added more tools and plotting for cross-comparison catalog analysis

crossCompare/analysis/makeCocenterProfiles.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python
+#+
+#   VIDE -- Void IDEntification pipeline -- ./pipeline/apAnalysis.py
+#   Copyright (C) 2010-2013 Guilhem Lavaux
+#   Copyright (C) 2011-2013 P. M. Sutter
+#
+#   This program is free software; you can redistribute it and/or modify
+#   it under the terms of the GNU General Public License as published by
+#   the Free Software Foundation; version 2 of the License.
+# 
+#
+#   This program is distributed in the hope that it will be useful,
+#   but WITHOUT ANY WARRANTY; without even the implied warranty of
+#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#   GNU General Public License for more details.
+#
+#   You should have received a copy of the GNU General Public License along
+#   with this program; if not, write to the Free Software Foundation, Inc.,
+#   51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+#+
+
+# takes voids of given radii, computes 1 profiles, 
+#   then computes 1d profiles  for higher-resolution catalogs using
+#   same positions
+
+# computes radial density profiles centered on baseSample
+
+import imp
+import pickle
+import os
+import numpy as np
+import argparse
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from void_python_tools.backend import *
+from util import *
+
+# ------------------------------------------------------------------------------
+
+parser = argparse.ArgumentParser(description='Analyze.')
+parser.add_argument('--parm', dest='parm', default='datasetsToAnalyze.py', help='path to parameter file')
+parser.add_argument('--show', dest='showPlot', action='store_const',
+                   const=True, default=False,
+                   help='display the plot (default: just write eps)')
+args = parser.parse_args()
+
+# -----------------------------------------------------------------------------
+# plot a slice of the density around the void in baseIDList, 
+#   with any voids in the slice shown and any voids in baseIDList flagged
+def saveProfiles(baseSample, stack, sampleList, profileList, radii,
+                 figDir, showPlot, outputDir):
+
+  thisRadius = str(stack.rMin) + "-" + str(stack.rMax)
+
+  plotName = "1dprofile_cocenter_" + baseSample.fullName+"_"+thisRadius
+
+  np.savez(outputDir+"/1dprofile_cocentered_"+plotName+".dat", 
+           profileList, radii)
+
+  return
+
+# -----------------------------------------------------------------------------
+
+filename = args.parm
+print " Loading parameters from", filename
+if not os.access(filename, os.F_OK):
+  print "  Cannot find parameter file %s!" % filename
+  exit(-1)
+parms = imp.load_source("name", filename)
+globals().update(vars(parms))
+
+if not os.access(outputDir, os.F_OK):
+  os.makedirs(outputDir)
+
+if not os.access(logDir, os.F_OK):
+  os.makedirs(logDir)
+
+if not os.access(figDir, os.F_OK):
+  os.makedirs(figDir)
+
+# get list of base voids
+with open(workDir+baseSampleDir+"/sample_info.dat", 'rb') as input:
+  baseSample = pickle.load(input)
+  baseSampleName = baseSample.fullName
+  baseVoidList = np.loadtxt(workDir+baseSampleDir+"/centers_central_"+\
+                    baseSampleName+".out")
+
+sampleList = []
+for sampleDir in sampleDirList:
+  if compareSampleTag in sampleDir: continue
+  with open(workDir+sampleDir+"/sample_info.dat", 'rb') as input:
+    sampleList.append(pickle.load(input))
+
+sampleDirList.insert(0,baseSampleDir)
+sampleList.insert(0,baseSample)
+
+# pick our void sample
+for stack in baseSample.stacks:
+  print " Stack:", stack.rMin, "-", stack.rMax
+  accepted = (baseVoidList[:,4] > stack.rMin) & (baseVoidList[:,4] < stack.rMax)
+  stackVoidList = baseVoidList[accepted]
+  print "  We have", len(stackVoidList), "voids here"
+
+  profileList = []
+  radii = []
+
+  rMaxProfile = stack.rMin*3 + 2
+  if baseSample.profileBinSize == "auto":
+    density = 0.5 * 50 / rMaxProfile / 2
+  else:
+    density = baseSample.profileBinSize
+  nBins = rMaxProfile*density
+
+  for (iSample, sampleDir) in enumerate(sampleDirList):
+    if compareSampleTag in sampleDir: continue
+    sample = sampleList[iSample]
+    print "  Working with", sample.fullName, "..."
+    sys.stdout.flush()
+    sampleName = sample.fullName
+
+    print "   Loading particle data..."
+    partData, boxLen, volNorm = loadPart(workDir, sampleDir, sample)
+
+    stackedProfile = np.zeros((nBins))
+
+    print "   Stacking voids..."
+    binCenters = [] 
+    for void in stackVoidList:
+      periodicLine = getPeriodic(sample)
+      center = void[0:3]
+      shiftedPart = shiftPart(partData, center, periodicLine, boxLen)
+
+      dist = np.sqrt(shiftedPart[:,0]**2 + shiftedPart[:,1]**2 + \
+                     shiftedPart[:,2]**2)  
+      thisProfile, radii = np.histogram(dist, bins=nBins, range=(0,rMaxProfile))
+      deltaV = 4*np.pi/3*(radii[1:]**3-radii[0:(radii.size-1)]**3)   
+      thisProfile = np.float32(thisProfile)
+      thisProfile /= deltaV
+      stackedProfile += thisProfile
+      binCenters = 0.5*(radii[1:]+radii[:-1])
+      
+    stackedProfile /= volNorm
+    stackedProfile /= len(stackVoidList)
+
+    profileList.append(stackedProfile)
+
+  # plot these profiles
+  print "  Plotting..."
+  sys.stdout.flush()
+  #binCenters = 0.5*(radii[1:] + radii[:-1])
+
+  saveProfiles(baseSample, stack, sampleList, profileList, binCenters,
+               figDir, args.showPlot, outputDir)
+    
+print " Done!"
+

crossCompare/analysis/overlapVoids.py

@@ -0,0 +1,81 @@
+#!/usr/bin/env python
+#+
+#   VIDE -- Void IDEntification pipeline -- ./crossCompare/analysis/mergerTree.py
+#   Copyright (C) 2010-2013 Guilhem Lavaux
+#   Copyright (C) 2011-2013 P. M. Sutter
+#
+#   This program is free software; you can redistribute it and/or modify
+#   it under the terms of the GNU General Public License as published by
+#   the Free Software Foundation; version 2 of the License.
+# 
+#
+#   This program is distributed in the hope that it will be useful,
+#   but WITHOUT ANY WARRANTY; without even the implied warranty of
+#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#   GNU General Public License for more details.
+#
+#   You should have received a copy of the GNU General Public License along
+#   with this program; if not, write to the Free Software Foundation, Inc.,
+#   51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+#+
+
+# computes the overlap between two void catalogs
+
+from void_python_tools.backend import *
+from void_python_tools.plotting import *
+import imp
+import pickle
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+import argparse
+
+# ------------------------------------------------------------------------------
+
+parser = argparse.ArgumentParser(description='Analyze.')
+parser.add_argument('--parm', dest='parm', default='datasetsToAnalyze.py',
+                    help='path to parameter file')
+args = parser.parse_args()
+
+# ------------------------------------------------------------------------------
+
+filename = args.parm
+print " Loading parameters from", filename
+if not os.access(filename, os.F_OK):
+  print "  Cannot find parameter file %s!" % filename
+  exit(-1)
+parms = imp.load_source("name", filename)
+globals().update(vars(parms))
+
+if not os.access(outputDir, os.F_OK):
+  os.makedirs(outputDir)
+
+outFileName = outputDir + "/" + "voidOverlap" #+ ".dat"
+
+with open(workDir+baseSampleDir+"/sample_info.dat", 'rb') as input:
+  baseSample = pickle.load(input)
+
+for (iSample, sampleDir) in enumerate(sampleDirList):
+
+  with open(workDir+sampleDir+"/sample_info.dat", 'rb') as input:
+    sample = pickle.load(input)
+
+  print " Working with", sample.fullName, "...",
+  sys.stdout.flush()
+
+  sampleName = sample.fullName
+
+  binPath = CTOOLS_PATH+"/analysis/voidOverlap"
+  logFile = logDir+"/mergertree_"+baseSample.fullName+"_"+sampleName+".out"
+  stepOutputFileName = outFileName + "_" + baseSample.fullName + "_" + \
+                       sampleName + "_"
+
+  launchVoidOverlap(baseSample, sample, workDir+baseSampleDir, 
+                    workDir+sampleDir, binPath, 
+                    thisDataPortion="central", logFile=logFile,
+                    continueRun=False, workDir=workDir,
+                    outputFile=stepOutputFileName,
+                    #matchMethod="useID")
+                    matchMethod="prox")
+
+print " Done!"

