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vide_public/crossCompare/plotting/plotDenMaps.py
Guilhem Lavaux 29723fff06 updated copyright notice
2014-04-28 08:52:27 +02:00

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#+
# VIDE -- Void IDentification and Examination -- ./crossCompare/plotting/plotDenMaps.py
# Copyright (C) 2010-2014 Guilhem Lavaux
# Copyright (C) 2011-2014 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
#+
# VIDE -- Void IDentification and Examination -- ./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!"
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