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

Browse source 
+ 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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3
c_source/pruning/pruneVoids.cpp
View file

@ -667,7 +667,6 @@ int main(int argc, char **argv) {
interval = 1.*(clock4 - clock3)/CLOCKS_PER_SEC; interval = 1.*(clock4 - clock3)/CLOCKS_PER_SEC;
//printf(" %.2f for maximum extent\n", interval); //printf(" %.2f for maximum extent\n", interval);
/*
// compute distance from center to nearest mock boundary particle // compute distance from center to nearest mock boundary particle
// (with new boundary handling this will go away) // (with new boundary handling this will go away)
clock3 = clock(); clock3 = clock();
@ -686,7 +685,6 @@ int main(int argc, char **argv) {
} else { } else {
voids[iVoid].nearestMock = 1.e99; voids[iVoid].nearestMock = 1.e99;
} }
if (args.isObservation_flag) { if (args.isObservation_flag) {
voids[iVoid].redshiftInMpc = voids[iVoid].redshiftInMpc =
sqrt(pow(voids[iVoid].macrocenter[0] - boxLen[0]/2.,2) + sqrt(pow(voids[iVoid].macrocenter[0] - boxLen[0]/2.,2) +
@ -745,7 +743,6 @@ int main(int argc, char **argv) {
clock4 = clock(); clock4 = clock();
interval = 1.*(clock4 - clock3)/CLOCKS_PER_SEC; interval = 1.*(clock4 - clock3)/CLOCKS_PER_SEC;
//printf(" %.2f for nearest edge\n", interval); //printf(" %.2f for nearest edge\n", interval);
*/
// compute eigenvalues and vectors for orientation and shape // compute eigenvalues and vectors for orientation and shape
clock3 = clock(); clock3 = clock();

10
python_source/backend/surveyTools.py
View file

@ -194,11 +194,9 @@ def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
rayDist = meanPartSep rayDist = meanPartSep
raySteps = np.arange(zmin, zmax, rayDist) raySteps = np.arange(zmin, zmax, rayDist)
log.write(" Will take %d steps per ray with %.2e distance between steps\n" % (len(raySteps), rayDist)) log.write(" Will take %d steps per ray with %.2e distance between steps\n" % (len(raySteps), rayDist))
#print(meanPartSep, len(raySteps))
contourPixels = np.nonzero(contourMap)[0] contourPixels = np.nonzero(contourMap)[0]
log.write(" We have %d rays to work with\n" % (len(contourPixels))) log.write(" We have %d rays to work with\n" % (len(contourPixels)))
#print(contourPixels)
for pixel in contourPixels: for pixel in contourPixels:
#print("Working with pixel %d" % pixel) #print("Working with pixel %d" % pixel)
vec = healpy.pix2vec(nside,pixel) vec = healpy.pix2vec(nside,pixel)
@ -227,6 +225,11 @@ def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
dist, nearest = galTree.query(minEdge) dist, nearest = galTree.query(minEdge)
flagList[nearest] = 3 flagList[nearest] = 3
log.write(" Flagging statistics:\n")
log.write(" %d original galaxies\n" % len(flagList))
log.write(" %d near edges\n" % len(flagList[flagList==1]))
log.write(" %d near redshift bounds\n" % len(flagList[flagList==2]))
log.write(" %d remaining\n" % len(flagList[flagList==0]))
# output flag information # output flag information
log.write(" Saving galaxy flags to file...\n") log.write(" Saving galaxy flags to file...\n")
np.savetxt(edgeGalFile, flagList, fmt="%d") np.savetxt(edgeGalFile, flagList, fmt="%d")
@ -243,7 +246,8 @@ def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
ipix = healpy.ang2pix(nside, theta, phi) ipix = healpy.ang2pix(nside, theta, phi)
np.put(flagMap, ipix, 1) np.put(flagMap, ipix, 1)
healpy.write_map(outputDir+"/flagged_galaxies.fits", flagMap, overwrite=True, healpy.write_map(outputDir+"/flagged_galaxies.fits", flagMap,
overwrite=True,
dtype=np.dtype('float64')) dtype=np.dtype('float64'))

