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updated plotting routine to optionally show the scatter bounding many runs
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P.M. Sutter
parent
f3408eea10
commit
dcd4733236
1 changed files with 138 additions and 122 deletions
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@ -1,23 +1,4 @@
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#!/usr/bin/env python
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#+
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# VIDE -- Void IDEntification pipeline -- ./crossCompare/plotting/plotNumberFunc.py
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# Copyright (C) 2010-2013 Guilhem Lavaux
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# Copyright (C) 2011-2013 P. M. Sutter
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#
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# This program is free software; you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation; version 2 of the License.
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#
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
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#+
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# plots cumulative distributions of number counts
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@ -30,31 +11,105 @@ import os
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import matplotlib.pyplot as plt
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import numpy as np
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import argparse
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from scipy.stats import ks_2samp
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# ------------------------------------------------------------------------------
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plotNameBase = "compdist"
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obsFudgeFactor = 1.0 # what fraction of the volume are we *reall* capturing?
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#obsFudgeFactor = .66 # what fraction of the volume are we *reall* capturing?
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#obsFudgeFactor = 1.0 # what fraction of the volume are we *reall* capturing?
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obsFudgeFactor = .15 # what fraction of the volume are we *reall* capturing?
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linewidth = 1
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histBinWidth = 1 # Mpc
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parser = argparse.ArgumentParser(description='Plot.')
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parser.add_argument('--show', dest='showPlot', action='store_const',
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const=True, default=False,
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help='display the plot (default: just write eps)')
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parser.add_argument('--binned', dest='binned', action='store_const',
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const=True, default=False,
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help='plot binned function (default: cumulative)')
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parser.add_argument('--parm', dest='parm', default='datasetsToPlot.py',
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help='path to parameter file')
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parser.add_argument('--xmax', dest='xmax', default=120.,
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help='x limit of plot')
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parser.add_argument('--xmin', dest='xmin', default=20.,
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help='x limit of plot')
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args = parser.parse_args()
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nErrorBars = 10
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plotMax = 120
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errorBarsX = np.linspace(0, plotMax, num=nErrorBars)
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# ------------------------------------------------------------------------------
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def loadData(sampleDir, dataPortion):
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with open(workDir+sampleDir+"/sample_info.dat", 'rb') as input:
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sample = pickle.load(input)
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filename = workDir+"/"+sampleDir+"/centers_"+dataPortion+"_"+sample.fullName+".out"
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if not os.access(filename, os.F_OK):
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print "File not found: ", filename
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return -1, -1, -1
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data = np.loadtxt(filename, comments="#")
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if data.ndim == 1:
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print " Too few!"
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return -1, -1, -1
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data = data[:,4]
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indices = np.arange(0, len(data), 1)
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sorted = np.sort(data)
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if sample.dataType == "observation":
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boxVol = vp.getSurveyProps(sample.maskFile,
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sample.zBoundary[0], sample.zBoundary[1],
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sample.zRange[0], sample.zRange[1], "all",
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selectionFuncFile=None)[0]
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#selectionFuncFile=sample.selFunFile)[0]
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boxVol *= obsFudgeFactor
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else:
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boxVol = sample.boxLen*sample.boxLen*(sample.zBoundaryMpc[1] -
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sample.zBoundaryMpc[0])
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boxVol *= 1.e-9 # Mpc->Gpc
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indices /= boxVol
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#xmin = sorted[0]
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#xmax = sorted[-1]
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#bins = int((xmax-xmin)/histBinWidth)
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bins = args.xmax/histBinWidth
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hist, binEdges = np.histogram(sorted, bins=bins, range=(0., args.xmax))
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#hist, binEdges = np.histogram(sorted, bins=bins, range=(xmin,xmax))
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binCenters = 0.5*(binEdges[1:] + binEdges[:-1])
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if not args.binned:
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foundStart = False
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for iBin in xrange(len(hist)):
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if not foundStart and hist[iBin] == 0:
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continue
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foundStart = True
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hist[iBin] = np.sum(hist[iBin:])
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hist /= boxVol
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hist = np.log10(hist)
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lineTitle = sample.nickName
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return hist, binCenters, lineTitle
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def fill_between(x, y1, y2=0, ax=None, **kwargs):
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"""Plot filled region between `y1` and `y2`.
