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https://bitbucket.org/cosmicvoids/vide_public.git
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bug fixes
This commit is contained in:
P.M. Sutter
parent
4cf0ec1173
commit
39e16d41ff
6 changed files with 103 additions and 102 deletions
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@ -31,7 +31,7 @@ setup(
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cmdclass = {'build_ext': build_ext},
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include_dirs = [np.get_include()],
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packages=
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['void_python_tools','void_python_tools.backend','void_python_tools.apTools', 'void_python_tools.xcor', 'void_python_tools.voidUtil',
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['void_python_tools','void_python_tools.backend','void_python_tools.apTools', 'void_python_tools.voidUtil',
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'void_python_tools.apTools.profiles','void_python_tools.apTools.chi2',],
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#ext_modules = [Extension("void_python_tools.chi2.velocityProfileFitNative", ["void_python_tools/chi2/velocityProfileFitNative.pyx"], libraries=["gsl", "gslcblas"]), Extension("void_python_tools.chi2.likelihoo", ["void_python_tools/chi2/likelihood.pyx"], libraries=["gsl", "gslcblas"])]
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#ext_modules = [
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@ -269,7 +269,7 @@ def loadVoidCatalog(sampleDir, dataPortion="central", loadPart=True):
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densCon = line[9],
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voidProb = line[10],
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radius = pow(line[7]/volNorm*3./4./np.pi, 1./3.),
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barycenter = np.zeros((3))
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barycenter = np.zeros((3)),
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parentID = 0,
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treeLevel = 0,
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numChildren = 0,
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@ -32,7 +32,7 @@ def compareCatalogs(baseCatalogDir, compareCatalogDir,
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outputDir="./", logDir="./",
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matchMethod="useID", dataPortion="central",
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strictMatch=True,
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pathToCTools="../../../c_tools")
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pathToCTools="../../../c_tools"):
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# reports the overlap between two void catalogs
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# baseCatalogDir: directory of catalog 1
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@ -44,36 +44,37 @@ def compareCatalogs(baseCatalogDir, compareCatalogDir,
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# strictMatch: if True, only attempt to match to trimmed catalog
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# pathToCTools: path to location of VIDE c_tools directory
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if not os.access(outputDir, os.F_OK):
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os.makedirs(outputDir)
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if not os.access(logDir, os.F_OK):
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os.makedirs(logDir)
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outFileName = outputDir + "/" + "voidOverlap" #+ ".dat"
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with open(baseCatalogDir+"/sample_info.dat", 'rb') as input:
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baseSample = pickle.load(input)
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with open(compareCatalogDir+"/sample_info.dat", 'rb') as input:
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sample = pickle.load(input)
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print " Comparing", baseSample.fullName, "to", sample.fullName, "...",
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sys.stdout.flush()
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sampleName = sample.fullName
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binPath = pathToCTools+"/analysis/voidOverlap"
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logFile = logDir+"/compare_"+baseSample.fullName+"_"+sampleName+".out"
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stepOutputFileName = outFileName + "_" + baseSample.fullName + "_" + \
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sampleName + "_"
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launchVoidOverlap(baseSample, sample, baseCatalogDir,
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compareCatalogDir, binPath,
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thisDataPortion=dataPortion, logFile=logFile,
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continueRun=False, workDir=workDir,
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outputFile=stepOutputFileName,
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matchMethod=matchMethod,
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strictMatch=strictMatch)
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print " Done!"
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if not os.access(outputDir, os.F_OK):
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os.makedirs(outputDir)
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if not os.access(logDir, os.F_OK):
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os.makedirs(logDir)
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outFileName = outputDir + "/" + "voidOverlap" #+ ".dat"
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with open(baseCatalogDir+"/sample_info.dat", 'rb') as input:
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baseSample = pickle.load(input)
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with open(compareCatalogDir+"/sample_info.dat", 'rb') as input:
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sample = pickle.load(input)
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print " Comparing", baseSample.fullName, "to", sample.fullName, "...",
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sys.stdout.flush()
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sampleName = sample.fullName
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binPath = pathToCTools+"/analysis/voidOverlap"
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logFile = logDir+"/compare_"+baseSample.fullName+"_"+sampleName+".out"
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stepOutputFileName = outFileName + "_" + baseSample.fullName + "_" + \
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sampleName + "_"
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launchVoidOverlap(baseSample, sample, baseCatalogDir,
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compareCatalogDir, binPath,
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thisDataPortion=dataPortion, logFile=logFile,
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continueRun=False, workDir=workDir,
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outputFile=stepOutputFileName,
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matchMethod=matchMethod,
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strictMatch=strictMatch)
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print " Done!"