45
python_source/voidUtil/catalogUtil.py
View file

@ -115,6 +115,8 @@ def loadPart(sampleDir):
boxLen = mul boxLen = mul
# TODO - should use built-in volNorm function instead
#if isObservation == 1: #if isObservation == 1:
# # look for the mask file # # look for the mask file
# if os.access(sample.maskFile, os.F_OK): # if os.access(sample.maskFile, os.F_OK):
@ -165,26 +167,29 @@ def getVolNorm(sampleDir):
boxLen = mul boxLen = mul
#if isObservation == 1:
# # look for the mask file
# if os.access(sample.maskFile, os.F_OK):
# maskFile = sample.maskFile
# else:
# maskFile = sampleDir+"/"+os.path.basename(sample.maskFile)
# print "Using maskfile found in:", maskFile
# props = getSurveyProps(maskFile, sample.zBoundary[0],
# sample.zBoundary[1],
# sample.zBoundary[0],
# sample.zBoundary[1], "all",
# selectionFuncFile=sample.selFunFile,
# useComoving=sample.useComoving)
# boxVol = props[0]
# volNorm = maskIndex/boxVol
#else:
boxVol = np.prod(boxLen) boxVol = np.prod(boxLen)
volNorm = Np/boxVol volNorm = Np/boxVol
volNormObs = volNorm
return volNorm if isObservation == 1:
# look for the mask file
if os.access(sample.maskFile, os.F_OK):
maskFile = sample.maskFile
else:
maskFile = sampleDir+"/"+os.path.basename(sample.maskFile)
print("Using maskfile found in: " + maskFile)
props = getSurveyProps(maskFile, sample.zBoundary[0],
sample.zBoundary[1],
sample.zBoundary[0],
sample.zBoundary[1],
"all",
sample.omegaM,
selectionFuncFile=sample.selFunFile,
useComoving=sample.useComoving)
boxVol = props[0]
volNormObs = maskIndex/boxVol
return volNorm, volNormObs
# ----------------------------------------------------------------------------- # -----------------------------------------------------------------------------
@ -285,7 +290,8 @@ class Catalog:
numVoids = 0 numVoids = 0
numPartTot = 0 numPartTot = 0
numZonesTot = 0 numZonesTot = 0
volNorm = 0 volNorm = 0 # normalization used by zobov across entire volumne
volNormObs = 0 # normalization based on average density of survey volume
boxLen = np.zeros((3)) boxLen = np.zeros((3))
ranges = np.zeros((3,2)) ranges = np.zeros((3,2))
part = None part = None
@ -328,8 +334,9 @@ def loadVoidCatalog(sampleDir, dataPortion="central", loadParticles=True,
catalog.ranges = ranges catalog.ranges = ranges
File.close() File.close()
volNorm = getVolNorm(sampleDir) volNorm, volNormObs = getVolNorm(sampleDir)
catalog.volNorm = volNorm catalog.volNorm = volNorm
catalog.volNormObs = volNormObs
if untrimmed: if untrimmed:
prefix = "untrimmed_" prefix = "untrimmed_"

