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Continuing development. Boundary handling seems to be stable and working. Now testing void finding.
This commit is contained in:
Paul M. Sutter
parent
3dce2593d9
commit
cf97cfba5d
6 changed files with 92 additions and 41 deletions
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@ -76,24 +76,6 @@ def launchPrep(sample, binPath, workDir=None, inputDataDir=None,
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sample.maskFile = outputDir + "/constructed_mask.fits"
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figureOutMask(datafile, sample.nsideForContour, sample.maskFile)
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# compute mean particle separation
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(boxVol, nbar) = getSurveyProps(sample.maskFile, sample.zRange[0],
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sample.zRange[1], sample.zRange[0], sample.zRange[1], "all",
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sample.omegaM, useComoving=useComoving)
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numTracers = int(open(outputDir+"/mask_index.txt", "r").read())
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sample.meanPartSep = (1.*numTracers/boxVol/nbar)**(-1/3.)
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# flag edge galaxies
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galFile = outputDir + "galaxies.txt"
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edgeGalFile = outputDir + "/galaxy_edge_flags.txt"
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#edgeMaskFile = outputDir + "/mask_edge_map.fits"
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contourFile = outputDir + "/contour_map.fits"
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findEdgeGalaxies(galFile, sample.maskFile, edgeGalFile, contourFile,
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sample.zBoundary[0], sample.zBoundary[1], sample.omegaM,
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useComoving, sample.boundaryWidth, sample.meanPartSep)
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if useComoving:
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useComovingFlag = "useComoving"
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else:
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@ -111,12 +93,11 @@ def launchPrep(sample, binPath, workDir=None, inputDataDir=None,
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%s
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omegaM %g
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nsideForContour %g
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meanPartSep %g
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""" % (datafile, sample.maskFile, outputFile,
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outputDir+"/zobov_slice_"+sampleName+".par",
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sample.zBoundary[0], sample.zBoundary[1], sample.fakeDensity,
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useComovingFlag, inputParameterFlag, sample.omegaM,
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sample.nsideForContour, sample.meanPartSep)
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sample.nsideForContour)
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parmFile = os.getcwd()+"/prep_"+sample.fullName+".par"
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@ -126,6 +107,24 @@ def launchPrep(sample, binPath, workDir=None, inputDataDir=None,
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arg1 = "--configFile=%s" % parmFile
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with open(logFile, 'wt') as log:
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subprocess.call([binPath, arg1], stdout=log, stderr=log)
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# compute mean particle separation
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(boxVol, nbar) = getSurveyProps(sample.maskFile, sample.zRange[0],
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sample.zRange[1], sample.zRange[0], sample.zRange[1], "all",
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sample.omegaM, useComoving=useComoving)
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numTracers = int(open(outputDir+"/mask_index.txt", "r").read())
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sample.meanPartSep = (1.*numTracers/boxVol/nbar)**(-1/3.)
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# flag edge galaxies with python routine for now
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galFile = outputDir+"/zobov_slice_"+sampleName
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#galFile = outputDir + "galaxies.txt"
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edgeGalFile = outputDir + "/galaxy_edge_flags.txt"
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contourFile = outputDir + "/contour_map.fits"
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findEdgeGalaxies(galFile, sample.maskFile, edgeGalFile, contourFile,
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sample.zBoundary[0], sample.zBoundary[1], sample.omegaM,
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useComoving, sample.boundaryWidth, sample.meanPartSep, outputDir)
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if jobSuccessful(logFile, "Done!\n"):
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endTime = time.time()
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walltime = endTime - startTime
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@ -137,6 +136,16 @@ def launchPrep(sample, binPath, workDir=None, inputDataDir=None,
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else:
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print("already done!")
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# compute mean particle separation
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# TODO - clean up this duplicate to cover all continueRun options
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(boxVol, nbar) = getSurveyProps(sample.maskFile, sample.zRange[0],
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sample.zRange[1], sample.zRange[0], sample.zRange[1], "all",
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sample.omegaM, useComoving=useComoving)
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numTracers = int(open(outputDir+"/mask_index.txt", "r").read())
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sample.meanPartSep = (1.*numTracers/boxVol/nbar)**(-1/3.)