crossCompare/parm/sampleParm.py

@@ -0,0 +1,32 @@
+#!/usr/bin/env python
+
+
+workDir = "" # base directory for all samples
+outputDir = "" 
+logDir  = "./logs/"
+figDir  = "./figs/"
+
+# path to c_tools directory in VIDE
+CTOOLS_PATH = "/home/psutter2/projects/Voids/vide/c_tools/"
+
+# the path under workDir/ which which holds the sample you want to compare againt (e.g., the fiducial case)
+baseSampleDir = "sample_/"
+
+# comma-separated list of samples to compare against baseSampleDir
+sampleDirList = [ 
+                  "sample1_/",
+                  "sample2_/",
+                  "sample3_/",
+                ]
+
+dataPortions = [ "all" ]
+
+# this name gets appended to all output filenames and plots
+plotLabel = "test"
+
+# title to place on plots
+plotTitle = "Test"
+
+# don't touch this for now; it will be fully implemented and explained later
+compareSampleTag = ""
+doTheory = False

crossCompare/plotting/plotCocenterProfiles.py

@@ -0,0 +1,174 @@
+#!/usr/bin/env python
+#+
+#   VIDE -- Void IDEntification pipeline -- ./pipeline/apAnalysis.py
+#   Copyright (C) 2010-2013 Guilhem Lavaux
+#   Copyright (C) 2011-2013 P. M. Sutter
+#
+#   This program is free software; you can redistribute it and/or modify
+#   it under the terms of the GNU General Public License as published by
+#   the Free Software Foundation; version 2 of the License.
+# 
+#
+#   This program is distributed in the hope that it will be useful,
+#   but WITHOUT ANY WARRANTY; without even the implied warranty of
+#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#   GNU General Public License for more details.
+#
+#   You should have received a copy of the GNU General Public License along
+#   with this program; if not, write to the Free Software Foundation, Inc.,
+#   51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+#+
+
+# plots radial density profiles centered on baseVoid.
+#   requires makeCocenteredProfiles to be run first!
+
+import imp
+import pickle
+import os
+import numpy as np
+import argparse
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from void_python_tools.backend import *
+from util import *
+from globalOptions import *
+from scipy.optimize import curve_fit
+
+matplotlib.rcParams.update({'font.size': 20})
+
+# ------------------------------------------------------------------------------
+
+parser = argparse.ArgumentParser(description='Analyze.')
+parser.add_argument('--parm', dest='parm', default='datasetsToAnalyze.py', help='path to parameter file')
+parser.add_argument('--show', dest='showPlot', action='store_const',
+                   const=True, default=False,
+                   help='display the plot (default: just write eps)')
+args = parser.parse_args()
+
+# -----------------------------------------------------------------------------
+# Lavaux & Wandelt (2012) profile
+def LWProfile(r, A0, A3, alpha):
+  return A0 + A3*r**3.
+  #return A0 + A3*r**alpha
+
+# -----------------------------------------------------------------------------
+# http://arxiv.org/pdf/astro-ph/0508297v1.pdf eq. 5
+def PadillaProfile(r, A1, A2, alpha):
+  return 1.5-A1*np.exp(-(A2*r)**alpha)
+
+
+# -----------------------------------------------------------------------------
+# plot a slice of the density around the void in baseIDList, 
+#   with any voids in the slice shown and any voids in baseIDList flagged
+def plotProfiles(baseSample, stack, sampleList,
+                 figDir, showPlot, outputDir, doTheory):
+
+  thisRadius = str(stack.rMin) + "-" + str(stack.rMax)
+  plotName = "1dprofile_cocenter_" + baseSample.fullName+"_"+thisRadius
+
+  filename = "1dprofile_cocenter_" + baseSample.fullName+"_"+thisRadius
+  npzfile = np.load(outputDir+"/1dprofile_cocentered_"+plotName+".dat.npz")
+  profileList = npzfile['arr_0']
+  radii = npzfile['arr_1']
+
+  plt.clf()
+
+  plt.xlabel(r"$R/R_{eff}$")
+  #plt.xlabel(r"$R/R_{v,\mathrm{max}}$")
+  plt.ylabel(r"$n / \bar n$")
+  plt.xlim(xmin=0.0, xmax=2.5)
+  plt.ylim(ymin=0.0, ymax=1.7)
+  #plt.xscale('log')
+
+  for (iSample, sample) in enumerate(sampleList):
+    lineTitle = sample.nickName[:-10]
+    if "DM LowDen" in lineTitle or "DM HighDen" in lineTitle: continue
+
+    thisPlotTitle = r"$R_{eff}$ = "+thisRadius+ r" $h^{-1}$Mpc"
+    legendTitle = "Fixed Center Samples"
+
+    thisProfile = profileList[iSample]
+    if np.all(thisProfile == 0.): continue
+
+    rV = (stack.rMin + stack.rMax)/2.
+
+    if len(radii) > 0:
+      scaledRadii = radii/rV
+      plt.plot(scaledRadii, thisProfile, label=lineTitle, 
+               color=colorList[iSample],
+               linewidth=linewidth)
+
+      if doTheory:
+        nBins = len(scaledRadii)/2
+        try:
+          popt, pcov = curve_fit(PadillaProfile, scaledRadii[0:nBins],
+                             thisProfile[0:nBins], maxfev=10000, xtol=5.e-3)
+        except RuntimeError:
+          print "Warning: no convergence reached"
+
+        label = r"A1=%.2f, A2=%.2f, $\alpha$=%.2f" % (popt[0], popt[1], popt[2])
+        rho = PadillaProfile(scaledRadii, popt[0], popt[1], popt[2])
+        plt.plot(scaledRadii, rho, '--', label=label,
+                 color=colorList[iSample], linewidth=2)
+
+
+  plt.title(thisPlotTitle + " (center from " + plotTitle + ")", fontsize=18)
+  plt.legend(title = legendTitle, loc = "lower right", prop={'size':16})
+
+  plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
+  plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
+  plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
+
+  if (showPlot):
+    os.system("display %s" % figDir+"/fig_"+plotName+".png")
+
+  return
+
+# -----------------------------------------------------------------------------
+
+filename = args.parm
+print " Loading parameters from", filename
+if not os.access(filename, os.F_OK):
+  print "  Cannot find parameter file %s!" % filename
+  exit(-1)
+parms = imp.load_source("name", filename)
+globals().update(vars(parms))
+
+if not os.access(outputDir, os.F_OK):
+  os.makedirs(outputDir)
+
+if not os.access(logDir, os.F_OK):
+  os.makedirs(logDir)
+
+if not os.access(figDir, os.F_OK):
+  os.makedirs(figDir)
+
+# get list of base voids
+with open(workDir+baseSampleDir+"/sample_info.dat", 'rb') as input:
+  baseSample = pickle.load(input)
+  baseSampleName = baseSample.fullName
+  baseVoidList = np.loadtxt(workDir+baseSampleDir+"/centers_central_"+\
+                    baseSampleName+".out")
+
+sampleList = []
+for sampleDir in sampleDirList:
+  if compareSampleTag in sampleDir: continue
+  with open(workDir+sampleDir+"/sample_info.dat", 'rb') as input:
+    sampleList.append(pickle.load(input))
+
+sampleDirList.insert(0,baseSampleDir)
+sampleList.insert(0,baseSample)
+
+# pick our void sample
+for stack in baseSample.stacks:
+  print " Stack:", stack.rMin, "-", stack.rMax
+
+  # plot these profiles
+  print "  Plotting..."
+  sys.stdout.flush()
+  plotProfiles(baseSample, stack, sampleList,
+               figDir, args.showPlot, outputDir, doTheory)
+    
+print " Done!"
+

