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integrated nico's cross-correlation backend and enabled plotting of the void distribution

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This commit is contained in:
P.M. Sutter 2013-03-22 08:51:45 -05:00
parent 37f8add937
commit 02f756e8a5
6 changed files with 147 additions and 5 deletions
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50
python_tools/void_python_tools/plotting/plotTools.py
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@ -18,7 +18,7 @@
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#+
__all__=['plotRedshiftDistribution', 'plotSizeDistribution', 'plot1dProfiles',
'plotMarg1d', 'plotNumberDistribution']
'plotMarg1d', 'plotNumberDistribution', 'plotVoidDistribution']
from void_python_tools.backend.classes import *
from plotDefs import *
@ -26,6 +26,7 @@ import numpy as np
import os
import pylab as plt
import void_python_tools.apTools as vp
import void_python_tools.xcor as xcor
# -----------------------------------------------------------------------------
def plotRedshiftDistribution(workDir=None, sampleList=None, figDir=None,
@ -266,7 +267,7 @@ def plotNumberDistribution(workDir=None, sampleList=None, figDir=None,
boxVol *= 1.e-9
filename = workDir+"/sample_"+sampleName+"/centers_"+dataPortion+"_"+\
filename = workDir+"/sample_"+sampleName+"/centers_nocut_"+dataPortion+"_"+\
sampleName+".out"
if not os.access(filename, os.F_OK):
print "File not found: ", filename
@ -294,5 +295,50 @@ def plotNumberDistribution(workDir=None, sampleList=None, figDir=None,
if showPlot:
os.system("display %s" % figDir+"/fig_"+plotName+".png")
# -----------------------------------------------------------------------------
def plotVoidDistribution(workDir=None, sampleList=None, figDir=None,
plotNameBase="dv",
showPlot=False, dataPortion=None, setName=None):
Nmesh = 256
for (iSample,sample) in enumerate(sampleList):
plt.clf()
plt.xlabel("x (Mpc/h)")
plt.xlabel("y (Mpc/h)")
sampleName = sample.fullName
plotName = plotNameBase+"_"+sampleName
filename = workDir+"/sample_"+sampleName+"/centers_nocut_"+dataPortion+"_"+\
sampleName+".out"
if not os.access(filename, os.F_OK):
print "File not found: ", filename
continue
void_file = open(filename,'r')
void_header1 = void_file.readline().split(",")
void_data1 = np.reshape(void_file.read().split(),
(-1,len(void_header1))).astype(np.float32)
void_file.close()
xv = void_data1[:,0:3]
rv = void_data1[:,4]
vv = void_data1[:,6]
dv, wm, ws = xcor.cic(xv, sample.boxLen, Lboxcut = 0., Nmesh = Nmesh)
plt.imshow(np.sum(dv+1,2)/Nmesh,extent=[0,sample.boxLen,0,sample.boxLen],
aspect='equal',
cmap='YlGnBu_r',interpolation='gaussian')
plt.colorbar()
plt.title("Voids: "+sampleName)
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")