8
python_source/voidUtil/periodic_kdtree.py
View file

@ -279,12 +279,12 @@ class PeriodicCKDTree(cKDTree):
retshape = np.shape(x)[:-1] retshape = np.shape(x)[:-1]
if retshape!=(): if retshape!=():
if k>1: if k>1:
dd = np.empty(retshape+(k,),dtype=np.float) dd = np.empty(retshape+(k,),dtype=float)
dd.fill(np.inf) dd.fill(np.inf)
ii = np.empty(retshape+(k,),dtype=np.int) ii = np.empty(retshape+(k,),dtype=np.int)
ii.fill(self.n) ii.fill(self.n)
elif k==1: elif k==1:
dd = np.empty(retshape,dtype=np.float) dd = np.empty(retshape,dtype=float)
dd.fill(np.inf) dd.fill(np.inf)
ii = np.empty(retshape,dtype=np.int) ii = np.empty(retshape,dtype=np.int)
ii.fill(self.n) ii.fill(self.n)
@ -310,7 +310,7 @@ class PeriodicCKDTree(cKDTree):
else: else:
return np.inf, self.n return np.inf, self.n
elif k>1: elif k>1:
dd = np.empty(k,dtype=np.float) dd = np.empty(k,dtype=float)
dd.fill(np.inf) dd.fill(np.inf)
ii = np.empty(k,dtype=np.int) ii = np.empty(k,dtype=np.int)
ii.fill(self.n) ii.fill(self.n)
@ -368,7 +368,7 @@ class PeriodicCKDTree(cKDTree):
save substantial amounts of time by putting them in a save substantial amounts of time by putting them in a
PeriodicCKDTree and using query_ball_tree. PeriodicCKDTree and using query_ball_tree.
""" """
x = np.asarray(x).astype(np.float) x = np.asarray(x).astype(float)
if x.shape[-1] != self.m: if x.shape[-1] != self.m:
raise ValueError("Searching for a %d-dimensional point in a " \ raise ValueError("Searching for a %d-dimensional point in a " \
"%d-dimensional KDTree" % (x.shape[-1], self.m)) "%d-dimensional KDTree" % (x.shape[-1], self.m))

30
python_source/voidUtil/plotUtil.py
View file

@ -57,8 +57,8 @@ def plotNumberFunction(catalogList,
# cumulative: if True, plots cumulative number function # cumulative: if True, plots cumulative number function
# binWidth: width of histogram bins in Mpc/h # binWidth: width of histogram bins in Mpc/h
# returns: # returns:
# ellipDistList: array of len(catalogList), # distList: array of len(catalogList),
# each element has array of size bins, number, +/- 1 sigma # each element has array of size bins, number, +/- 1 sigma
print("Plotting number function") print("Plotting number function")
@ -72,7 +72,7 @@ def plotNumberFunction(catalogList,
else: else:
plt.ylabel(r"log ($dn/dR$ [$h^3$ Gpc$^{-3}$])", fontsize=14) plt.ylabel(r"log ($dn/dR$ [$h^3$ Gpc$^{-3}$])", fontsize=14)
ellipDistList = [] distList = []
for (iSample,catalog) in enumerate(catalogList): for (iSample,catalog) in enumerate(catalogList):
sample = catalog.sampleInfo sample = catalog.sampleInfo
@ -126,24 +126,25 @@ def plotNumberFunction(catalogList,
alpha = 0.55 alpha = 0.55
fill_between(binCentersToUse, lower, upper, fill_between(binCentersToUse, lower, upper,
label=lineTitle, color=lineColor, label='', color=lineColor,
alpha=alpha, alpha=alpha,
) )
lineStyle = '-' lineStyle = '-'
plt.plot(binCentersToUse, mean, lineStyle, plt.plot(binCentersToUse, mean, lineStyle,
color=lineColor, color=lineColor,
label=lineTitle,
linewidth=3) linewidth=3)
ellipDistList.append((binCentersToUse, mean, lower, upper)) distList.append((binCentersToUse, mean, lower, upper))
plt.legend(loc = "upper right", fancybox=True, prop={'size':14}) plt.legend(loc = "upper right", fancybox=True, prop={'size':14})
plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight") plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight") #plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight") plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
