mirror of
https://bitbucket.org/cosmicvoids/vide_public.git
synced 2025-07-04 15:21:11 +00:00
+ radial profiles now full actual density from survey volume, not zobov normalization
+ 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
parent
b79046ac22
commit
326756b2bc
8 changed files with 73 additions and 61 deletions
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@ -667,7 +667,6 @@ int main(int argc, char **argv) {
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interval = 1.*(clock4 - clock3)/CLOCKS_PER_SEC;
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//printf(" %.2f for maximum extent\n", interval);
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/*
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// compute distance from center to nearest mock boundary particle
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// (with new boundary handling this will go away)
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clock3 = clock();
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@ -686,7 +685,6 @@ int main(int argc, char **argv) {
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} else {
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voids[iVoid].nearestMock = 1.e99;
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}
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if (args.isObservation_flag) {
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voids[iVoid].redshiftInMpc =
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sqrt(pow(voids[iVoid].macrocenter[0] - boxLen[0]/2.,2) +
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@ -745,7 +743,6 @@ int main(int argc, char **argv) {
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clock4 = clock();
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interval = 1.*(clock4 - clock3)/CLOCKS_PER_SEC;
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//printf(" %.2f for nearest edge\n", interval);
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*/
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// compute eigenvalues and vectors for orientation and shape
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clock3 = clock();
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@ -194,11 +194,9 @@ def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
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rayDist = meanPartSep
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raySteps = np.arange(zmin, zmax, rayDist)
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log.write(" Will take %d steps per ray with %.2e distance between steps\n" % (len(raySteps), rayDist))
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#print(meanPartSep, len(raySteps))
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contourPixels = np.nonzero(contourMap)[0]
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log.write(" We have %d rays to work with\n" % (len(contourPixels)))
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#print(contourPixels)
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for pixel in contourPixels:
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#print("Working with pixel %d" % pixel)
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vec = healpy.pix2vec(nside,pixel)
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@ -227,6 +225,11 @@ def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
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dist, nearest = galTree.query(minEdge)
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flagList[nearest] = 3
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log.write(" Flagging statistics:\n")
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log.write(" %d original galaxies\n" % len(flagList))
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log.write(" %d near edges\n" % len(flagList[flagList==1]))
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log.write(" %d near redshift bounds\n" % len(flagList[flagList==2]))
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log.write(" %d remaining\n" % len(flagList[flagList==0]))
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# output flag information
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log.write(" Saving galaxy flags to file...\n")
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np.savetxt(edgeGalFile, flagList, fmt="%d")
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@ -243,7 +246,8 @@ def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
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ipix = healpy.ang2pix(nside, theta, phi)
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np.put(flagMap, ipix, 1)
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healpy.write_map(outputDir+"/flagged_galaxies.fits", flagMap, overwrite=True,
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healpy.write_map(outputDir+"/flagged_galaxies.fits", flagMap,
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overwrite=True,
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dtype=np.dtype('float64'))
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@ -115,6 +115,8 @@ def loadPart(sampleDir):
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boxLen = mul
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# TODO - should use built-in volNorm function instead
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#if isObservation == 1:
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# # look for the mask file
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# if os.access(sample.maskFile, os.F_OK):
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@ -165,26 +167,29 @@ def getVolNorm(sampleDir):
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boxLen = mul
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#if isObservation == 1:
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# # look for the mask file
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# if os.access(sample.maskFile, os.F_OK):
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# maskFile = sample.maskFile
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# else:
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# maskFile = sampleDir+"/"+os.path.basename(sample.maskFile)
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# print "Using maskfile found in:", maskFile
