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Continuing development. Boundary handling seems to be stable and working. Now testing void finding.

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This commit is contained in:
Paul M. Sutter 2025-01-08 15:13:29 +08:00
parent 3dce2593d9
commit cf97cfba5d
6 changed files with 92 additions and 41 deletions
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5
c_source/prep/prepObservation.cpp
View file

@ -229,6 +229,7 @@ void flagEdgeGalaxies(prepObservation_info& args ,
output_data.mask_index = output_data.id_gal.size();
// write a small text file with galaxy position (for diagnostic purposes)
// TODO - remove this
FILE *fp;
fp = fopen("galaxies.txt", "w");
for (int i = 0; i < data.size(); i++) {
@ -240,7 +241,7 @@ void flagEdgeGalaxies(prepObservation_info& args ,
}
fclose(fp);
/* NOTE: temporarily moved to python for quick debugging. Will move back to
/* NOTE: temporarily moved to python for quick prototyping. Will move back to
here once it's all sorted
// convert redshift boundaries to covmoving if necessary
@ -463,8 +464,8 @@ int main(int argc, char **argv)
fp = fopen("total_particles.txt", "w");
fprintf(fp, "%d", output_data.pos.size());
fclose(fp);
printf("Done!\n");
// END PMS - TODO REMOVE THIS
printf("Done!\n");
return 0;
}

2
c_source/prep/prepObservation.ggo
View file

@ -19,5 +19,3 @@ option "useComoving" - "Convert to real space using LCDM cosmology" flag off
option "omegaM" - "Omega Matter for fiducial cosmology" double optional default="0.27"
option "nsideForContour" - "HEALPix NSIDE resolution for figuring out mask contours" int optional default="-1"
option "meanPartSep" - "Estimated mean tracer seperation in h^3 / Mpc^3" double optional default="1"

4
examples/example_observation/example_observation.py
View file

@ -30,7 +30,7 @@ continueRun = False
# 1 : extract redshift slices from data
# 2 : void extraction using zobov
# 3 : removal of small voids and voids near the edge
startCatalogStage = 2
startCatalogStage = 1
endCatalogStage = 3
basePath = os.path.dirname(os.path.abspath(__file__))
@ -51,7 +51,7 @@ figDir = os.path.join(workDir,"figs","example_observation")
numZobovThreads = 2
# optimization: number of subdivisions of the volume
numZobovDivisions = 2
numZobovDivisions = 1
# Maximum density for merging voids
# 0 (equivalent to infinitely large value) -> Merge everything (no threshold)

49
python_source/backend/launchers.py
View file

@ -76,24 +76,6 @@ def launchPrep(sample, binPath, workDir=None, inputDataDir=None,
sample.maskFile = outputDir + "/constructed_mask.fits"
figureOutMask(datafile, sample.nsideForContour, sample.maskFile)
# compute mean particle separation
(boxVol, nbar) = getSurveyProps(sample.maskFile, sample.zRange[0],
sample.zRange[1], sample.zRange[0], sample.zRange[1], "all",
sample.omegaM, useComoving=useComoving)
numTracers = int(open(outputDir+"/mask_index.txt", "r").read())
sample.meanPartSep = (1.*numTracers/boxVol/nbar)**(-1/3.)
# flag edge galaxies
galFile = outputDir + "galaxies.txt"
edgeGalFile = outputDir + "/galaxy_edge_flags.txt"
#edgeMaskFile = outputDir + "/mask_edge_map.fits"
contourFile = outputDir + "/contour_map.fits"
findEdgeGalaxies(galFile, sample.maskFile, edgeGalFile, contourFile,
sample.zBoundary[0], sample.zBoundary[1], sample.omegaM,
useComoving, sample.boundaryWidth, sample.meanPartSep)
if useComoving:
useComovingFlag = "useComoving"
else:
@ -111,12 +93,11 @@ def launchPrep(sample, binPath, workDir=None, inputDataDir=None,
%s
omegaM %g
nsideForContour %g
meanPartSep %g
""" % (datafile, sample.maskFile, outputFile,
outputDir+"/zobov_slice_"+sampleName+".par",
sample.zBoundary[0], sample.zBoundary[1], sample.fakeDensity,
useComovingFlag, inputParameterFlag, sample.omegaM,
sample.nsideForContour, sample.meanPartSep)
sample.nsideForContour)
parmFile = os.getcwd()+"/prep_"+sample.fullName+".par"
@ -126,6 +107,24 @@ def launchPrep(sample, binPath, workDir=None, inputDataDir=None,
arg1 = "--configFile=%s" % parmFile
with open(logFile, 'wt') as log:
subprocess.call([binPath, arg1], stdout=log, stderr=log)
# compute mean particle separation
(boxVol, nbar) = getSurveyProps(sample.maskFile, sample.zRange[0],
sample.zRange[1], sample.zRange[0], sample.zRange[1], "all",
sample.omegaM, useComoving=useComoving)
numTracers = int(open(outputDir+"/mask_index.txt", "r").read())
sample.meanPartSep = (1.*numTracers/boxVol/nbar)**(-1/3.)
# flag edge galaxies with python routine for now
galFile = outputDir+"/zobov_slice_"+sampleName
#galFile = outputDir + "galaxies.txt"
edgeGalFile = outputDir + "/galaxy_edge_flags.txt"
contourFile = outputDir + "/contour_map.fits"
findEdgeGalaxies(galFile, sample.maskFile, edgeGalFile, contourFile,
sample.zBoundary[0], sample.zBoundary[1], sample.omegaM,
useComoving, sample.boundaryWidth, sample.meanPartSep, outputDir)
if jobSuccessful(logFile, "Done!\n"):
endTime = time.time()
walltime = endTime - startTime
@ -137,6 +136,16 @@ def launchPrep(sample, binPath, workDir=None, inputDataDir=None,
else:
print("already done!")
# compute mean particle separation
# TODO - clean up this duplicate to cover all continueRun options
(boxVol, nbar) = getSurveyProps(sample.maskFile, sample.zRange[0],
sample.zRange[1], sample.zRange[0], sample.zRange[1], "all",
sample.omegaM, useComoving=useComoving)
numTracers = int(open(outputDir+"/mask_index.txt", "r").read())
sample.meanPartSep = (1.*numTracers/boxVol/nbar)**(-1/3.)
if os.access(parmFile, os.F_OK): os.unlink(parmFile)
if os.access("contour_map.fits", os.F_OK):