crossCompare/plotting/plotCompareDensCon.py

@@ -1,121 +0,0 @@
-#+
-#   VIDE -- Void IDEntification pipeline -- ./crossCompare/plotting/plotCompareDensCon.py
-#   Copyright (C) 2010-2013 Guilhem Lavaux
-#   Copyright (C) 2011-2013 P. M. Sutter
-#
-#   This program is free software; you can redistribute it and/or modify
-#   it under the terms of the GNU General Public License as published by
-#   the Free Software Foundation; version 2 of the License.
-# 
-#
-#   This program is distributed in the hope that it will be useful,
-#   but WITHOUT ANY WARRANTY; without even the implied warranty of
-#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-#   GNU General Public License for more details.
-#
-#   You should have received a copy of the GNU General Public License along
-#   with this program; if not, write to the Free Software Foundation, Inc.,
-#   51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-#+
-#!/usr/bin/env python
-
-# plots cumulative distributions of number counts versus density contrast
-
-from void_python_tools.backend import *
-from void_python_tools.plotting import *
-import void_python_tools.apTools as vp
-import imp
-import pickle
-import os
-import matplotlib.pyplot as plt
-import numpy as np
-import argparse
-
-# ------------------------------------------------------------------------------
-
-plotNameBase = "compdenscon"
-
-obsFudgeFactor = .66 # what fraction of the volume are we *reall* capturing?
-
-parser = argparse.ArgumentParser(description='Plot.')
-parser.add_argument('--show', dest='showPlot', action='store_const',
-                   const=True, default=False,
-                   help='display the plot (default: just write eps)')
-parser.add_argument('--parmFile', dest='parmFile', default='datasetsToPlot.py',
-                    help='path to parameter file')
-args = parser.parse_args()
-
-# ------------------------------------------------------------------------------
-
-filename = args.parmFile
-print " Loading parameters from", filename
-if not os.access(filename, os.F_OK):
-  print "  Cannot find parameter file %s!" % filename
-  exit(-1)
-parms = imp.load_source("name", filename)
-globals().update(vars(parms))
-
-if not os.access(figDir, os.F_OK):
-  os.makedirs(figDir)
-
-dataSampleList = []
-
-for sampleDir in sampleDirList:
-  with open(workDir+sampleDir+"/sample_info.dat", 'rb') as input:
-    dataSampleList.append(pickle.load(input))
-
-plt.clf()
-plt.xlabel("Void Density Contrast")
-plt.ylabel(r"N > R [$h^3$ Gpc$^{-3}$]")
-plt.yscale('log')
-plt.xlim(xmax=5.)
-
-plotName = plotNameBase
-
-for (iSample,sample) in enumerate(dataSampleList):
-
-  sampleName = sample.fullName
-  lineTitle = sampleName
-
-  if sample.dataType == "observation":
-    boxVol = vp.getSurveyProps(sample.maskFile, 
-                               sample.zBoundary[0], sample.zBoundary[1], 
-                               sample.zRange[0], sample.zRange[1], "all",
-                               selectionFuncFile=sample.selFunFile)[0]
-    boxVol *= obsFudgeFactor
-  else:
-    boxVol = sample.boxLen*sample.boxLen*(sample.zBoundaryMpc[1] - 
-                                          sample.zBoundaryMpc[0])
-
-  boxVol *= 1.e-9 # Mpc->Gpc
-
-  print " Loading", sampleName
-  filename = workDir+"/"+sampleDirList[iSample]+"/centers_"+dataPortion+"_"+\
-             sampleName+".out"
-  if not os.access(filename, os.F_OK):
-    print "File not found: ", filename
-    continue
-
-  data = np.loadtxt(filename, comments="#")
-  if data.ndim == 1:
-    print " Too few!"
-    continue
-  data = data[:,8]
-  indices = np.arange(0, len(data), 1)
-  sorted = np.sort(data)
-
-  plt.plot(sorted, indices[::-1]/boxVol, '-',
-           label=lineTitle, color=colorList[iSample],
-           linewidth=linewidth)
-
-plt.legend(title = "Samples", loc = "upper right", prop={'size':8})
-#plt.title(plotTitle)
-
-plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
-plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
-plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
-
-if args.showPlot:
-  os.system("display %s" % figDir+"/fig_"+plotName+".png")
-
-

