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removed 2d correlation function

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P.M. Sutter 2014-05-26 23:12:23 -04:00
parent 6f004beded
commit 3523e667fe
1 changed files with 2 additions and 48 deletions
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50
python_tools/void_python_tools/voidUtil/xcorUtil.py
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@ -15,8 +15,8 @@ def computeXcor(catalog,
# Computes and plots void-void and void-matter(galaxy) correlations
# catalog: catalog to analyze
# figDir: where to place plots
# Nmesh: number of grid cells in power spectrum calculation
# Nbin: number of grid cells in plot
# Nmesh: number of grid cells in cic mesh-interpolation
# Nbin: number of bins in final plots
# Parameters
Lbox = catalog.boxLen # Boxlength
@ -41,10 +41,6 @@ def computeXcor(catalog,
((Nm, km, Pvm, SPvm),(Nmx, rm, Xvm, SXvm)) = xcorlib.powcor(dvk, dmk, Lbox, Nbin, 'lin', True, True, 1)
((Nm, km, Pvv, SPvv),(Nmx, rm, Xvv, SXvv)) = xcorlib.powcor(dvk, dvk, Lbox, Nbin, 'lin', True, True, 1)
# 2D Power spectra & correlation functions
((Nm2d, kmper, kmpar, Pmm2d),(Nmx2d, rmper, rmpar, Xmm2d)) = xcorlib.powcor(dmk, dmk, Lbox, Nbin, 'lin', True, True, 2)
((Nm2d, kmper, kmpar, Pvm2d),(Nmx2d, rmper, rmpar, Xvm2d)) = xcorlib.powcor(dvk, dmk, Lbox, Nbin, 'lin', True, True, 2)
# Number densities
nm = np.empty(len(km))
nh = np.empty(len(km))
@ -155,46 +151,4 @@ def computeXcor(catalog,
plt.clf()
# 2D power spectra & correlation functions
kpermin = kmper.min()
kpermax = 0.3001
kparmin = kmpar.min()
kparmax = 0.3001
rpermin = rmper.min()
rpermax = 40
rparmin = rmpar.min()
rparmax = 40
for (P2d,idx,vmin,vmax) in ([Pmm2d,'mm',None,None],[Pvm2d,'vm',None,None],[Phm2d,'gm',None,None],[Pvv2d,'vv',None,2.9],[Pvh2d,'vg',None,None],[Phh2d,'gg',None,None]):
cut = np.where(kmper[:,0] <= kpermax)[0].max()+2
plt.pcolormesh(kmper[0:cut,0:cut], kmpar[0:cut,0:cut], P2d[0:cut,0:cut]/1e4, cmap=cm.Spectral_r, shading='gouraud', vmin=vmin, vmax=vmax)
plt.colorbar(format='%.1f')
plt.contour(kmper[0:cut,0:cut], kmpar[0:cut,0:cut], P2d[0:cut,0:cut]/1e4, levels=np.array(P2d.min()+(P2d.max()-P2d.min())*(np.arange(Nbin/6+1)/float(Nbin/6)))/1e4, vmin=vmin, vmax=vmax, colors='k', linewidths=0.2)
plt.xlabel(r'$k_\perp \;[h\mathrm{Mpc}^{-1}]$', fontsize=fs)
plt.ylabel(r'$k_\parallel \;[h\mathrm{Mpc}^{-1}]$', fontsize=fs)
plt.axes().set_aspect('equal')
plt.xscale('linear')
plt.yscale('linear')
plt.xlim(kpermin,kpermax)
plt.ylim(kparmin,kparmax)
plt.title(r'$P_{\mathrm{'+idx+r'}}(k_\perp, k_\parallel) \;[10^4h^{-3}\mathrm{Mpc}^3]$', fontsize=fs)
plt.savefig(figDir+'/P'+idx+'2d_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
plt.clf()
for (X2d,idx,vmin,vmax) in ([Xmm2d,'mm',None,None],[Xvm2d,'vm',None,None],[Xhm2d,'gm',None,None],[Xvv2d,'vv',None,0.2],[Xvh2d,'vg',None,None],[Xhh2d,'gg',None,None]):
cut = np.where(rmper[:,0] <= rpermax)[0].max()+3
plt.pcolormesh(rmper[0:cut,0:cut], rmpar[0:cut,0:cut], X2d[0:cut,0:cut], cmap=cm.Spectral_r, shading='gouraud', vmin=vmin, vmax=vmax)
plt.colorbar(format='%+.2f')
plt.contour(rmper[0:cut,0:cut], rmpar[0:cut,0:cut], X2d[0:cut,0:cut], levels=np.array(X2d.min()+(X2d.max()-X2d.min())*(np.arange(Nbin/6+1)/float(Nbin/6))), vmin=vmin, vmax=vmax, colors='k', linewidths=0.2)
plt.xlabel(r'$r_\perp \;[h^{-1}\mathrm{Mpc}]$', fontsize=fs)
plt.ylabel(r'$r_\parallel \;[h^{-1}\mathrm{Mpc}]$', fontsize=fs)
plt.axes().set_aspect('equal')
plt.xscale('linear')
plt.yscale('linear')
plt.xlim(rpermin,rpermax)
plt.ylim(rparmin,rparmax)
plt.title(r'$\xi_{\mathrm{'+idx+r'}}(r_\perp, r_\parallel)$', fontsize=fs)
plt.savefig(figDir+'/X'+idx+'2d_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
plt.clf()
return