual density from survey volume, not zobov normalization

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+ getVolNorm provides both zobov normalization and average density from survey volume for observations

+ significant update and cleanup to plotting routines
This commit is contained in:
Paul M. Sutter 2025-03-04 14:07:36 -05:00
parent b79046ac22
commit 326756b2bc
8 changed files with 73 additions and 61 deletions
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34
python_source/voidUtil/xcorUtil.py
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@ -27,13 +27,15 @@ from voidUtil import getArray
def computeXcor(catalog,
figDir="./",
namePrefix="",
Nmesh = 256,
Nbin = 100
Nbin = 100,
):
# Computes and plots void-void and void-matter(galaxy) correlations
# catalog: catalog to analyze
# figDir: where to place plots
# namePrefix: prefix to add to file names
# Nmesh: number of grid cells in cic mesh-interpolation
# Nbin: number of bins in final plots
@ -84,18 +86,18 @@ def computeXcor(catalog,
plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
plt.title(r'Dark matter')
plt.savefig(figDir+'/dm.eps', bbox_inches="tight")
plt.savefig(figDir+'/dm.pdf', bbox_inches="tight")
plt.savefig(figDir+'/dm.png', bbox_inches="tight")
#plt.savefig(figDir+'/dm.eps', bbox_inches="tight")
plt.savefig(figDir+'/'+namePrefix+'dm.pdf', bbox_inches="tight")
plt.savefig(figDir+'/'+namePrefix+'dm.png', 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.savefig(figDir+'/dv.eps', bbox_inches="tight") #, dpi=300
plt.savefig(figDir+'/dv.pdf', bbox_inches="tight") #, dpi=300
plt.savefig(figDir+'/dv.png', bbox_inches="tight") #, dpi=300
#plt.savefig(figDir+'/dv.eps', bbox_inches="tight") #, dpi=300
plt.savefig(figDir+'/'+namePrefix+'dv.pdf', bbox_inches="tight") #, dpi=300
plt.savefig(figDir+'/'+namePrefix+'dv.png', bbox_inches="tight") #, dpi=300
plt.clf()
@ -116,10 +118,11 @@ def computeXcor(catalog,
plt.yscale('log')
plt.xlim(kmin,kmax)
plt.ylim(10**np.floor(np.log10(abs(Pvm[1:]).min()))/margin, max(10**np.ceil(np.log10(Pmm.max())),10**np.ceil(np.log10(Pvv.max())))*margin)
plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12})
plt.savefig(figDir+'/power.eps', bbox_inches="tight")
plt.savefig(figDir+'/power.pdf', bbox_inches="tight")
plt.savefig(figDir+'/power.png', bbox_inches="tight")
plt.legend([pa, pb, pc],['tt', 'vt', 'vv'])
#plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12})
#plt.savefig(figDir+'/power.eps', bbox_inches="tight")
plt.savefig(figDir+'/'+namePrefix+'power.pdf', bbox_inches="tight")
plt.savefig(figDir+'/'+namePrefix+'power.png', bbox_inches="tight")
plt.clf()
pa ,= plt.plot(rm, Xmm, 'k-o', ms=0.8*ms, mew=mew)
@ -137,10 +140,11 @@ def computeXcor(catalog,
plt.yscale('log')
plt.xlim(rmin,rmax)
plt.ylim(min(10**np.floor(np.log10(abs(Xvm).min())),10**np.floor(np.log10(abs(Xmm).min())))/margin, max(10**np.ceil(np.log10(Xmm.max())),10**np.ceil(np.log10(Xvv.max())))*margin)
plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12})
plt.savefig(figDir+'/correlation.eps', bbox_inches="tight")
plt.savefig(figDir+'/correlation.pdf', bbox_inches="tight")
plt.savefig(figDir+'/correlation.png', bbox_inches="tight")
plt.legend([pa, pb, pc],['tt', 'vt', 'vv'])
#plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12})
#plt.savefig(figDir+'/correlation.eps', bbox_inches="tight")
plt.savefig(figDir+'/'+namePrefix+'correlation.pdf', bbox_inches="tight")
plt.savefig(figDir+'/'+namePrefix+'correlation.png', bbox_inches="tight")
plt.clf()