return ellipDistList return distList
# ----------------------------------------------------------------------------- # -----------------------------------------------------------------------------
@ -186,10 +187,10 @@ def plotEllipDist(catalogList,
plt.legend(loc = "upper right", fancybox=True, prop={'size':14}) plt.legend(loc = "upper right", fancybox=True, prop={'size':14})
plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight") plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight") #plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight") plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
return return ellipDistList
# ----------------------------------------------------------------------------- # -----------------------------------------------------------------------------
@ -297,7 +298,7 @@ def plotVoidCells(catalog,
plotName="cells"+str(voidID) plotName="cells"+str(voidID)
plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight") plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight") #plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight") plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
return return
@ -305,7 +306,7 @@ def plotVoidCells(catalog,
# ----------------------------------------------------------------------------- # -----------------------------------------------------------------------------
def plotVoronoi(catalog, def plotVoronoi(catalog,
voidIndex=-1, voidID=-1,
figDir="./", figDir="./",
plotName="voronoi", plotName="voronoi",
plotCenter = (0,0,0), plotCenter = (0,0,0),
@ -314,7 +315,7 @@ def plotVoronoi(catalog,
# plots the voronoi cells belonging to a void (or a slice if no void given) # plots the voronoi cells belonging to a void (or a slice if no void given)
# catalog: void catalog # catalog: void catalog
# voidIndec: index of void to plot (-1 to use all particles) # voidID: ID of void to plot (-1 to use all particles)
# figDir: output directory for figures # figDir: output directory for figures
# plotName: name to prefix to all outputs # plotName: name to prefix to all outputs
@ -327,7 +328,7 @@ def plotVoronoi(catalog,
print(" Selecting particles...") print(" Selecting particles...")
if (voidIndex == -1): if (voidID == -1):
xmin = plotCenter[0]-plotWidth[0]/2. xmin = plotCenter[0]-plotWidth[0]/2.
xmax = plotCenter[0]+plotWidth[0]/2. xmax = plotCenter[0]+plotWidth[0]/2.
ymin = plotCenter[1]-plotWidth[1]/2. ymin = plotCenter[1]-plotWidth[1]/2.
@ -348,7 +349,6 @@ def plotVoronoi(catalog,
#ax.set_ylim(ymin,ymax) #ax.set_ylim(ymin,ymax)
#ax_set.zlim(zmin,zmax) #ax_set.zlim(zmin,zmax)
else: else:
voidID = catalog.voids[voidIndex].voidID
partList = getVoidPart(catalog, voidID) partList = getVoidPart(catalog, voidID)
plotCenter = catalog.voids[voidIndex].macrocenter plotCenter = catalog.voids[voidIndex].macrocenter
plotRadius = catalog.voids[voidIndex].radius plotRadius = catalog.voids[voidIndex].radius
@ -381,7 +381,7 @@ def plotVoronoi(catalog,
print(" Saving...") print(" Saving...")
plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight") plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight") #plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight") plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
print(" Done!") print(" Done!")