crossCompare/plotting/plotDenMaps.py

@@ -0,0 +1,385 @@
+#!/usr/bin/env python
+#+
+#   VIDE -- Void IDEntification pipeline -- ./pipeline/apAnalysis.py
+#   Copyright (C) 2010-2013 Guilhem Lavaux
+#   Copyright (C) 2011-2013 P. M. Sutter
+#
+#   This program is free software; you can redistribute it and/or modify
+#   it under the terms of the GNU General Public License as published by
+#   the Free Software Foundation; version 2 of the License.
+# 
+#
+#   This program is distributed in the hope that it will be useful,
+#   but WITHOUT ANY WARRANTY; without even the implied warranty of
+#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#   GNU General Public License for more details.
+#
+#   You should have received a copy of the GNU General Public License along
+#   with this program; if not, write to the Free Software Foundation, Inc.,
+#   51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+#+
+
+# takes nVoids evenly distributed, plots a slice of the local density and
+#  overlays the voids
+
+import imp
+import pickle
+import os
+import numpy as np
+import argparse
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from void_python_tools.backend import *
+import void_python_tools.xcor as xcor
+from netCDF4 import Dataset
+#import pylab as plt
+
+NetCDFFile = Dataset
+ncFloat = 'f8'
+
+matplotlib.rcParams.update({'font.size': 16})
+# ------------------------------------------------------------------------------
+
+mergerNameBase = "voidOverlap"
+
+parser = argparse.ArgumentParser(description='Analyze.')
+parser.add_argument('--parm', dest='parm', default='datasetsToAnalyze.py', help='path to parameter file')
+parser.add_argument('--show', dest='showPlot', action='store_const',
+                   const=True, default=False,
+                   help='display the plot (default: just write eps)')
+args = parser.parse_args()
+
+nVoids = 10
+
+# ------------------------------------------------------------------------------
+
+# -----------------------------------------------------------------------------
+# plot a slice of the density around the void in baseIDList, 
+#   with any voids in the slice shown and any voids in baseIDList flagged
+def plotVoidAndDen(idList, voidList, partData, boxLen, figDir,
+                   sliceCenter=None, sliceWidth=200,
+                   baseIDList=None, baseRadius=0, nickName=None,
+                   baseNickName=None,
+                   baseIndex=0,
+                   periodic=None, showPlot=False, plotName=None):
+
+  if len(voidList) <= 1: return
+  plt.clf()
+
+  #sliceWidth = 220
+  sliceWidth = max(220, sliceWidth)
+
+  # make an appropriate box
+  xwidth = sliceWidth
+  ywidth = sliceWidth
+  zwidth = sliceWidth/4.
+  #zwidth = max(sliceWidth/4., 50)
+
+  # get mean density
+  part = 1.*partData
+  totalNumPart = len(part)
+  totalVol = (part[:,0].max() - part[:,0].min()) * \
+             (part[:,1].max() - part[:,1].min()) * \
+             (part[:,2].max() - part[:,2].min())
+
+  meanDen = totalNumPart/totalVol
+
+  # single out the matched void
+  keepVoid = []
+  if len(np.atleast_1d(idList)) > 0:
+    keepVoid = voidList[voidList[:,7] == idList]
+  if len(np.shape(keepVoid)) > 1: keepVoid = keepVoid[0,:]
+  filter = voidList[:,7] != idList
+  voidList = voidList[filter,:]
+
+  # convert everything to relative coordinates
+  part[:,0] -= sliceCenter[0]
+  part[:,1] -= sliceCenter[1]
+  part[:,2] -= sliceCenter[2]
+
+  shiftUs = np.abs(part[:,0]) > boxLen[0]/2.
+  if ("x" in periodicLine): part[shiftUs,0] -= \
+                            np.copysign(boxLen[0],part[shiftUs,0])
+  shiftUs = np.abs(part[:,1]) > boxLen[1]/2.
+  if ("y" in periodicLine): part[shiftUs,1] -= \
+                            np.copysign(boxLen[1],part[shiftUs,1])
+  shiftUs = np.abs(part[:,2]) > boxLen[2]/2.
+  if ("z" in periodicLine): part[shiftUs,2] -= \
+                            np.copysign(boxLen[2],part[shiftUs,2])
+  
+  voidList = np.atleast_2d(voidList)
+  np.atleast_2d(voidList)[:,0] -= sliceCenter[0]
+  np.atleast_2d(voidList)[:,1] -= sliceCenter[1]
+  np.atleast_2d(voidList)[:,2] -= sliceCenter[2]
+
+  shiftUs = np.abs(voidList[:,0]) > boxLen[0]/2.
+  if ("x" in periodicLine): 
+    voidList[shiftUs,0] -= \
+                            np.copysign(boxLen[0],voidList[shiftUs,0])
+  shiftUs = np.abs(voidList[:,1]) > boxLen[1]/2.
+  if ("y" in periodicLine): voidList[shiftUs,1] -= \
+                            np.copysign(boxLen[1],voidList[shiftUs,1])
+  shiftUs = np.abs(voidList[:,2]) > boxLen[2]/2.
+  if ("z" in periodicLine): voidList[shiftUs,2] -= \
+                            np.copysign(boxLen[2],voidList[shiftUs,2])
+
+  if len(np.atleast_1d(keepVoid)) >= 1:
+    keepVoid[0] -= sliceCenter[0]
+    keepVoid[1] -= sliceCenter[1]
+    keepVoid[2] -= sliceCenter[2]
+    shiftUs = np.abs(keepVoid[0]) > boxLen[0]/2.
+    if ("x" in periodicLine) and shiftUs: keepVoid[0] -= \
+                                        np.copysign(boxLen[0],keepVoid[0])
+    shiftUs = np.abs(keepVoid[1]) > boxLen[1]/2.
+    if ("y" in periodicLine) and shiftUs: keepVoid[1] -= \
+                                        np.copysign(boxLen[1],keepVoid[1])
+    shiftUs = np.abs(keepVoid[2]) > boxLen[2]/2.
+    if ("z" in periodicLine) and shiftUs: keepVoid[2] -= \
+                                        np.copysign(boxLen[2],keepVoid[2])
+
+  xmin = -xwidth/2.
+  xmax = xwidth/2.
+  ymin = -ywidth/2.
+  ymax = ywidth/2.
+  zmin = -zwidth/2.
+  zmax = zwidth/2.
+
+  # pull out voids that were potential matches
+  filter = np.sqrt(voidList[:,0]**2 + voidList[:,1]**2 + voidList[:,2]**2) <=\
+           baseRadius*1.5
+  potentialMatches = voidList[filter] 
+
+  # get centers and radii of any other voids in slice
+  zminVoid = -zwidth/16.
+  zmaxVoid = zwidth/16.
+
+  filter = (voidList[:,0] > xmin) & (voidList[:,0] < xmax) & \
+           (voidList[:,1] > ymin) & (voidList[:,1] < ymax) & \
+           (voidList[:,2] > zminVoid) & (voidList[:,2] < zmaxVoid) 
+  voidList = voidList[filter,:]
+
+  # slice particles
+  filter = (part[:,0] > xmin) & (part[:,0] < xmax) & \
+           (part[:,1] > ymin) & (part[:,1] < ymax) & \
+           (part[:,2] > zmin) & (part[:,2] < zmax) 
+  part = part[filter]
+
+  # plot density
+  extent = [xmin, xmax, ymin, ymax]
+  hist, xedges, yedges = np.histogram2d(part[:,0], part[:,1], normed=False,
+                                        bins=64)
+ 
+  #hist /= meanDen 
+  hist = np.log10(hist+1)
+  plt.imshow(hist, 
+             aspect='equal',
+             extent=extent,
+             interpolation='gaussian',
+             cmap='YlGnBu_r')