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This function works exactly the same as matplotlib's fill_between, except
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that it also plots a proxy artist (specifically, a rectangle of 0 size)
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so that it can be added it appears on a legend.
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"""
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ax = ax if ax is not None else plt.gca()
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ax.fill_between(x, y1, y2, interpolate=True, **kwargs)
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p = plt.Rectangle((0, 0), 0, 0, **kwargs)
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ax.add_patch(p)
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# ------------------------------------------------------------------------------
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print "Plotting number function"
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filename = args.parm
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print " Loading parameters from", filename
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if not os.access(filename, os.F_OK):
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@ -66,114 +121,75 @@ globals().update(vars(parms))
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if not os.access(figDir, os.F_OK):
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os.makedirs(figDir)
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dataSampleList = []
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for sampleDir in sampleDirList:
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with open(workDir+sampleDir+"/sample_info.dat", 'rb') as input:
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dataSampleList.append(pickle.load(input))
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plt.clf()
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plt.xlabel("Void Radius [Mpc/h]")
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plt.ylabel(r"N > R [$h^3$ Gpc$^{-3}$]")
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plt.yscale('log')
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plt.xlim(xmax=plotMax)
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plt.ylabel(r"log (N > R [$h^3$ Gpc$^{-3}$])")
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#plt.yscale('log')
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#plt.xlim(xmax=args.xmax)
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#plt.xlim(xmin=args.xmin)
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#plt.ylim(ymin=-1)
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#plt.ylim(ymax=6)
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plotName = plotNameBase
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allData = []
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plotNameBase = "numberfunc"
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plotName = plotNameBase + "_" + plotLabel
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for dataPortion in dataPortions:
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print "Data portion:", dataPortion
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sizeList = []
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for (iSample,sample) in enumerate(dataSampleList):
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for (iSample,sampleDir) in enumerate(sampleDirList):
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for dataPortion in dataPortions:
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# get all the data
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allHist = []
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if "ZZZZ" in sampleDir:
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for fileZ in fileList:
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thisSampleDir = sampleDir.replace("ZZZZ", fileZ)
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hist, binCenters, lineTitle = loadData(thisSampleDir, dataPortion)
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if lineTitle == -1: continue
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allHist.append(hist)
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lineLabel = lineTitle.replace(fileZ, "all")
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lineLabel += ", " + dataPortion
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maxHist = 1.*allHist[-1]
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minHist = 1.*allHist[-1]
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for iHist in xrange(len(allHist)-1):
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maxHist = np.maximum(maxHist, allHist[iHist])
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minHist = np.minimum(minHist, allHist[iHist])
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sampleName = sample.fullName
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lineTitle = sampleName
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if sample.dataType == "observation":
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boxVol = vp.getSurveyProps(sample.maskFile,
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sample.zBoundary[0], sample.zBoundary[1],
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sample.zRange[0], sample.zRange[1], "all",
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selectionFuncFile=None)[0]
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#selectionFuncFile=sample.selFunFile)[0]
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boxVol *= obsFudgeFactor
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trim = (maxHist > 1)
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minHist = minHist[trim]
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maxHist = maxHist[trim]
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binCentersToUse = binCenters[trim]
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if dataPortion == "central":
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hatch = '/'
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else:
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boxVol = sample.boxLen*sample.boxLen*(sample.zBoundaryMpc[1] -
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sample.zBoundaryMpc[0])
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hatch = None
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fill_between(binCentersToUse, minHist, maxHist,
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label=lineLabel, color=colorList[iSample],
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alpha=0.5, hatch=hatch
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)
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boxVol *= 1.e-9 # Mpc->Gpc
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else:
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filename = workDir+"/"+sampleDirList[iSample]+"/centers_nocut_"+dataPortion+"_"+\
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sampleName+".out"
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if not os.access(filename, os.F_OK):
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print "File not found: ", filename
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continue
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data = np.loadtxt(filename, comments="#")
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if data.ndim == 1:
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print " Too few!"