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return
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@ -43,68 +43,68 @@ def plotNumberFunction(catalogList, figDir="./",
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plotName="numberfunc",
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dataPortion="central"):
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print "Plotting number function"
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plt.clf()
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plt.xlabel("$R_{eff}$ [$h^{-1}Mpc$]", fontsize=14)
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plt.ylabel(r"log ($n$ (> R) [$h^3$ Gpc$^{-3}$])", fontsize=14)
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for (iSample,catalog) in enumerate(catalogList):
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sample = catalog.sampleInfo
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data = catalog.voids[:].radius
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if sample.dataType == "observation":
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maskFile = sample.maskFile
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boxVol = vp.getSurveyProps(maskFile,
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sample.zBoundary[0], sample.zBoundary[1],
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sample.zRange[0], sample.zRange[1], "all",
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selectionFuncFile=None)[0]
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#selectionFuncFile=sample.selFunFile)[0]
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boxVol *= obsFudgeFactor
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else:
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boxVol = sample.boxLen*sample.boxLen*(sample.zBoundaryMpc[1] -
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sample.zBoundaryMpc[0])
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boxVol *= 1.e-9 # Mpc->Gpc
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bins = args.xmax/5.
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hist, binEdges = np.histogram(data, bins=bins, range=(0., 100.))
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binCenters = 0.5*(binEdges[1:] + binEdges[:-1])
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nvoids = len(data)
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var = hist * (1. - hist/nvoids)
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sig = np.sqrt(var)
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lowerbound = hist - sig
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upperbound = hist + sig
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mean = np.log10(hist/boxVol)
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lowerbound = np.log10(lowerbound/boxVol)
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upperbound = np.log10(upperbound/boxVol)
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lineColor = colorList[iSample]
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lineTitle = sample.fullName
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print "Plotting number function"
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trim = (bounds[0] > .01)
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mean = mean[trim]
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binCentersToUse = binCenters[trim]
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lower = lowerbound[trim]
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upper = upperbound[trim]
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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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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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linewidth=3)
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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+".png", bbox_inches="tight")
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plt.clf()
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plt.xlabel("$R_{eff}$ [$h^{-1}Mpc$]", fontsize=14)
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plt.ylabel(r"log ($n$ (> R) [$h^3$ Gpc$^{-3}$])", fontsize=14)
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for (iSample,catalog) in enumerate(catalogList):
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sample = catalog.sampleInfo
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data = catalog.voids[:].radius
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if sample.dataType == "observation":
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maskFile = sample.maskFile
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boxVol = vp.getSurveyProps(maskFile,
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sample.zBoundary[0], sample.zBoundary[1],
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sample.zRange[0], sample.zRange[1], "all",
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selectionFuncFile=None)[0]
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#selectionFuncFile=sample.selFunFile)[0]
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boxVol *= obsFudgeFactor
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else:
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boxVol = sample.boxLen*sample.boxLen*(sample.zBoundaryMpc[1] -
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sample.zBoundaryMpc[0])
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boxVol *= 1.e-9 # Mpc->Gpc
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bins = args.xmax/5.
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hist, binEdges = np.histogram(data, bins=bins, range=(0., 100.))
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binCenters = 0.5*(binEdges[1:] + binEdges[:-1])
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nvoids = len(data)
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var = hist * (1. - hist/nvoids)
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sig = np.sqrt(var)
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lowerbound = hist - sig
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upperbound = hist + sig
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mean = np.log10(hist/boxVol)
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lowerbound = np.log10(lowerbound/boxVol)
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upperbound = np.log10(upperbound/boxVol)
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lineColor = colorList[iSample]
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lineTitle = sample.fullName
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trim = (bounds[0] > .01)
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mean = mean[trim]
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binCentersToUse = binCenters[trim]
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lower = lowerbound[trim]
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upper = upperbound[trim]
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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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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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linewidth=3)
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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+".png", bbox_inches="tight")
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@ -94,7 +94,7 @@ def fitHSWProfile(radii, densities, sigmas):
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# pcov: covariance matrix
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popt, pcov = curve_fit(HamausProfile, radii, densities,
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sigma=sigmas)
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sigma=sigmas,
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maxfev=10000, xtol=5.e-3,
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p0=[1.0,-1.0])
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@ -61,7 +61,7 @@ def computeCrossCor(catalogDir,
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outputDir="./", logDir="./",
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matchMethod="useID", dataPortion="central",
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strictMatch=True,
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pathToCTools="../../../c_tools")
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pathToCTools="../../../c_tools"):
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# Computes void-void and void-matter(galaxy) correlations
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# baseCatalogDir: directory of catalog
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