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if os.access(parmFile, os.F_OK): os.unlink(parmFile)
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if os.access("contour_map.fits", os.F_OK):
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@ -131,7 +131,7 @@ def figureOutMask(galFile, nside, outMaskFile):
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# figures out which galaxies live on a mask or redshift edge
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def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
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zmin, zmax, omegaM, useComoving, boundaryWidth,
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meanPartSep):
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meanPartSep, outputDir):
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if useComoving:
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zmin = comovingDistance(zmin, Om=omegaM)*LIGHT_SPEED
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@ -145,15 +145,50 @@ def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
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npix = healpy.nside2npix(nside)
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# load in galaxies
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galPos = np.genfromtxt(galFile)
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#galPos = np.genfromtxt(galFile)
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galPos = np.genfromtxt(outputDir+"/galaxies.txt")
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with open(galFile, 'rb') as File:
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chk = np.fromfile(File, dtype=np.int32,count=1)
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Np = np.fromfile(File, dtype=np.int32,count=1)[0]
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chk = np.fromfile(File, dtype=np.int32,count=1)
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chk = np.fromfile(File, dtype=np.int32,count=1)
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x = np.fromfile(File, dtype=np.float32,count=Np)
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chk = np.fromfile(File, dtype=np.int32,count=1)
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chk = np.fromfile(File, dtype=np.int32,count=1)
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y = np.fromfile(File, dtype=np.float32,count=Np)
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chk = np.fromfile(File, dtype=np.int32,count=1)
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chk = np.fromfile(File, dtype=np.int32,count=1)
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z = np.fromfile(File, dtype=np.float32,count=Np)
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chk = np.fromfile(File, dtype=np.int32,count=1)
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chk = np.fromfile(File, dtype=np.int32,count=1)
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RA = np.fromfile(File, dtype=np.float32,count=Np)
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chk = np.fromfile(File, dtype=np.int32,count=1)
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chk = np.fromfile(File, dtype=np.int32,count=1)
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Dec = np.fromfile(File, dtype=np.float32,count=Np)
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chk = np.fromfile(File, dtype=np.int32,count=1)
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chk = np.fromfile(File, dtype=np.int32,count=1)
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redshift = np.fromfile(File, dtype=np.float32,count=Np)
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chk = np.fromfile(File, dtype=np.int32,count=1)
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#print(galPos.shape)
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#galPos = np.column_stack((x,y,z))
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#print(galPos.shape)
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flagList = np.zeros(len(galPos[:,0]))
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galTree = scipy.spatial.cKDTree(galPos)
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# flag galaxies near mask edges
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# using the "ray marching" algorithm: follow rays along lines of sight
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# of all mask edges, flagging nearest neighbor galaxies as we go
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raySteps = np.arange(zmin, zmax, meanPartSep)
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#print(meanPartSep, len(raySteps))
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contourPixels = np.nonzero(contourMap)[0]
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#print(contourPixels)
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@ -164,34 +199,42 @@ def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
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y = raySteps * vec[1]
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z = raySteps * vec[2]
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ray = np.array((x,y,z)).T
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#print(ray)
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dist, nearest = galTree.query(ray)
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flagList[nearest] = 1
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#print(nearest)
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# flag galaxies near redsfhit boundaries
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# TODO - save time by only covering portion of sphere with data
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ds = np.sqrt(healpy.nside2pixarea(nside)) / 1000.
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phi = np.arange(0, 2*np.pi, ds*2)
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theta = np.arange(0, np.pi, ds)
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vec = healpy.ang2vec(theta, phi)
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# TODO - save time by only covering portion of sphere that has data
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sphereIndices = np.arange(len(contourMap))
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vec = healpy.pix2vec(nside, sphereIndices)
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vec = np.asarray(vec)
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maxEdge = zmax * vec
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maxEdge = maxEdge.T
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dist, nearest = galTree.query(maxEdge)
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#print(nearest)
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#print(galPos[nearest])
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flagList[nearest] = 2
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minEdge = zmin * vec
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minEdge = minEdge.T
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dist, nearest = galTree.query(minEdge)
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#print(nearest)
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#print(galPos[nearest])
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flagList[nearest] = 3
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# output flag information
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np.savetxt(edgeGalFile, flagList, fmt="%d")
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# paint galaxy flags onto healpix map for diagnostics
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# TODO - drop this when done testing
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flagMap = np.zeros(len(contourMap))
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justEdgeRA = RA[flagList == 1]
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justEdgeDec = Dec[flagList == 1]
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phi, theta = convertAngle(justEdgeRA, justEdgeDec)
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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("./flagged_galaxies.fits", flagMap, overwrite=True,
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dtype=np.dtype('float64'))
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# # output galaxy edge flags
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@ -112,7 +112,7 @@ for sample in dataSampleList:
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# -------------------------------------------------------------------------
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if (startCatalogStage <= 3) and (endCatalogStage >= 3) and not sample.isCombo:
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print(" Pruning void catalogs", "...", end='')
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print(" Pruning void catalogs...", end='')
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sys.stdout.flush()
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logFile = logDir+"/pruneVoids_"+sampleName+".out"
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