+  #plt.colorbar()
+
+  # overlay voids as circles
+  fig = plt.gcf()
+  ax = fig.add_subplot(1,1,1)
+
+  # the original void
+  circle = plt.Circle((0,0), baseRadius,
+                      edgecolor='orange', facecolor=None, fill=False,
+                      linewidth=5)
+  fig.gca().add_artist(circle)
+
+  # our matched void
+  if len(np.atleast_1d(keepVoid)) > 0:
+    if idList == baseIDList:
+      edgecolor = 'orange'
+    else:
+      edgecolor = 'red'
+
+    circle = plt.Circle((keepVoid[0], keepVoid[1]), keepVoid[4], 
+                         edgecolor=edgecolor, facecolor=None, fill=False,
+                         linewidth=5)
+    fig.gca().add_artist(circle)
+ 
+  # other voids in the slice 
+  for void in voidList:
+    if np.any(void[7] == idList):
+      continue
+    else:
+      color = 'white'
+    circle = plt.Circle((void[0], void[1]), void[4], 
+                         edgecolor=color, facecolor=None, fill=False,
+                         linewidth=5)
+    fig.gca().add_artist(circle)
+
+  # potential match voids
+  #for void in potentialMatches:
+  #  if np.any(void[7] == idList):
+  #    continue
+  #  else:
+  #    color = 'green'
+  #  circle = plt.Circle((void[0], void[1]), void[4], 
+  #                       edgecolor=color, facecolor=None, fill=False,
+  #                       linewidth=5)
+  #  fig.gca().add_artist(circle)
+
+  baseNickName = baseNickName[:-10].lstrip()
+  nickName = nickName[:-10].lstrip()
+  if idList == baseIDList:
+    title = "%d $h^{-1}$Mpc" % int(baseRadius)
+    title += " (" + baseNickName + ")"
+  else:
+    title = r"$\rightarrow$ " 
+    if len(np.atleast_1d(keepVoid)) > 0:
+      title += "%d $h^{-1}$Mpc" % int(keepVoid[4]) + " (" + nickName + ")"
+    else:
+      title += "No match"
+      
+  #title += "(" + str(int(baseIDList)) + ")"
+  
+  plt.title(title, fontsize=20)
+  #plt.xlabel("x [$h^{-1}$Mpc]", fontsize=14)
+  #plt.ylabel("y [$h^{-1}$Mpc]", fontsize=14)
+
+  plotName += "_" + str(int(baseIndex))
+  #plotName += "_" + str(int(baseRadius))
+  plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
+  plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
+  plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
+
+  if showPlot: os.system("display %s" % figDir+"/fig_"+plotName+".png")
+
+  return
+
+# -----------------------------------------------------------------------------
+
+filename = args.parm
+print " Loading parameters from", filename
+if not os.access(filename, os.F_OK):
+  print "  Cannot find parameter file %s!" % filename
+  exit(-1)
+parms = imp.load_source("name", filename)
+globals().update(vars(parms))
+
+if not os.access(outputDir, os.F_OK):
+  os.makedirs(outputDir)
+
+if not os.access(logDir, os.F_OK):
+  os.makedirs(logDir)
+
+if not os.access(figDir, os.F_OK):
+  os.makedirs(figDir)
+
+mergerFileBase = outputDir + "/" + mergerNameBase
+
+# get list of base voids
+with open(workDir+baseSampleDir+"/sample_info.dat", 'rb') as input:
+  baseSample = pickle.load(input)
+  baseSampleName = baseSample.fullName
+  baseVoidList = np.loadtxt(workDir+baseSampleDir+"/centers_central_"+\
+                    baseSampleName+".out")
+
+  # sort by size
+  radii = baseVoidList[:,4]
+  indices = np.argsort(radii)[::-1]
+  baseVoidList = baseVoidList[indices,:]
+  setName = baseSampleDir.split('/')[0]
+
+# pick our void sample
+bigVoidList = baseVoidList[0:10,:]
+stride = len(baseVoidList)/nVoids
+baseVoidList = baseVoidList[::stride]
+baseVoidList = np.vstack((bigVoidList,baseVoidList))
+
+sampleDirList.insert(0,baseSampleDir)
+
+for (iSample, sampleDir) in enumerate(sampleDirList):
+  if compareSampleTag in sampleDir: continue
+  with open(workDir+sampleDir+"/sample_info.dat", 'rb') as input:
+    sample = pickle.load(input)
+  print "  Working with", sample.fullName, "..."
+  sys.stdout.flush()
+  sampleName = sample.fullName
+
+  print "    Loading particle data..."
+  sys.stdout.flush()
+
+  
+  infoFile = workDir+"/"+sampleDir+"/zobov_slice_"+sample.fullName+".par"
+  File = NetCDFFile(infoFile, 'r')
+  ranges = np.zeros((3,2))
+  ranges[0][0] = getattr(File, 'range_x_min')
+  ranges[0][1] = getattr(File, 'range_x_max')
+  ranges[1][0] = getattr(File, 'range_y_min')
+  ranges[1][1] = getattr(File, 'range_y_max')
+  ranges[2][0] = getattr(File, 'range_z_min')
+  ranges[2][1] = getattr(File, 'range_z_max')
+  File.close()
+  mul = np.zeros((3))
+  mul[:] = ranges[:,1] - ranges[:,0]
+  boxLen = mul
+
+  partFile = workDir+"/"+sampleDir+"/zobov_slice_"+sample.fullName
+  #partFile = catalogDir+"/"+sample.dataFile
+  iLine = 0
+  partData = []
+  part = np.zeros((3))
+  File = file(partFile)
+  chk = np.fromfile(File, dtype=np.int32,count=1)
+  Np = np.fromfile(File, dtype=np.int32,count=1)
+  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)
+  x *= mul[0]
+  x += ranges[0][0]
+  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)
+  y *= mul[1] 
+  y += ranges[1][0]
+  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)
+  z *= mul[2] 
+  z += ranges[2][0]
+  chk = np.fromfile(File, dtype=np.int32,count=1)
+  File.close()
+  partData = np.column_stack((x,y,z))#.transpose()
+
+  for (iBaseVoid,baseVoid) in enumerate(baseVoidList):
+    print "   Void:", int(baseVoid[7]), "(", int(baseVoid[4]), ")"
+    baseIDList = baseVoid[7]
+    sliceCenter = baseVoid[0:3]
+    sliceWidth  = baseVoid[4]*4
+ 
+    # get matched void
+    idList = []
+    if sample.fullName == baseSample.fullName:
+      idList = baseIDList
+    else:
+      matchFile=mergerFileBase+"_"+baseSampleName+"_"+sampleName+"_summary.out"
+      if os.access(matchFile, os.F_OK):
+        matchList = np.loadtxt(matchFile)
+        for i,testID in enumerate(matchList[:,0]):
+          if testID == baseIDList:
+            if (matchList[i,8] > 0): idList.append(matchList[i,8])
+        idList = np.array(idList)
+        idList = idList.astype(int)
+
+    voidList = np.loadtxt(workDir+sampleDir+"/trimmed_nodencut_centers_central_"+\
+                          sampleName+".out")
+
+    periodicLine = getPeriodic(sample)
+    plotVoidAndDen(idList, voidList, partData, boxLen, figDir, 
+                   sliceCenter=sliceCenter, sliceWidth=sliceWidth, 
+                   baseIDList=baseIDList, baseRadius=baseVoid[4],
+                   baseIndex=iBaseVoid,
+                   nickName=sample.nickName, periodic=periodicLine,
+                   baseNickName=baseSample.nickName,
+                   showPlot=args.showPlot, 
+                   plotName="denmap_"+setName+"_"+baseSampleName+"_"+sampleName) 
+print " Done!"
+

