guilhem_lavaux/vide_public
0
0
Fork 0
mirror of https://bitbucket.org/cosmicvoids/vide_public.git synced 2025-07-05 07:41:11 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

now also compute K-S statistic among different number functions while plotting

Browse source
This commit is contained in:
P.M. Sutter 2013-02-11 08:56:20 -06:00
parent 168ef7a0d7
commit 8e1b94949d
1 changed files with 68 additions and 33 deletions
Download patch file Download diff file Expand all files Collapse all files

45
crossCompare/plotting/plotNumberFunc.py
View file

@ -11,6 +11,7 @@ import os
import matplotlib.pyplot as plt
import numpy as np
import argparse
from scipy.stats import ks_2samp
# ------------------------------------------------------------------------------
@ -19,6 +20,8 @@ plotNameBase = "compdist"
obsFudgeFactor = 1.0 # what fraction of the volume are we *reall* capturing?
#obsFudgeFactor = .66 # what fraction of the volume are we *reall* capturing?
linewidth = 1
parser = argparse.ArgumentParser(description='Plot.')
parser.add_argument('--show', dest='showPlot', action='store_const',
const=True, default=False,
@ -27,6 +30,10 @@ parser.add_argument('--parmFile', dest='parmFile', default='datasetsToPlot.py',
help='path to parameter file')
args = parser.parse_args()
nErrorBars = 10
plotMax = 120
errorBarsX = np.linspace(0, plotMax, num=nErrorBars)
# ------------------------------------------------------------------------------
filename = args.parmFile
@ -50,11 +57,14 @@ plt.clf()
plt.xlabel("Void Radius [Mpc/h]")
plt.ylabel(r"N > R [$h^3$ Gpc$^{-3}$]")
plt.yscale('log')
plt.xlim(xmax=120.)
plt.xlim(xmax=plotMax)
plotName = plotNameBase
allData = []
for dataPortion in dataPortions:
print "Data portion:", dataPortion
sizeList = []
for (iSample,sample) in enumerate(dataSampleList):
sampleName = sample.fullName
@ -85,11 +95,28 @@ for (iSample,sample) in enumerate(dataSampleList):
continue
data = data[:,4]
indices = np.arange(0, len(data), 1)
sorted = np.sort(data)
numVoids = indices[::-1]/boxVol
voidSizes = np.sort(data)
plt.plot(sorted, indices[::-1]/boxVol, '-',
label=lineTitle, color=colorList[iSample],
linewidth=linewidth)
sizeList.append(voidSizes)
myErrorBarsX = []
errorBarsY = []
errorBarsDY = []
errorBarsIdx = []
for errorBarLoc in errorBarsX:
nearestIdx = (np.abs(voidSizes-errorBarLoc)).argmin()
if nearestIdx == 0: continue
myErrorBarsX.append(errorBarLoc)
errorBarsIdx.append(nearestIdx)
errorBarsY.append(numVoids[nearestIdx])
errorBarsDY.append(np.sqrt(numVoids[nearestIdx]))
thisPlot = plt.plot(voidSizes, numVoids, '-',
color=colorList[iSample],
linewidth=linewidth, label=lineTitle)
plt.errorbar(myErrorBarsX, errorBarsY, errorBarsDY,
ecolor=colorList[iSample],
fmt=None, label='_nolegend_', capsize=0)
hist, bin_edges = np.histogram(data, bins=100, range=(0,100))
allData.append(hist)
@ -97,6 +124,14 @@ for (iSample,sample) in enumerate(dataSampleList):
plt.legend(title = "Samples", loc = "upper right", prop={'size':8})
#plt.title(plotTitle)
# compute K-S statistic for all pairs of sets
for (i,sample1) in enumerate(dataSampleList):
for (j,sample2) in enumerate(dataSampleList):
if j <= i: continue
ks, pval = ks_2samp(sizeList[i][:], sizeList[j][:])
print sample1.fullName, sample2.fullName, pval
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")