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# props = getSurveyProps(maskFile, sample.zBoundary[0],
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# sample.zBoundary[1],
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# sample.zBoundary[0],
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# sample.zBoundary[1], "all",
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# selectionFuncFile=sample.selFunFile,
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# useComoving=sample.useComoving)
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# boxVol = props[0]
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# volNorm = maskIndex/boxVol
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#else:
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boxVol = np.prod(boxLen)
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volNorm = Np/boxVol
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volNormObs = volNorm
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return volNorm
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if isObservation == 1:
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# look for the mask file
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if os.access(sample.maskFile, os.F_OK):
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maskFile = sample.maskFile
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else:
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maskFile = sampleDir+"/"+os.path.basename(sample.maskFile)
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print("Using maskfile found in: " + maskFile)
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props = getSurveyProps(maskFile, sample.zBoundary[0],
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sample.zBoundary[1],
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sample.zBoundary[0],
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sample.zBoundary[1],
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"all",
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sample.omegaM,
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selectionFuncFile=sample.selFunFile,
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useComoving=sample.useComoving)
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boxVol = props[0]
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volNormObs = maskIndex/boxVol
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return volNorm, volNormObs
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# -----------------------------------------------------------------------------
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@ -285,7 +290,8 @@ class Catalog:
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numVoids = 0
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numPartTot = 0
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numZonesTot = 0
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volNorm = 0
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volNorm = 0 # normalization used by zobov across entire volumne
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volNormObs = 0 # normalization based on average density of survey volume
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boxLen = np.zeros((3))
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ranges = np.zeros((3,2))
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part = None
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@ -328,8 +334,9 @@ def loadVoidCatalog(sampleDir, dataPortion="central", loadParticles=True,
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catalog.ranges = ranges
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File.close()
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volNorm = getVolNorm(sampleDir)
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volNorm, volNormObs = getVolNorm(sampleDir)
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catalog.volNorm = volNorm
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catalog.volNormObs = volNormObs
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if untrimmed:
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prefix = "untrimmed_"
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@ -279,12 +279,12 @@ class PeriodicCKDTree(cKDTree):
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retshape = np.shape(x)[:-1]
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if retshape!=():
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if k>1:
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dd = np.empty(retshape+(k,),dtype=np.float)
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dd = np.empty(retshape+(k,),dtype=float)
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dd.fill(np.inf)
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ii = np.empty(retshape+(k,),dtype=np.int)
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ii.fill(self.n)
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elif k==1:
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dd = np.empty(retshape,dtype=np.float)
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dd = np.empty(retshape,dtype=float)
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dd.fill(np.inf)
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ii = np.empty(retshape,dtype=np.int)
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ii.fill(self.n)
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@ -310,7 +310,7 @@ class PeriodicCKDTree(cKDTree):
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else:
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return np.inf, self.n
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elif k>1:
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dd = np.empty(k,dtype=np.float)
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dd = np.empty(k,dtype=float)
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dd.fill(np.inf)
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ii = np.empty(k,dtype=np.int)
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ii.fill(self.n)
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@ -368,7 +368,7 @@ class PeriodicCKDTree(cKDTree):
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save substantial amounts of time by putting them in a
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PeriodicCKDTree and using query_ball_tree.