2
python_source/voidUtil/profileUtil.py
View file

@ -75,7 +75,7 @@ def buildProfile(catalog, rMin, rMax, nBins=10):
deltaV = 4*np.pi/3*(radii[1:]**3-radii[0:(radii.size-1)]**3) deltaV = 4*np.pi/3*(radii[1:]**3-radii[0:(radii.size-1)]**3)
thisProfile = np.float32(thisProfile) thisProfile = np.float32(thisProfile)
thisProfile /= deltaV thisProfile /= deltaV
thisProfile /= catalog.volNorm thisProfile /= catalog.volNormObs
allProfiles.append(thisProfile) allProfiles.append(thisProfile)
binCenters = 0.5*(radii[1:] + radii[:-1]) binCenters = 0.5*(radii[1:] + radii[:-1])

34
python_source/voidUtil/xcorUtil.py
View file

@ -27,13 +27,15 @@ from voidUtil import getArray
def computeXcor(catalog, def computeXcor(catalog,
figDir="./", figDir="./",
namePrefix="",
Nmesh = 256, Nmesh = 256,
Nbin = 100 Nbin = 100,
): ):
# Computes and plots void-void and void-matter(galaxy) correlations # Computes and plots void-void and void-matter(galaxy) correlations
# catalog: catalog to analyze # catalog: catalog to analyze
# figDir: where to place plots # figDir: where to place plots
# namePrefix: prefix to add to file names
# Nmesh: number of grid cells in cic mesh-interpolation # Nmesh: number of grid cells in cic mesh-interpolation
# Nbin: number of bins in final plots # Nbin: number of bins in final plots
@ -84,18 +86,18 @@ def computeXcor(catalog,
plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$') plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$') plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
plt.title(r'Dark matter') plt.title(r'Dark matter')
plt.savefig(figDir+'/dm.eps', bbox_inches="tight") #plt.savefig(figDir+'/dm.eps', bbox_inches="tight")
plt.savefig(figDir+'/dm.pdf', bbox_inches="tight") plt.savefig(figDir+'/'+namePrefix+'dm.pdf', bbox_inches="tight")
plt.savefig(figDir+'/dm.png', bbox_inches="tight") plt.savefig(figDir+'/'+namePrefix+'dm.png', bbox_inches="tight")
plt.clf() plt.clf()
plt.imshow(np.sum(dv[:,:,:]+1,2)/Nmesh,extent=[0,Lbox,0,Lbox],aspect='equal',cmap='YlGnBu_r',interpolation='gaussian') 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.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$') plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
plt.title(r'Voids') plt.title(r'Voids')
plt.savefig(figDir+'/dv.eps', bbox_inches="tight") #, dpi=300 #plt.savefig(figDir+'/dv.eps', bbox_inches="tight") #, dpi=300
plt.savefig(figDir+'/dv.pdf', bbox_inches="tight") #, dpi=300 plt.savefig(figDir+'/'+namePrefix+'dv.pdf', bbox_inches="tight") #, dpi=300
plt.savefig(figDir+'/dv.png', bbox_inches="tight") #, dpi=300 plt.savefig(figDir+'/'+namePrefix+'dv.png', bbox_inches="tight") #, dpi=300
plt.clf() plt.clf()
@ -116,10 +118,11 @@ def computeXcor(catalog,
plt.yscale('log') plt.yscale('log')
plt.xlim(kmin,kmax) 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.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.legend([pa, pb, pc],['tt', 'vt', 'vv'])
plt.savefig(figDir+'/power.eps', bbox_inches="tight") #plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12})
plt.savefig(figDir+'/power.pdf', bbox_inches="tight") #plt.savefig(figDir+'/power.eps', bbox_inches="tight")
plt.savefig(figDir+'/power.png', bbox_inches="tight") plt.savefig(figDir+'/'+namePrefix+'power.pdf', bbox_inches="tight")
plt.savefig(figDir+'/'+namePrefix+'power.png', bbox_inches="tight")
plt.clf() plt.clf()
pa ,= plt.plot(rm, Xmm, 'k-o', ms=0.8*ms, mew=mew) pa ,= plt.plot(rm, Xmm, 'k-o', ms=0.8*ms, mew=mew)
@ -137,10 +140,11 @@ def computeXcor(catalog,
plt.yscale('log') plt.yscale('log')
plt.xlim(rmin,rmax) 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.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.legend([pa, pb, pc],['tt', 'vt', 'vv'])
plt.savefig(figDir+'/correlation.eps', bbox_inches="tight") #plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12})
plt.savefig(figDir+'/correlation.pdf', bbox_inches="tight") #plt.savefig(figDir+'/correlation.eps', bbox_inches="tight")
plt.savefig(figDir+'/correlation.png', bbox_inches="tight") plt.savefig(figDir+'/'+namePrefix+'correlation.pdf', bbox_inches="tight")
plt.savefig(figDir+'/'+namePrefix+'correlation.png', bbox_inches="tight")
plt.clf() plt.clf()

2
python_source/voidUtil/xcorlib.py
View file

@ -71,7 +71,7 @@ def powcor(d1, d2, Lbox, Nbin = 10, scale = 'lin', cor = False, cic = True, dim
dx = Lbox/Nmesh dx = Lbox/Nmesh
Xr = np.fft.irfftn(Pk)*(Nmesh/Lbox)**3 Xr = np.fft.irfftn(Pk)*(Nmesh/Lbox)**3
(Nmx, rm, Xrm, SXrm) = shellavg(np.real(Xr), dx, Nmesh, Nbin = Nbin/2, xmin = dx, xmax = 140., scale = scale, dim = dim) (Nmx, rm, Xrm, SXrm) = shellavg(np.real(Xr), dx, Nmesh, Nbin = Nbin//2, xmin = dx, xmax = 140., scale = scale, dim = dim)
return ((Nm, km, Pkm, SPkm),(Nmx, rm, Xrm, SXrm)) return ((Nm, km, Pkm, SPkm),(Nmx, rm, Xrm, SXrm))