diff --git a/python_tools/void_python_tools/voidUtil/xcorUtil.py b/python_tools/void_python_tools/voidUtil/xcorUtil.py index a8852ee..a870955 100644 --- a/python_tools/void_python_tools/voidUtil/xcorUtil.py +++ b/python_tools/void_python_tools/voidUtil/xcorUtil.py @@ -5,6 +5,7 @@ import matplotlib.pyplot as plt import matplotlib.cm as cm from matplotlib import rc import xcorlib +from void_python_tools.voidUtil import getArray def computeXcor(catalog, figDir="./", @@ -19,7 +20,7 @@ def computeXcor(catalog, # Nbin: number of bins in final plots # Parameters - Lbox = catalog.boxLen # Boxlength + Lbox = catalog.boxLen[0] # Boxlength Lboxcut = 0. Lbox -= 2*Lboxcut @@ -43,24 +44,10 @@ def computeXcor(catalog, # Number densities nm = np.empty(len(km)) - nh = np.empty(len(km)) nv = np.empty(len(km)) nm[:] = len(xm)/Lbox**3 - nh[:] = len(xh)/Lbox**3 nv[:] = len(xv)/Lbox**3 - # Bias - b_hh = np.sqrt(Phh/Pmm) - b_vv = np.sqrt(Pvv/Pmm) - b_hm = Phm/Pmm - b_vm = Pvm/Pmm - b_vh = Pvh/Phh - - # Shot Noise - sn_hh = Phh - Phm**2/Pmm - sn_vh = Pvh - Pvm*Phm/Pmm - sn_vv = Pvv - Pvm**2/Pmm - # Plots mpl.rc('font', family='serif') @@ -79,39 +66,26 @@ def computeXcor(catalog, plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$') plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$') plt.title(r'Dark matter') - plt.savefig(figDir+'/dm_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight") + plt.savefig(figDir+'/dm.eps', format='eps', bbox_inches="tight") plt.clf() plt.imshow(np.sum(dv[:,:,:]+1,2)/Nmesh,extent=[0,Lbox,0,Lbox],aspect='equal',cmap='YlGnBu_r',interpolation='gaussian') plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$') plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$') plt.title(r'Voids') - plt.savefig(figDir+'/dv_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight") #, dpi=300 + plt.savefig(figDir+'/dv.eps', format='eps', bbox_inches="tight") #, dpi=300 plt.clf() # Power spectra & correlation functions - pa ,= plt.plot(km, Phh, 'r-s', ms=ms, mew=mew, mec='k') - plt.plot(km, Phh-sn_hh, 'r--', ms=ms, mew=mew) - plt.plot(km, sn_hh, 'r:', ms=ms, mew=mew) - plt.fill_between(km, Phh+SPhh, abs(Phh-SPhh), color='r', alpha=0.2) - pb ,= plt.plot(km, Phm, 'y-^', ms=ms, mew=mew, mec='k') - plt.fill_between(km, Phm+SPhm, abs(Phm-SPhm), color='y', alpha=0.2) - pc ,= plt.plot(km, Pmm, 'k-o', ms=0.8*ms, mew=mew, mec='k') - plt.plot(km, Pmm-1./nm, 'k--', ms=ms, mew=mew) + pa ,= plt.plot(km, Pmm, 'k-o', ms=0.8*ms, mew=mew, mec='k') + #plt.plot(km, Pmm-1./nm, 'k--', ms=ms, mew=mew) plt.fill_between(km, Pmm+SPmm, abs(Pmm-SPmm), color='k', alpha=0.2) - pd ,= plt.plot(km, Pvh, 'g-*', ms=1.5*ms, mew=mew, mec='k') - plt.plot(km, -Pvh, 'g*', ms=1.5*ms, mew=mew, mec='k') - plt.plot(km, abs(Pvh-sn_vh), 'g--', ms=ms, mew=mew) - plt.plot(km, sn_vh, 'g:', ms=ms, mew=mew) - plt.plot(km, -sn_vh, 'g-.', ms=ms, mew=mew) - plt.fill_between(km, abs(Pvh+SPvh), abs(Pvh-SPvh), color='g', alpha=0.2) - pe ,= plt.plot(km, Pvm, 'm-D', ms=ms, mew=mew, mec='k') + pb ,= plt.plot(km, Pvm, 'm-D', ms=ms, mew=mew, mec='k') plt.plot(km, -Pvm, 