mirror of
https://bitbucket.org/cosmicvoids/vide_public.git
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121 lines
3.6 KiB
Python
Executable file
121 lines
3.6 KiB
Python
Executable file
#!/usr/bin/env python
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# plots cumulative distributions of number counts
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from void_python_tools.backend import *
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from void_python_tools.plotting import *
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import void_python_tools.apTools as vp
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import imp
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import pickle
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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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# ------------------------------------------------------------------------------
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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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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('--parmFile', dest='parmFile', default='datasetsToPlot.py',
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help='path to parameter file')
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args = parser.parse_args()
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# ------------------------------------------------------------------------------
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filename = args.parmFile
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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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print " Cannot find parameter file %s!" % filename
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exit(-1)
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parms = imp.load_source("name", filename)
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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=120.)
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plotName = plotNameBase
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allData = []
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for (iSample,sample) in enumerate(dataSampleList):
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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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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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filename = workDir+"/"+sampleDirList[iSample]+"/centers_"+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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sorted = np.sort(data)
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plt.plot(sorted, indices[::-1]/boxVol, '-',
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label=lineTitle, color=colorList[iSample],
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linewidth=linewidth)
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hist, bin_edges = np.histogram(data, bins=100, range=(0,100))
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allData.append(hist)
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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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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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