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bug fixes

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This commit is contained in:
P.M. Sutter 2014-05-09 12:01:59 -05:00
parent 4cf0ec1173
commit 39e16d41ff
6 changed files with 103 additions and 102 deletions
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128
python_tools/void_python_tools/voidUtil/plotUtil.py
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@ -43,68 +43,68 @@ def plotNumberFunction(catalogList, figDir="./",
plotName="numberfunc",
dataPortion="central"):
print "Plotting number function"
plt.clf()
plt.xlabel("$R_{eff}$ [$h^{-1}Mpc$]", fontsize=14)
plt.ylabel(r"log ($n$ (> R) [$h^3$ Gpc$^{-3}$])", fontsize=14)
for (iSample,catalog) in enumerate(catalogList):
sample = catalog.sampleInfo
data = catalog.voids[:].radius
if sample.dataType == "observation":
maskFile = sample.maskFile
boxVol = vp.getSurveyProps(maskFile,
sample.zBoundary[0], sample.zBoundary[1],
sample.zRange[0], sample.zRange[1], "all",
selectionFuncFile=None)[0]
#selectionFuncFile=sample.selFunFile)[0]
boxVol *= obsFudgeFactor
else:
boxVol = sample.boxLen*sample.boxLen*(sample.zBoundaryMpc[1] -
sample.zBoundaryMpc[0])
boxVol *= 1.e-9 # Mpc->Gpc
bins = args.xmax/5.
hist, binEdges = np.histogram(data, bins=bins, range=(0., 100.))
binCenters = 0.5*(binEdges[1:] + binEdges[:-1])
nvoids = len(data)
var = hist * (1. - hist/nvoids)
sig = np.sqrt(var)
lowerbound = hist - sig
upperbound = hist + sig
mean = np.log10(hist/boxVol)
lowerbound = np.log10(lowerbound/boxVol)
upperbound = np.log10(upperbound/boxVol)
lineColor = colorList[iSample]
lineTitle = sample.fullName
print "Plotting number function"
trim = (bounds[0] > .01)
mean = mean[trim]
binCentersToUse = binCenters[trim]
lower = lowerbound[trim]
upper = upperbound[trim]
alpha = 0.55
fill_between(binCentersToUse, lower, upper,
label=lineTitle, color=lineColor,
alpha=alpha,
)
lineStyle = '-'
plt.plot(binCentersToUse, mean, lineStyle,
color=lineColor,
linewidth=3)
plt.legend(loc = "upper right", fancybox=True, prop={'size':14})
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")
plt.clf()
plt.xlabel("$R_{eff}$ [$h^{-1}Mpc$]", fontsize=14)
plt.ylabel(r"log ($n$ (> R) [$h^3$ Gpc$^{-3}$])", fontsize=14)
for (iSample,catalog) in enumerate(catalogList):
sample = catalog.sampleInfo
data = catalog.voids[:].radius
if sample.dataType == "observation":
maskFile = sample.maskFile
boxVol = vp.getSurveyProps(maskFile,
sample.zBoundary[0], sample.zBoundary[1],
sample.zRange[0], sample.zRange[1], "all",
selectionFuncFile=None)[0]
#selectionFuncFile=sample.selFunFile)[0]
boxVol *= obsFudgeFactor
else:
boxVol = sample.boxLen*sample.boxLen*(sample.zBoundaryMpc[1] -
sample.zBoundaryMpc[0])
boxVol *= 1.e-9 # Mpc->Gpc
bins = args.xmax/5.
hist, binEdges = np.histogram(data, bins=bins, range=(0., 100.))
binCenters = 0.5*(binEdges[1:] + binEdges[:-1])
nvoids = len(data)
var = hist * (1. - hist/nvoids)
sig = np.sqrt(var)
lowerbound = hist - sig
upperbound = hist + sig
mean = np.log10(hist/boxVol)
lowerbound = np.log10(lowerbound/boxVol)
upperbound = np.log10(upperbound/boxVol)
lineColor = colorList[iSample]
lineTitle = sample.fullName
trim = (bounds[0] > .01)
mean = mean[trim]
binCentersToUse = binCenters[trim]
lower = lowerbound[trim]
upper = upperbound[trim]
alpha = 0.55
fill_between(binCentersToUse, lower, upper,
label=lineTitle, color=lineColor,
alpha=alpha,
)
lineStyle = '-'
plt.plot(binCentersToUse, mean, lineStyle,
color=lineColor,
linewidth=3)
plt.legend(loc = "upper right", fancybox=True, prop={'size':14})
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")