crossCompare/plotting/plotDensConVsR.py

@@ -1,118 +0,0 @@
-#+
-#   VIDE -- Void IDEntification pipeline -- ./crossCompare/plotting/plotDensConVsR.py
-#   Copyright (C) 2010-2013 Guilhem Lavaux
-#   Copyright (C) 2011-2013 P. M. Sutter
-#
-#   This program is free software; you can redistribute it and/or modify
-#   it under the terms of the GNU General Public License as published by
-#   the Free Software Foundation; version 2 of the License.
-# 
-#
-#   This program is distributed in the hope that it will be useful,
-#   but WITHOUT ANY WARRANTY; without even the implied warranty of
-#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-#   GNU General Public License for more details.
-#
-#   You should have received a copy of the GNU General Public License along
-#   with this program; if not, write to the Free Software Foundation, Inc.,
-#   51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-#+
-#!/usr/bin/env python
-
-# plots cumulative distributions of number counts versus density contrast
-
-from void_python_tools.backend import *
-from void_python_tools.plotting import *
-import void_python_tools.apTools as vp
-import imp
-import pickle
-import os
-import matplotlib.pyplot as plt
-import numpy as np
-import argparse
-
-# ------------------------------------------------------------------------------
-
-plotNameBase = "densconvsr"
-
-obsFudgeFactor = .66 # what fraction of the volume are we *reall* capturing?
-
-parser = argparse.ArgumentParser(description='Plot.')
-parser.add_argument('--show', dest='showPlot', action='store_const',
-                   const=True, default=False,
-                   help='display the plot (default: just write eps)')
-parser.add_argument('--parmFile', dest='parmFile', default='datasetsToPlot.py',
-                    help='path to parameter file')
-args = parser.parse_args()
-
-# ------------------------------------------------------------------------------
-
-filename = args.parmFile
-print " Loading parameters from", filename
-if not os.access(filename, os.F_OK):
-  print "  Cannot find parameter file %s!" % filename
-  exit(-1)
-parms = imp.load_source("name", filename)
-globals().update(vars(parms))
-
-if not os.access(figDir, os.F_OK):
-  os.makedirs(figDir)
-
-dataSampleList = []
-
-for sampleDir in sampleDirList:
-  with open(workDir+sampleDir+"/sample_info.dat", 'rb') as input:
-    dataSampleList.append(pickle.load(input))
-
-plt.clf()
-plt.ylabel("Void Density Contrast")
-plt.xlabel("Void Radius [Mpc/h]")
-#plt.xlim(xmax=5.)
-plt.yscale('log')
-
-plotName = plotNameBase
-
-for (iSample,sample) in enumerate(dataSampleList):
-
-  sampleName = sample.fullName
-  lineTitle = sampleName
-
-  if sample.dataType == "observation":
-    boxVol = vp.getSurveyProps(sample.maskFile, 
-                               sample.zBoundary[0], sample.zBoundary[1], 
-                               sample.zRange[0], sample.zRange[1], "all",
-                               selectionFuncFile=sample.selFunFile)[0]
-    boxVol *= obsFudgeFactor
-  else:
-    boxVol = sample.boxLen*sample.boxLen*(sample.zBoundaryMpc[1] - 
-                                          sample.zBoundaryMpc[0])
-
-  boxVol *= 1.e-9 # Mpc->Gpc
-
-  print " Loading", sampleName
-  filename = workDir+"/"+sampleDirList[iSample]+"/centers_"+dataPortion+"_"+\
-             sampleName+".out"
-  if not os.access(filename, os.F_OK):
-    print "File not found: ", filename
-    continue
-
-  data = np.loadtxt(filename, comments="#")
-  if data.ndim == 1:
-    print " Too few!"
-    continue
-
-  plt.plot(data[:,4], data[:,8], '-',
-           label=lineTitle, color=colorList[iSample],
-           linewidth=linewidth)
-
-plt.legend(title = "Samples", loc = "upper right", prop={'size':8})
-#plt.title(plotTitle)
-
-plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
-plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
-plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
-
-if args.showPlot:
-  os.system("display %s" % figDir+"/fig_"+plotName+".png")
-
-

crossCompare/plotting/plotMatchEllipRatio.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python
+
+# plots cumulative distributions of number counts
+
+import matplotlib
+matplotlib.use('Agg')
+from void_python_tools.backend import *
+from void_python_tools.plotting import *
+import void_python_tools.apTools as vp
+import imp
+import pickle
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+import argparse
+from globalOptions import *
+
+# plots the ratio of ellipticities for matched voids
+
+# ------------------------------------------------------------------------------
+
+parser = argparse.ArgumentParser(description='Plot.')
+parser.add_argument('--show', dest='showPlot', action='store_const',
+                   const=True, default=False,
+                   help='display the plot (default: just write eps)')
+parser.add_argument('--parm', dest='parm', default='datasetsToPlot.py',
+                    help='path to parameter file')
+args = parser.parse_args()
+
+# ------------------------------------------------------------------------------
+
+print "Plotting ellipticity ratio"
+
+filename = args.parm
+print " Loading parameters from", filename
+if not os.access(filename, os.F_OK):
+  print "  Cannot find parameter file %s!" % filename
+  exit(-1)
+parms = imp.load_source("name", filename)
+globals().update(vars(parms))
+
+if not os.access(figDir, os.F_OK):
+  os.makedirs(figDir)
+
+dataSampleList = []
+compareSampleList = []
+
+with open(workDir+baseSampleDir+"/sample_info.dat", 'rb') as input:  
+  baseSample = pickle.load(input)
+
+for sampleDir in sampleDirList:
+  with open(workDir+sampleDir+"/sample_info.dat", 'rb') as input:
+    thisSample = pickle.load(input)
+    if compareSampleTag in thisSample.fullName:
+      compareSampleList.append(thisSample)
+    else:
+      dataSampleList.append(thisSample)
+
+
+plt.clf()
+
+numSubPlots = len(dataSampleList)
+fig, axesList = plt.subplots(numSubPlots, sharex=True, sharey=True)
+axesList = np.atleast_1d(axesList)
+
+for (iSample,sample) in enumerate(dataSampleList):
+
+  if sample.fullName == baseSample.fullName: continue
+
+  sampleName = sample.fullName
+  lineTitle = sample.nickName[:-10]
+
+#  plt.xlabel("Void Radius [Mpc/h]")
+#  plt.ylabel(r"1st Progenitor Relative Ellipticity")
+  #plt.yscale('log')
+#  plt.xlim(xmax=rMax)
+  plt.xlim(rMin, rMax)
+
+  plotNameBase = "matchrelellip"
+  plotName = plotNameBase + "_" + plotLabel# + "_" + sampleName
+
+
+  filename = outputDir+"/voidOverlap_"+baseSample.fullName+"_"+sampleName+"_summary.out"
+  if not os.access(filename, os.F_OK):
+    print "File not found: ", filename
+    continue
+
+  data = np.loadtxt(filename, comments="#")
+  if data.ndim == 1:
+    print " Too few!"
+    continue
+
+  # find a sample to compare it to
+  for compareSample in compareSampleList:
+    if compareSample.nickName[:10] == sample.nickName[:10]:
+      filename = outputDir+"/voidOverlap_"+baseSample.fullName+"_"+compareSample.fullName+"_summary.out"
+      compareData = np.loadtxt(filename, comments="#")
+      axesList[iSample].scatter(compareData[:,1], compareData[:,10],
+            color='blue', alpha=alpha, s=pointsize)
+
+  #plt.scatter(data[:,1], data[:,10],
+  #         label=lineTitle, color=colorList[iSample])
+
+  axesList[iSample].scatter(data[:,1], data[:,10],
+           label=lineTitle, color='red', alpha=alpha, s=pointsize)
+  axesList[iSample].legend(loc = "upper left", prop={'size':10})
+
+  plt.ylim(0., 4.0)
+  yticks = axesList[iSample].yaxis.get_major_ticks()
+  yticks[-1].label1.set_visible(False)
+  yticks[0].label1.set_visible(False)
+
+  #axesList[iSample].set_xlim([20,rMax])
+
+fig.subplots_adjust(hspace=0)
+
+plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
+
+axesList[0].set_title(plotTitle, fontsize=14)
+#axesList[0].set_title("Match ellipticity ratio - "+plotTitle)
+fig.text(0.5, 0.04, r'$R_{eff}$ [$h^{-1}$Mpc]', ha='center', va='center', fontsize=14)
+fig.text(0.06, 0.5, 'Match Ellipticity Ratio', ha='center', va='center', rotation='vertical', fontsize=14)
+
+
+#plt.legend(title = "Samples", loc = "upper left", prop={'size':8})
+#plt.title("Match ellipticity ratio - "+plotTitle+" - "+sample.nickName)
+
+plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
+plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
+plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
+
+if args.showPlot:
+  os.system("display %s" % figDir+"/fig_"+plotName+".png")
+
+

