From 3523e667fe207407e3b73fe7547539e916e1cb66 Mon Sep 17 00:00:00 2001 From: "P.M. Sutter" Date: Mon, 26 May 2014 23:12:23 -0400 Subject: [PATCH] removed 2d correlation function --- .../void_python_tools/voidUtil/xcorUtil.py | 50 +------------------ 1 file changed, 2 insertions(+), 48 deletions(-) diff --git a/python_tools/void_python_tools/voidUtil/xcorUtil.py b/python_tools/void_python_tools/voidUtil/xcorUtil.py index 0fbb900..a8852ee 100644 --- a/python_tools/void_python_tools/voidUtil/xcorUtil.py +++ b/python_tools/void_python_tools/voidUtil/xcorUtil.py @@ -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