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"""
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x = np.asarray(x).astype(np.float)
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x = np.asarray(x).astype(float)
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if x.shape[-1] != self.m:
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raise ValueError("Searching for a %d-dimensional point in a " \
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"%d-dimensional KDTree" % (x.shape[-1], self.m))
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@ -57,8 +57,8 @@ def plotNumberFunction(catalogList,
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# cumulative: if True, plots cumulative number function
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# binWidth: width of histogram bins in Mpc/h
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# returns:
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# ellipDistList: array of len(catalogList),
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# each element has array of size bins, number, +/- 1 sigma
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# distList: array of len(catalogList),
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# each element has array of size bins, number, +/- 1 sigma
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print("Plotting number function")
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@ -72,7 +72,7 @@ def plotNumberFunction(catalogList,
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else:
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plt.ylabel(r"log ($dn/dR$ [$h^3$ Gpc$^{-3}$])", fontsize=14)
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ellipDistList = []
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distList = []
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for (iSample,catalog) in enumerate(catalogList):
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sample = catalog.sampleInfo
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@ -126,24 +126,25 @@ def plotNumberFunction(catalogList,
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alpha = 0.55
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fill_between(binCentersToUse, lower, upper,
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label=lineTitle, color=lineColor,
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label='', color=lineColor,
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alpha=alpha,
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)
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lineStyle = '-'
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plt.plot(binCentersToUse, mean, lineStyle,
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color=lineColor,
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label=lineTitle,
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linewidth=3)
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ellipDistList.append((binCentersToUse, mean, lower, upper))
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distList.append((binCentersToUse, mean, lower, upper))
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plt.legend(loc = "upper right", fancybox=True, prop={'size':14})
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plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
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plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
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#plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
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plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
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return ellipDistList
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return distList
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# -----------------------------------------------------------------------------
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@ -186,10 +187,10 @@ def plotEllipDist(catalogList,
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plt.legend(loc = "upper right", fancybox=True, prop={'size':14})
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plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
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plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
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#plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
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plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
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return
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return ellipDistList
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# -----------------------------------------------------------------------------
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@ -297,7 +298,7 @@ def plotVoidCells(catalog,
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plotName="cells"+str(voidID)
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plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
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plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
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#plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
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plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
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return
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@ -305,7 +306,7 @@ def plotVoidCells(catalog,
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# -----------------------------------------------------------------------------
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def plotVoronoi(catalog,
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voidIndex=-1,
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voidID=-1,
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figDir="./",
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plotName="voronoi",
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plotCenter = (0,0,0),
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@ -314,7 +315,7 @@ def plotVoronoi(catalog,
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# plots the voronoi cells belonging to a void (or a slice if no void given)
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# catalog: void catalog
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# voidIndec: index of void to plot (-1 to use all particles)
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# voidID: ID of void to plot (-1 to use all particles)
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# figDir: output directory for figures
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# plotName: name to prefix to all outputs
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@ -327,7 +328,7 @@ def plotVoronoi(catalog,
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print(" Selecting particles...")
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if (voidIndex == -1):
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if (voidID == -1):
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xmin = plotCenter[0]-plotWidth[0]/2.
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xmax = plotCenter[0]+plotWidth[0]/2.
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ymin = plotCenter[1]-plotWidth[1]/2.
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@ -348,7 +349,6 @@ def plotVoronoi(catalog,
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#ax.set_ylim(ymin,ymax)
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#ax_set.zlim(zmin,zmax)
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else:
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voidID = catalog.voids[voidIndex].voidID
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partList = getVoidPart(catalog, voidID)
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plotCenter = catalog.voids[voidIndex].macrocenter
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plotRadius = catalog.voids[voidIndex].radius
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@ -381,7 +381,7 @@ def plotVoronoi(catalog,
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print(" Saving...")
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plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
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plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
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#plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
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plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
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print(" Done!")
