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Merged in python3 (pull request #5)
Port to python3, large code cleanup * Fixed command line for cosmotool * Fix path * Dump command line is log file * Fix important typo * Modify paths for example * Fix path again * Use an explicit constructor * Change file to open (python 2->3) * python3 fix for xrange in periodic_kdtree.py * Fixed index for Np, numPart, numZones, numZonesTot, partID, zoneID in catalogUtil.py
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298
python_tools/vide/voidUtil/plotUtil.py
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298
python_tools/vide/voidUtil/plotUtil.py
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#+
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# VIDE -- Void IDentification and Examination -- ./python_tools/vide/voidUtil/plotUtil.py
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# Copyright (C) 2010-2014 Guilhem Lavaux
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# Copyright (C) 2011-2014 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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__all__=['plotNumberFunction','plotEllipDist','plotVoidCells']
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from vide.backend.classes import *
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from .plotDefs import *
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import numpy as np
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import os
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import pylab as plt
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import vide.apTools as vp
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from vide.voidUtil import getArray, shiftPart, getVoidPart
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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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def plotNumberFunction(catalogList,
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figDir="./",
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plotName="numberfunc",
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cumulative=True,
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binWidth=1):
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# plots a cumulative number function
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# catalogList: list of void catalogs to plot
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# figDir: output directory for figures
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# plotName: name to prefix to all outputs
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# cumulative: if True, plots cumulative number function
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# binWidth: width of histogram bins in Mpc/h
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# returns:
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# ellipDistList: array of len(catalogList),
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# each element has array of size bins, number, +/- 1 sigma
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print("Plotting number function")
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catalogList = np.atleast_1d(catalogList)
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plt.clf()
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plt.xlabel("$R_{eff}$ [$h^{-1}Mpc$]", fontsize=14)
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if cumulative:
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plt.ylabel(r"log ($n$ (> R) [$h^3$ Gpc$^{-3}$])", fontsize=14)
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else:
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plt.ylabel(r"log ($dn/dR$ [$h^3$ Gpc$^{-3}$])", fontsize=14)
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ellipDistList = []
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for (iSample,catalog) in enumerate(catalogList):
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sample = catalog.sampleInfo
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data = getArray(catalog.voids, 'radius')
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if sample.dataType == "observation":
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maskFile = sample.maskFile
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boxVol = vp.getSurveyProps(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=sample.selFunFile)[0]
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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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bins = 100./binWidth
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hist, binEdges = np.histogram(data, bins=bins, range=(0., 100.))
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binCenters = 0.5*(binEdges[1:] + binEdges[:-1])
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if cumulative:
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foundStart = False
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for iBin in range(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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nvoids = len(data)
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var = hist * (1. - hist/nvoids)
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sig = np.sqrt(var)
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lowerbound = hist - sig
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upperbound = hist + sig
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mean = np.log10(hist/boxVol)
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lowerbound = np.log10(lowerbound/boxVol)
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upperbound = np.log10(upperbound/boxVol)
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lineColor = colorList[iSample]
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lineTitle = sample.fullName
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trim = (lowerbound > .01)
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mean = mean[trim]
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binCentersToUse = binCenters[trim]
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lower = lowerbound[trim]
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upper = upperbound[trim]
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alpha = 0.55
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fill_between(binCentersToUse, lower, upper,
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label=lineTitle, color=lineColor,
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alpha=alpha,
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)
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lineStyle = '-'
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plt.plot(binCentersToUse, mean, lineStyle,
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color=lineColor,
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linewidth=3)
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ellipDistList.append((binCentersToUse, mean, lower, upper))
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plt.legend(loc = "upper right", fancybox=True, prop={'size':14})
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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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return ellipDistList
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# -----------------------------------------------------------------------------
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def plotEllipDist(catalogList,
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figDir="./",
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plotName="ellipdist"):
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# plots ellipticity distributions
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# catalogList: list of void catalogs to plot
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# figDir: output directory for figures
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# plotName: name to prefix to all outputs
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# returns:
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# ellipDistList: array of len(catalogList),
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# each element has array of ellipticity distributions
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print("Plotting ellipticity distributions")