'mD', ms=ms, mew=mew, mec='k') plt.fill_between(km, abs(Pvm+SPvm), abs(Pvm-SPvm), color='m', alpha=0.2) - pf ,= plt.plot(km, Pvv, 'b-p', ms=1.3*ms, mew=mew, mec='k') - plt.plot(km, Pvv-sn_vv, 'b--', ms=ms, mew=mew) - plt.plot(km, sn_vv, 'b:', ms=ms, mew=mew) + pc ,= plt.plot(km, Pvv, 'b-p', ms=1.3*ms, mew=mew, mec='k') + #plt.plot(km, Pvv-1./nv, 'b--', ms=ms, mew=mew) plt.fill_between(km, Pvv+SPvv, abs(Pvv-SPvv), color='b', alpha=0.2) plt.xlabel(r'$k \;[h\mathrm{Mpc}^{-1}]$') plt.ylabel(r'$P(k) \;[h^{-3}\mathrm{Mpc}^3]$') @@ -119,25 +93,18 @@ def computeXcor(catalog, plt.xscale('log') plt.yscale('log') plt.xlim(kmin,kmax) - plt.ylim(10**np.floor(np.log10(abs(Pvh).min()))/margin, max(10**np.ceil(np.log10(Phh.max())),10**np.ceil(np.log10(Pvv.max())))*margin) - plt.legend([pa, pb, pc, pd, pe, pf],['gg', 'gm', 'mm', 'vg', 'vm', 'vv'],'lower left',prop={'size':12}) - plt.savefig(figDir+'/power_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight") + plt.ylim(10**np.floor(np.log10(abs(Pvm[1:]).min()))/margin, max(10**np.ceil(np.log10(Pmm.max())),10**np.ceil(np.log10(Pvv.max())))*margin) + plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12}) + plt.savefig(figDir+'/power.eps', format='eps', bbox_inches="tight") plt.clf() - pa ,= plt.plot(rm, Xhh, 'r-', ms=ms, mew=mew) - plt.fill_between(rm, abs(Xhh+SXhh), abs(Xhh-SXhh), color='r', alpha=0.2) - pb ,= plt.plot(rm, Xhm, 'y-', ms=ms, mew=mew) - plt.fill_between(rm, abs(Xhm+SXhm), abs(Xhm-SXhm), color='y', alpha=0.2) - pc ,= plt.plot(rm, Xmm, 'k-', ms=ms, mew=mew) + pa ,= plt.plot(rm, Xmm, 'k-o', ms=0.8*ms, mew=mew) plt.fill_between(rm, abs(Xmm+SXmm), abs(Xmm-SXmm), color='k', alpha=0.2) - pd ,= plt.plot(rm, Xvh, 'g-', ms=ms, mew=mew) - plt.plot(rm, -Xvh, 'g--', ms=ms, mew=mew) - plt.fill_between(rm, abs(Xvh+SXvh), abs(Xvh-SXvh), color='g', alpha=0.2) - pe ,= plt.plot(rm, Xvm, 'm-', ms=ms, mew=mew) - plt.plot(rm, -Xvm, 'm--', ms=ms, mew=mew) + pb ,= plt.plot(rm, Xvm, 'm-D', ms=ms, mew=mew) + plt.plot(rm, -Xvm, 'mD', ms=ms, mew=mew) plt.fill_between(rm, abs(Xvm+SXvm), abs(Xvm-SXvm), color='m', alpha=0.2) - pf ,= plt.plot(rm, Xvv, 'b-', ms=ms, mew=mew) - plt.plot(rm, -Xvv, 'b--', ms=ms, mew=mew) + pc ,= plt.plot(rm, Xvv, 'b-p', ms=1.3*ms, mew=mew) + plt.plot(rm, -Xvv, 'bp', ms=ms, mew=1.3*mew) plt.fill_between(rm, abs(Xvv+SXvv), abs(Xvv-SXvv), color='b', alpha=0.2) plt.xlabel(r'$r \;[h^{-1}\mathrm{Mpc}]$') plt.ylabel(r'$\xi(r)$') @@ -145,9 +112,9 @@ def computeXcor(catalog, plt.xscale('log') plt.yscale('log') plt.xlim(rmin,rmax) - plt.ylim(10**np.floor(np.log10(abs(Xvh).min()))/margin, max(10**np.ceil(np.log10(Xhh.max())),10**np.ceil(np.log10(Xvv.max())))*margin) - plt.legend([pa, pb, pc, pd, pe, pf],['gg', 'gm', 'mm', 'vg', 'vm', 'vv'],'best',prop={'size':12}) - plt.savefig(figDir+'/correlation_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight") + plt.ylim(min(10**np.floor(np.log10(abs(Xvm).min())),10**np.floor(np.log10(abs(Xmm).min())))/margin, max(10**np.ceil(np.log10(Xmm.max())),10**np.ceil(np.log10(Xvv.max())))*margin) + plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12}) + plt.savefig(figDir+'/correlation.eps', format='eps', bbox_inches="tight") plt.clf()