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continue
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data = data[:,4]
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indices = np.arange(0, len(data), 1)
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numVoids = indices[::-1]/boxVol
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voidSizes = np.sort(data)
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sizeList.append(voidSizes)
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myErrorBarsX = []
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errorBarsY = []
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errorBarsDY = []
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errorBarsIdx = []
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for errorBarLoc in errorBarsX:
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nearestIdx = (np.abs(voidSizes-errorBarLoc)).argmin()
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if nearestIdx == 0: continue
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myErrorBarsX.append(errorBarLoc)
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errorBarsIdx.append(nearestIdx)
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errorBarsY.append(numVoids[nearestIdx])
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errorBarsDY.append(np.sqrt(numVoids[nearestIdx]))
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thisPlot = plt.plot(voidSizes, numVoids, '-',
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color=colorList[iSample],
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linewidth=linewidth, label=lineTitle)
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plt.errorbar(myErrorBarsX, errorBarsY, errorBarsDY,
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ecolor=colorList[iSample],
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fmt=None, label='_nolegend_', capsize=0)
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hist, bin_edges = np.histogram(data, bins=40, range=(0,100))
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#allData.append(hist)
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binCenters = 0.5*(bin_edges[1:] + bin_edges[:-1])
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#plt.plot(binCenters, hist, '-',
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# label=lineTitle, color=colorList[iSample],
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# linewidth=linewidth)
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plt.legend(title = "Samples", loc = "upper right", prop={'size':8})
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#plt.title(plotTitle)
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# compute K-S statistic for all pairs of sets
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for (i,sample1) in enumerate(dataSampleList):
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for (j,sample2) in enumerate(dataSampleList):
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if j <= i: continue
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ks, pval = ks_2samp(sizeList[i][:], sizeList[j][:])
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print sample1.fullName, sample2.fullName, pval
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hist, binCenters, lineLabel = loadData(sampleDir, dataPortion)
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trim = (hist > 1)
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hist = hist[trim]
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binCentersToUse = binCenters[trim]
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if lineLabel == -1: continue
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lineLabel += ", " + dataPortion
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if dataPortion == "central":
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lineStyle = '--'
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else:
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lineStyle = '-'
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plt.plot(binCentersToUse, hist, lineStyle,
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label=lineLabel, color=colorList[iSample],
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linewidth=linewidth)
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plt.legend(title = "Samples", loc = "upper right", prop={'size':8})
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plt.title("Number func - "+plotTitle)
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plt.savefig(figDir+"/fig_"+plotName+".pdf", bbox_inches="tight")
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plt.savefig(figDir+"/fig_"+plotName+".eps", bbox_inches="tight")
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plt.savefig(figDir+"/fig_"+plotName+".png", bbox_inches="tight")
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#dataFile = figDir+"/data_"+plotName+".dat"
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#fp = open(dataFile, 'w')
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#fp.write("# R [Mpc/h], N [h^3 Gpc^-3]\n")
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#fp.write("# ")
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#for sample in dataSampleList:
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# fp.write(sample.fullName+" ")
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#fp.write("\n")
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#for i in xrange(100):
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# fp.write(str(bin_edges[i]) + " ")
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# for iSample in xrange(len(dataSampleList)):
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# fp.write(str(allData[iSample][i])+" ")
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# fp.write("\n")
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#fp.close()
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if args.showPlot:
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os.system("display %s" % figDir+"/fig_"+plotName+".png")
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