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plt.clf()
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plt.xlabel(r"Ellipticity $\epsilon$", fontsize=14)
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plt.ylabel(r"P($\epsilon$)", fontsize=14)
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ellipDistList = []
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catalogList = np.atleast_1d(catalogList)
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for (iSample,catalog) in enumerate(catalogList):
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sample = catalog.sampleInfo
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data = getArray(catalog.voids, 'ellipticity')
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dataWeights = np.ones_like(data)/len(data)
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dataHist, dataBins = np.histogram(data, bins=10, weights=dataWeights,
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range=(0.0,0.35))
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binCenters = 0.5*(dataBins[1:] + dataBins[:-1])
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plt.plot(binCenters, dataHist, label=sample.fullName,
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color=colorList[iSample])
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ellipDistList.append((dataBins, dataHist,))
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plt.legend(loc = "upper right", fancybox=True, prop={'size':14})
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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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return
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# -----------------------------------------------------------------------------
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def plotVoidCells(catalog,
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voidID,
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figDir="./",
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plotName="cells",
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plotDensity=True,
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sliceWidth=250):
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# plots the particles belonging to a void
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# catalog: void catalog
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# voidID: ID of void to plot
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# figDir: output directory for figures
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# plotName: name to prefix to all outputs
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# sliceWidth: width of slice in Mpc/h
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sample = catalog.sampleInfo
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periodicLine = getPeriodic(sample)
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plt.clf()
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iVoid = -1
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for i in range(len(catalog.voids)):
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if catalog.voids[i].voidID == voidID:
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iVoid = i
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break
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if iVoid == -1:
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print("Void ID %d not found!" % voidID)
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return
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sliceCenter = catalog.voids[iVoid].macrocenter
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xwidth = sliceWidth
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ywidth = sliceWidth
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zwidth = max(sliceWidth/4., 50)
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xmin = -xwidth/2.
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xmax = xwidth/2.
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ymin = -ywidth/2.
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ymax = ywidth/2.
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zmin = -zwidth/2.
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zmax = zwidth/2.
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#Slice Particles
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if plotDensity:
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part = catalog.partPos
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part = shiftPart(part, sliceCenter, periodicLine, catalog.ranges)
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filter = (part[:,0] > xmin) & (part[:,0] < xmax) & \
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(part[:,1] > ymin) & (part[:,1] < ymax) & \
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(part[:,2] > zmin) & (part[:,2] < zmax)
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part = part[filter]
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extent = [xmin, xmax, ymin, ymax]
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hist, xedges, yedges = np.histogram2d(part[:,0], part[:,1], normed=False,
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bins=64)
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hist = np.log10(hist+1)
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plt.imshow(hist,
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aspect='equal',
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extent=extent,
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interpolation='gaussian',
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cmap='YlGnBu_r')
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# overlay voids as circles
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fig = plt.gcf()
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voidPart = getVoidPart(catalog, voidID)
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newpart = np.zeros((len(voidPart),3))
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volume=np.zeros(len(voidPart))
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radius=np.zeros(len(voidPart))
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newpart[:,0] = getArray(voidPart, 'x')
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newpart[:,1] = getArray(voidPart, 'y')
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newpart[:,2] = getArray(voidPart, 'z')
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volume = getArray(voidPart, 'volume')
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radius = (3.*volume/(4.*np.pi))**(1./3.)
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shiftedPartVoid =shiftPart(newpart,sliceCenter, periodicLine, catalog.ranges)
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#Limiting plotted cells to cells into the slice
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#Possibility to only plot bigger cells (through cellsradiuslim)
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cellsMinlimz = zmin
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cellsMaxlimz = zmax
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cellsradiuslim = 0.0
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for p in range(len(volume)):
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if (shiftedPartVoid[p,2]>(cellsMinlimz) and \
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shiftedPartVoid[p,2]<(cellsMaxlimz) and \
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radius[p]>cellsradiuslim):
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color = 'blue'
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circle = plt.Circle((shiftedPartVoid[p,0], \
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shiftedPartVoid[p,1]), \
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radius[p],
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alpha =.2, fc=color,edgecolor=None,linewidth=1)
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fig.gca().add_artist(circle)
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title="cells"+str(voidID)
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plt.title(title, fontsize=20)
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plotName="cells"+str(voidID)
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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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return
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