crossCompare/plotting/plotMatchSizeRatio.py

@@ -0,0 +1,144 @@
+#!/usr/bin/env python
+
+# plots cumulative distributions of number counts
+
+import matplotlib
+matplotlib.use('Agg')
+from void_python_tools.backend import *
+from void_python_tools.plotting import *
+import void_python_tools.apTools as vp
+import imp
+import pickle
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+import argparse
+from globalOptions import *
+from scipy.optimize import curve_fit
+
+# plots the ratio of sizes for matched voids
+
+# ------------------------------------------------------------------------------
+
+parser = argparse.ArgumentParser(description='Plot.')
+parser.add_argument('--show', dest='showPlot', action='store_const',
+                   const=True, default=False,
+                   help='display the plot (default: just write eps)')
+parser.add_argument('--parm', dest='parm', default='datasetsToPlot.py',
+                    help='path to parameter file')
+args = parser.parse_args()
+
+# -----------------------------------------------------------------------------
+def Linear(x, a, b):
+  return a*x+b
+
+# ------------------------------------------------------------------------------
+
+print "Plotting match size ratio"
+
+filename = args.parm
+print " Loading parameters from", filename
+if not os.access(filename, os.F_OK):
+  print "  Cannot find parameter file %s!" % filename
+  exit(-1)
+parms = imp.load_source("name", filename)
+globals().update(vars(parms))
+
+if not os.access(figDir, os.F_OK):
+  os.makedirs(figDir)
+
+dataSampleList = []
+compareSampleList = []
+
+with open(workDir+baseSampleDir+"/sample_info.dat", 'rb') as input:
+  baseSample = pickle.load(input)
+
+for sampleDir in sampleDirList:
+  with open(workDir+sampleDir+"/sample_info.dat", 'rb') as input:
+    thisSample = pickle.load(input)
+    if compareSampleTag in thisSample.fullName:
+      compareSampleList.append(thisSample)
+    else:
+      dataSampleList.append(thisSample)
+
+plt.clf()
+#plt.yscale('log')
+
+numSubPlots = len(dataSampleList)
+fig, axesList = plt.subplots(numSubPlots, sharex=True, sharey=True)
+axesList = np.atleast_1d(axesList)
+
+for (iSample,sample) in enumerate(dataSampleList):
+
+  if sample.fullName == baseSample.fullName: continue
+
+  sampleName = sample.fullName
+  lineTitle = sample.nickName[:-10]
+
+  plotNameBase = "matchvolrelradius"
+  plotName = plotNameBase + "_" + plotLabel# + "_" + sampleName
+
+  filename = outputDir+"/voidOverlap_"+baseSample.fullName+"_"+sampleName+"_summary.out"
+  if not os.access(filename, os.F_OK):
+    print "File not found: ", filename
+    continue
+
+  data = np.loadtxt(filename, comments="#")
+  if data.ndim == 1:
+    print " Too few!"
+    continue
+
+  # find a sample to compare it to
+  for compareSample in compareSampleList:
+    if compareSample.nickName[:10] == sample.nickName[:10]:
+      filename = outputDir+"/voidOverlap_"+baseSample.fullName+"_"+compareSample.fullName+"_summary.out"
+      compareData = np.loadtxt(filename, comments="#")
+      axesList[iSample].scatter(compareData[:,1], compareData[:,2],
+            color='blue', alpha=alpha, s=pointsize)
+ 
+  axesList[iSample].scatter(data[:,1], data[:,2],
+           label=lineTitle, color='red', alpha=alpha, s=pointsize)
+
+  try:
+    popt, pcov = curve_fit(Linear, data[:,1],
+                           data[:,2], maxfev=10000, xtol=5.e-3)
+  except RuntimeError:
+    print "Warning: no convergence reached"
+
+  label = r"$a$=%.2f, $b$=%.2f" % (popt[0], popt[1])
+  radii = np.arange(rMin, rMax, 1)
+  relRad = Linear(radii, popt[0], popt[1])
+  #axesList[iSample].plot(radii, relRad, '-', label=label,
+  #         color='black', linewidth=2)
+
+  axesList[iSample].legend(loc = "best", fancybox=True,prop={'size':10})
+
+  plt.ylim(0., 2.5)
+     
+  yticks = axesList[iSample].yaxis.get_major_ticks()
+  yticks[-1].label1.set_visible(False)
+  yticks[0].label1.set_visible(False)
+ 
+  axesList[iSample].set_xlim([rMin,rMax])
+
+
+fig.subplots_adjust(hspace=0)
+
+plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
+
+axesList[0].set_title(plotTitle, fontsize=14)
+#axesList[0].set_title("Match size ratio - "+plotTitle)
+fig.text(0.5, 0.04, r'$R_{eff}$ [$h^{-1}$Mpc]', ha='center', va='center', fontsize=14)
+fig.text(0.06, 0.5, 'Match Relative Radius', ha='center', va='center', rotation='vertical', fontsize=14)
+
+#plt.legend(title = "Samples", loc = "upper left", prop={'size':8})
+#plt.title("Match size ratio - "+plotTitle+" - "+sample.nickName)
+
+plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
+plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
+plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
+
+if args.showPlot:
+  os.system("display %s" % figDir+"/fig_"+plotName+".png")
+
+

crossCompare/plotting/plotNumberFunc.py

@@ -2,6 +2,8 @@
 
 # plots cumulative distributions of number counts
 
+import matplotlib
+matplotlib.use('Agg')
 from void_python_tools.backend import *
 from void_python_tools.plotting import *
 import void_python_tools.apTools as vp
@@ -11,12 +13,13 @@ import os
 import matplotlib.pyplot as plt
 import numpy as np
 import argparse
+import svdw
+from scipy.optimize import curve_fit
+from scipy.interpolate import interp1d
+from globalOptions import *
 
 # ------------------------------------------------------------------------------
 
-obsFudgeFactor = 1.0 # what fraction of the volume are we *reall* capturing?
-#obsFudgeFactor = .66 # what fraction of the volume are we *reall* capturing?
-
 histBinWidth = 1 # Mpc
 
 parser = argparse.ArgumentParser(description='Plot.')
@@ -35,9 +38,20 @@ parser.add_argument('--xmin', dest='xmin', default=20.,
 args = parser.parse_args()
 
 # ------------------------------------------------------------------------------
+def svdwFunc(r, scaleFactor):
+  radius, cumu_ps = svdw.getSvdW(.01, 100, 100, scaleFactor=scaleFactor)
+  cumu_ps += 0.1
+  cumu_ps = np.log10(cumu_ps)
+  interped = interp1d(radius, cumu_ps)
+  interpVal = interped(r)
+  interpVal[interpVal<1.0] = 1.0
+  #print "HELLO", r, interpVal
+  return interpVal
+
+# ------------------------------------------------------------------------------
 