69
python_source/backend/surveyTools.py
View file

@ -131,7 +131,7 @@ def figureOutMask(galFile, nside, outMaskFile):
# figures out which galaxies live on a mask or redshift edge
def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
zmin, zmax, omegaM, useComoving, boundaryWidth,
meanPartSep):
meanPartSep, outputDir):
if useComoving:
zmin = comovingDistance(zmin, Om=omegaM)*LIGHT_SPEED
@ -145,7 +145,41 @@ def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
npix = healpy.nside2npix(nside)
# load in galaxies
galPos = np.genfromtxt(galFile)
#galPos = np.genfromtxt(galFile)
galPos = np.genfromtxt(outputDir+"/galaxies.txt")
with open(galFile, 'rb') as File:
chk = np.fromfile(File, dtype=np.int32,count=1)
Np = np.fromfile(File, dtype=np.int32,count=1)[0]
chk = np.fromfile(File, dtype=np.int32,count=1)
chk = np.fromfile(File, dtype=np.int32,count=1)
x = np.fromfile(File, dtype=np.float32,count=Np)
chk = np.fromfile(File, dtype=np.int32,count=1)
chk = np.fromfile(File, dtype=np.int32,count=1)
y = np.fromfile(File, dtype=np.float32,count=Np)
chk = np.fromfile(File, dtype=np.int32,count=1)
chk = np.fromfile(File, dtype=np.int32,count=1)
z = np.fromfile(File, dtype=np.float32,count=Np)
chk = np.fromfile(File, dtype=np.int32,count=1)
chk = np.fromfile(File, dtype=np.int32,count=1)
RA = np.fromfile(File, dtype=np.float32,count=Np)
chk = np.fromfile(File, dtype=np.int32,count=1)
chk = np.fromfile(File, dtype=np.int32,count=1)
Dec = np.fromfile(File, dtype=np.float32,count=Np)
chk = np.fromfile(File, dtype=np.int32,count=1)
chk = np.fromfile(File, dtype=np.int32,count=1)
redshift = np.fromfile(File, dtype=np.float32,count=Np)
chk = np.fromfile(File, dtype=np.int32,count=1)
#print(galPos.shape)
#galPos = np.column_stack((x,y,z))
#print(galPos.shape)
flagList = np.zeros(len(galPos[:,0]))
galTree = scipy.spatial.cKDTree(galPos)
@ -154,6 +188,7 @@ def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
# of all mask edges, flagging nearest neighbor galaxies as we go
raySteps = np.arange(zmin, zmax, meanPartSep)
#print(meanPartSep, len(raySteps))
contourPixels = np.nonzero(contourMap)[0]
#print(contourPixels)
@ -164,34 +199,42 @@ def findEdgeGalaxies(galFile, maskFile, edgeGalFile, contourFile,
y = raySteps * vec[1]
z = raySteps * vec[2]
ray = np.array((x,y,z)).T
#print(ray)
dist, nearest = galTree.query(ray)
flagList[nearest] = 1
#print(nearest)
# flag galaxies near redsfhit boundaries
# TODO - save time by only covering portion of sphere with data
ds = np.sqrt(healpy.nside2pixarea(nside)) / 1000.
phi = np.arange(0, 2*np.pi, ds*2)
theta = np.arange(0, np.pi, ds)
vec = healpy.ang2vec(theta, phi)
# TODO - save time by only covering portion of sphere that has data
sphereIndices = np.arange(len(contourMap))
vec = healpy.pix2vec(nside, sphereIndices)
vec = np.asarray(vec)
maxEdge = zmax * vec
maxEdge = maxEdge.T
dist, nearest = galTree.query(maxEdge)
#print(nearest)
#print(galPos[nearest])
flagList[nearest] = 2
minEdge = zmin * vec
minEdge = minEdge.T
dist, nearest = galTree.query(minEdge)
#print(nearest)
#print(galPos[nearest])
flagList[nearest] = 3
# output flag information
np.savetxt(edgeGalFile, flagList, fmt="%d")
# paint galaxy flags onto healpix map for diagnostics
# TODO - drop this when done testing
flagMap = np.zeros(len(contourMap))
justEdgeRA = RA[flagList == 1]
justEdgeDec = Dec[flagList == 1]
phi, theta = convertAngle(justEdgeRA, justEdgeDec)
ipix = healpy.ang2pix(nside, theta, phi)
np.put(flagMap, ipix, 1)
healpy.write_map("./flagged_galaxies.fits", flagMap, overwrite=True,
dtype=np.dtype('float64'))
# # output galaxy edge flags

2
python_source/vide_pipeline/__main__.py
View file

@ -112,7 +112,7 @@ for sample in dataSampleList:
# -------------------------------------------------------------------------
if (startCatalogStage <= 3) and (endCatalogStage >= 3) and not sample.isCombo:
print(" Pruning void catalogs", "...", end='')
print(" Pruning void catalogs...", end='')
sys.stdout.flush()
logFile = logDir+"/pruneVoids_"+sampleName+".out"