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@ -75,7 +75,7 @@ def buildProfile(catalog, rMin, rMax, nBins=10):
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deltaV = 4*np.pi/3*(radii[1:]**3-radii[0:(radii.size-1)]**3)
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thisProfile = np.float32(thisProfile)
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thisProfile /= deltaV
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thisProfile /= catalog.volNorm
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thisProfile /= catalog.volNormObs
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allProfiles.append(thisProfile)
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binCenters = 0.5*(radii[1:] + radii[:-1])
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@ -27,13 +27,15 @@ from voidUtil import getArray
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def computeXcor(catalog,
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figDir="./",
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namePrefix="",
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Nmesh = 256,
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Nbin = 100
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Nbin = 100,
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):
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# Computes and plots void-void and void-matter(galaxy) correlations
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# catalog: catalog to analyze
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# figDir: where to place plots
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# namePrefix: prefix to add to file names
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# Nmesh: number of grid cells in cic mesh-interpolation
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# Nbin: number of bins in final plots
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@ -84,18 +86,18 @@ def computeXcor(catalog,
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plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
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plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
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plt.title(r'Dark matter')
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plt.savefig(figDir+'/dm.eps', bbox_inches="tight")
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plt.savefig(figDir+'/dm.pdf', bbox_inches="tight")
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plt.savefig(figDir+'/dm.png', bbox_inches="tight")
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#plt.savefig(figDir+'/dm.eps', bbox_inches="tight")
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plt.savefig(figDir+'/'+namePrefix+'dm.pdf', bbox_inches="tight")
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plt.savefig(figDir+'/'+namePrefix+'dm.png', bbox_inches="tight")
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plt.clf()
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plt.imshow(np.sum(dv[:,:,:]+1,2)/Nmesh,extent=[0,Lbox,0,Lbox],aspect='equal',cmap='YlGnBu_r',interpolation='gaussian')
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plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
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plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
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plt.title(r'Voids')
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plt.savefig(figDir+'/dv.eps', bbox_inches="tight") #, dpi=300
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plt.savefig(figDir+'/dv.pdf', bbox_inches="tight") #, dpi=300
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plt.savefig(figDir+'/dv.png', bbox_inches="tight") #, dpi=300
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#plt.savefig(figDir+'/dv.eps', bbox_inches="tight") #, dpi=300
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plt.savefig(figDir+'/'+namePrefix+'dv.pdf', bbox_inches="tight") #, dpi=300
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plt.savefig(figDir+'/'+namePrefix+'dv.png', bbox_inches="tight") #, dpi=300
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plt.clf()
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@ -116,10 +118,11 @@ def computeXcor(catalog,
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plt.yscale('log')
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plt.xlim(kmin,kmax)
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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)
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plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12})
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plt.savefig(figDir+'/power.eps', bbox_inches="tight")
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plt.savefig(figDir+'/power.pdf', bbox_inches="tight")
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plt.savefig(figDir+'/power.png', bbox_inches="tight")
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plt.legend([pa, pb, pc],['tt', 'vt', 'vv'])
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#plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12})
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#plt.savefig(figDir+'/power.eps', bbox_inches="tight")
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plt.savefig(figDir+'/'+namePrefix+'power.pdf', bbox_inches="tight")
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plt.savefig(figDir+'/'+namePrefix+'power.png', bbox_inches="tight")
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plt.clf()
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pa ,= plt.plot(rm, Xmm, 'k-o', ms=0.8*ms, mew=mew)
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@ -137,10 +140,11 @@ def computeXcor(catalog,
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plt.yscale('log')
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plt.xlim(rmin,rmax)
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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)
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plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12})
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plt.savefig(figDir+'/correlation.eps', bbox_inches="tight")
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plt.savefig(figDir+'/correlation.pdf', bbox_inches="tight")
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plt.savefig(figDir+'/correlation.png', bbox_inches="tight")
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plt.legend([pa, pb, pc],['tt', 'vt', 'vv'])
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#plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12})
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#plt.savefig(figDir+'/correlation.eps', bbox_inches="tight")
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plt.savefig(figDir+'/'+namePrefix+'correlation.pdf', bbox_inches="tight")
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plt.savefig(figDir+'/'+namePrefix+'correlation.png', bbox_inches="tight")
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plt.clf()
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@ -71,7 +71,7 @@ def powcor(d1, d2, Lbox, Nbin = 10, scale = 'lin', cor = False, cic = True, dim
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dx = Lbox/Nmesh
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Xr = np.fft.irfftn(Pk)*(Nmesh/Lbox)**3
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(Nmx, rm, Xrm, SXrm) = shellavg(np.real(Xr), dx, Nmesh, Nbin = Nbin/2, xmin = dx, xmax = 140., scale = scale, dim = dim)
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(Nmx, rm, Xrm, SXrm) = shellavg(np.real(Xr), dx, Nmesh, Nbin = Nbin//2, xmin = dx, xmax = 140., scale = scale, dim = dim)
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return ((Nm, km, Pkm, SPkm),(Nmx, rm, Xrm, SXrm))
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|
|
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Loading…
Add table
Add a link
Reference in a new issue