 
-def loadData(sampleDir, dataPortion):
+def loadData(sampleDir, dataPortion, treePortion='all'):
   with open(workDir+sampleDir+"/sample_info.dat", 'rb') as input:
     sample = pickle.load(input)
 
@@ -51,10 +65,18 @@ def loadData(sampleDir, dataPortion):
     print " Too few!"
     return -1, -1, -1
 
+  if treePortion == "parents":
+    filter = data[:,10] == -1
+    data = data[filter]
+  elif treePortion == "children":
+    filter = data[:,10] != -1
+    data = data[filter]
+
   data = data[:,4]
   indices = np.arange(0, len(data), 1)
   sorted = np.sort(data)
 
+ 
   if sample.dataType == "observation":
     boxVol = vp.getSurveyProps(sample.maskFile,
                                sample.zBoundary[0], sample.zBoundary[1],
@@ -90,7 +112,7 @@ def loadData(sampleDir, dataPortion):
 
   hist = np.log10(hist)
 
-  lineTitle = sample.nickName
+  lineTitle = sample.nickName[:-10]
 
   return hist, binCenters, lineTitle
 
@@ -122,17 +144,19 @@ if not os.access(figDir, os.F_OK):
   os.makedirs(figDir)
 
 plt.clf()
-plt.xlabel("Void Radius [Mpc/h]")
+plt.xlabel(r"$R_{eff}$ [$h^{-1}$Mpc]", fontsize=14)
-plt.ylabel(r"log (N > R [$h^3$ Gpc$^{-3}$])")
+plt.ylabel(r"log ($n$ > $R_{eff}$ [$h^3$ Gpc$^{-3}$])", fontsize=14)
 #plt.yscale('log')
-#plt.xlim(xmax=args.xmax)
+plt.xlim(xmin=5.)
-#plt.xlim(xmin=args.xmin)
+plt.xlim(xmax=100.)
-#plt.ylim(ymin=-1)
+plt.ylim(ymin=1)
-#plt.ylim(ymax=6)
+plt.ylim(ymax=5)
 
 plotNameBase = "numberfunc"
 plotName = plotNameBase + "_" + plotLabel
 
+sampleDirList.append(baseSampleDir)
+
 for (iSample,sampleDir) in enumerate(sampleDirList):
  for dataPortion in dataPortions:
   # get all the data
@@ -145,7 +169,7 @@ for (iSample,sampleDir) in enumerate(sampleDirList):
       allHist.append(hist)
    
     lineLabel = lineTitle.replace(fileZ, "all")
-    lineLabel += ", " + dataPortion
+    if dataPortion != 'all': lineLabel += ", " + dataPortion
     
     maxHist = 1.*allHist[-1]
     minHist = 1.*allHist[-1]
@@ -170,23 +194,87 @@ for (iSample,sampleDir) in enumerate(sampleDirList):
 
   else:
 
-    hist, binCenters, lineLabel = loadData(sampleDir, dataPortion)
+    #treeList = ["children", "parents", "all"]
-    trim = (hist > 1) 
+    treeList = ["all"]
-    hist = hist[trim]
+    for (iTree,treeItem) in enumerate(treeList):
-    binCentersToUse = binCenters[trim]
+      hist, binCenters, lineLabel = loadData(sampleDir, dataPortion, treePortion=treeItem)
-    if lineLabel == -1: continue
+      trim = (hist > 1) 
-    lineLabel += ", " + dataPortion
+      hist = hist[trim]
-    if dataPortion == "central":
+      binCentersToUse = binCenters[trim]
-      lineStyle = '--'
+      if lineLabel == -1: continue
-    else:
+      if dataPortion != 'all': lineLabel += ", " + dataPortion
-      lineStyle = '-'
-    plt.plot(binCentersToUse, hist, lineStyle,
-              label=lineLabel, color=colorList[iSample],
-              linewidth=linewidth)
 
+      if treeItem != "all": lineLabel += ", " + treeItem
+      iColor = iSample + iTree
 
-plt.legend(title = "Samples", loc = "upper right", prop={'size':8})
+      if dataPortion == "central":
-plt.title("Number func - "+plotTitle)
+        lineStyle = '--'
+      else:
+        lineStyle = '-'
+
+      if "DM" in lineLabel: lineColor = colorList[0]
+      if "Halos" in lineLabel: lineColor = colorList[1]
+      if "HOD" in lineLabel: lineColor = colorList[2]
+
+      if "FullDen" in lineLabel:  
+        lineColor = colorList[4]
+        lineStyle = '-'
+        linewidth = 5
+   
+      if "HighDen" in lineLabel or "HighRes" in lineLabel or \
+         "All" in lineLabel: 
+        lineStyle = "-"
+        linewidth = 5
+      if "LowDen" in lineLabel or "LowRes" in lineLabel or \
+         "1.20" in lineLabel: 
+          lineStyle = "--"
+          linewidth = 5
+     
+      plt.plot(binCentersToUse, hist, lineStyle,
+                label=lineLabel, color=lineColor,
+                linewidth=linewidth)
+
+      #if doTheory and "LowRes" in sampleDir:
+      #  popt, pcov = curve_fit(svdwFunc, binCentersToUse, hist, p0=25.)
+      #  radius, cumu_ps = svdw.getSvdW(.01, 100, 100, scaleFactor=popt[0])
+      #  cumu_ps = np.log10(cumu_ps)
+      #  print "HELLO", binCentersToUse, hist, cumu_ps
+      #  plt.plot(radius, cumu_ps, color='black', label="SVdW/%g" % popt[0])
+
+# and now the theoretical curve
+if doTheory:
+  #radius, cumu_ps = svdw.getSvdW(.01, 100, 100)
+  #cumu_ps = np.log10(cumu_ps)
+  #plt.plot(radius, cumu_ps, color='black', label="SVdW" )
+
+  iLine = 0
+  scaleFactorList = []
+  #scaleFactorList = [10, 5]
+  for scaleFactor in scaleFactorList:
+    #radius, cumu_ps = svdw.getSvdW(.01, 100, 100, scaleFactor=scaleFactor)
+    #cumu_ps = np.log10(cumu_ps)
+    #plt.plot(radius, cumu_ps, lineList[iLine], color='black', label="SVdW/%g" % scaleFactor)
+    iLine += 1
+ 
+  dvList = [-.07]
+  iLine = 0
+  for dv in dvList:
+    radius, cumu_ps = svdw.getSvdW(.01, 100, 1000, d_v=dv)
+    cumu_ps = np.log10(cumu_ps)
+    plt.plot(radius, cumu_ps, lineList[iLine], color='black', label=r"SVdW $\delta_v$=%g" % dv, linewidth=3)
+    iLine += 1
+   
+  dvList = [-.015]
+  for dv in dvList:
+    radius, cumu_ps = svdw.getSvdW(.01, 100, 1000, d_v=dv)
+    #radius, cumu_ps = svdw.getSvdW(.01, 100, 100, d_v=dv)
+    cumu_ps = np.log10(cumu_ps)
+    plt.plot(radius, cumu_ps, lineList[iLine], color='black', label="SVdW $\delta_v$=%g" % dv, linewidth=3)
+    iLine += 1
+
+plt.legend(loc = "best", fancybox=True, prop={'size':12})
+#plt.legend(title = "Samples", loc = "upper right", prop={'size':8})
+#plt.title("Number func - "+plotTitle)
 
 plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
 plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")