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xcorUtil now operational
This commit is contained in:
Nico Hamaus
parent
a4b942e3ae
commit
39a1a16310
1 changed files with 20 additions and 53 deletions
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@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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from matplotlib import rc
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import xcorlib
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from void_python_tools.voidUtil import getArray
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def computeXcor(catalog,
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figDir="./",
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@ -19,7 +20,7 @@ def computeXcor(catalog,
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# Nbin: number of bins in final plots
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# Parameters
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Lbox = catalog.boxLen # Boxlength
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Lbox = catalog.boxLen[0] # Boxlength
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Lboxcut = 0.
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Lbox -= 2*Lboxcut
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@ -43,24 +44,10 @@ def computeXcor(catalog,
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# Number densities
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nm = np.empty(len(km))
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nh = np.empty(len(km))
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nv = np.empty(len(km))
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nm[:] = len(xm)/Lbox**3
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nh[:] = len(xh)/Lbox**3
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nv[:] = len(xv)/Lbox**3
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# Bias
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b_hh = np.sqrt(Phh/Pmm)
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b_vv = np.sqrt(Pvv/Pmm)
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b_hm = Phm/Pmm
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b_vm = Pvm/Pmm
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b_vh = Pvh/Phh
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# Shot Noise
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sn_hh = Phh - Phm**2/Pmm
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sn_vh = Pvh - Pvm*Phm/Pmm
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sn_vv = Pvv - Pvm**2/Pmm
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# Plots
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mpl.rc('font', family='serif')
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@ -79,39 +66,26 @@ def computeXcor(catalog,
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plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
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plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
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plt.title(r'Dark matter')
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plt.savefig(figDir+'/dm_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
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plt.savefig(figDir+'/dm.eps', format='eps', bbox_inches="tight")
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plt.clf()
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plt.imshow(np.sum(dv[:,:,:]+1,2)/Nmesh,extent=[0,Lbox,0,Lbox],aspect='equal',cmap='YlGnBu_r',interpolation='gaussian')
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plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
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plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
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plt.title(r'Voids')
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plt.savefig(figDir+'/dv_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight") #, dpi=300
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plt.savefig(figDir+'/dv.eps', format='eps', bbox_inches="tight") #, dpi=300
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plt.clf()
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# Power spectra & correlation functions
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pa ,= plt.plot(km, Phh, 'r-s', ms=ms, mew=mew, mec='k')
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plt.plot(km, Phh-sn_hh, 'r--', ms=ms, mew=mew)
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plt.plot(km, sn_hh, 'r:', ms=ms, mew=mew)
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plt.fill_between(km, Phh+SPhh, abs(Phh-SPhh), color='r', alpha=0.2)
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pb ,= plt.plot(km, Phm, 'y-^', ms=ms, mew=mew, mec='k')
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plt.fill_between(km, Phm+SPhm, abs(Phm-SPhm), color='y', alpha=0.2)
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pc ,= plt.plot(km, Pmm, 'k-o', ms=0.8*ms, mew=mew, mec='k')
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plt.plot(km, Pmm-1./nm, 'k--', ms=ms, mew=mew)
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pa ,= plt.plot(km, Pmm, 'k-o', ms=0.8*ms, mew=mew, mec='k')
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#plt.plot(km, Pmm-1./nm, 'k--', ms=ms, mew=mew)
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plt.fill_between(km, Pmm+SPmm, abs(Pmm-SPmm), color='k', alpha=0.2)
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pd ,= plt.plot(km, Pvh, 'g-*', ms=1.5*ms, mew=mew, mec='k')
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plt.plot(km, -Pvh, 'g*', ms=1.5*ms, mew=mew, mec='k')
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plt.plot(km, abs(Pvh-sn_vh), 'g--', ms=ms, mew=mew)
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plt.plot(km, sn_vh, 'g:', ms=ms, mew=mew)
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plt.plot(km, -sn_vh, 'g-.', ms=ms, mew=mew)
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plt.fill_between(km, abs(Pvh+SPvh), abs(Pvh-SPvh), color='g', alpha=0.2)
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pe ,= plt.plot(km, Pvm, 'm-D', ms=ms, mew=mew, mec='k')
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pb ,= plt.plot(km, Pvm, 'm-D', ms=ms, mew=mew, mec='k')
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plt.plot(km, -Pvm, 'mD', ms=ms, mew=mew, mec='k')
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plt.fill_between(km, abs(Pvm+SPvm), abs(Pvm-SPvm), color='m', alpha=0.2)
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pf ,= plt.plot(km, Pvv, 'b-p', ms=1.3*ms, mew=mew, mec='k')
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plt.plot(km, Pvv-sn_vv, 'b--', ms=ms, mew=mew)
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plt.plot(km, sn_vv, 'b:', ms=ms, mew=mew)
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pc ,= plt.plot(km, Pvv, 'b-p', ms=1.3*ms, mew=mew, mec='k')
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#plt.plot(km, Pvv-1./nv, 'b--', ms=ms, mew=mew)
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plt.fill_between(km, Pvv+SPvv, abs(Pvv-SPvv), color='b', alpha=0.2)
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plt.xlabel(r'$k \;[h\mathrm{Mpc}^{-1}]$')
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plt.ylabel(r'$P(k) \;[h^{-3}\mathrm{Mpc}^3]$')
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@ -119,25 +93,18 @@ def computeXcor(catalog,
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plt.xscale('log')
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plt.yscale('log')
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plt.xlim(kmin,kmax)
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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)
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plt.legend([pa, pb, pc, pd, pe, pf],['gg', 'gm', 'mm', 'vg', 'vm', 'vv'],'lower left',prop={'size':12})
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plt.savefig(figDir+'/power_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
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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)
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plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12})
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plt.savefig(figDir+'/power.eps', format='eps', bbox_inches="tight")
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plt.clf()
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pa ,= plt.plot(rm, Xhh, 'r-', ms=ms, mew=mew)
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plt.fill_between(rm, abs(Xhh+SXhh), abs(Xhh-SXhh), color='r', alpha=0.2)
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pb ,= plt.plot(rm, Xhm, 'y-', ms=ms, mew=mew)
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plt.fill_between(rm, abs(Xhm+SXhm), abs(Xhm-SXhm), color='y', alpha=0.2)
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pc ,= plt.plot(rm, Xmm, 'k-', ms=ms, mew=mew)
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pa ,= plt.plot(rm, Xmm, 'k-o', ms=0.8*ms, mew=mew)
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plt.fill_between(rm, abs(Xmm+SXmm), abs(Xmm-SXmm), color='k', alpha=0.2)
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pd ,= plt.plot(rm, Xvh, 'g-', ms=ms, mew=mew)
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plt.plot(rm, -Xvh, 'g--', ms=ms, mew=mew)
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plt.fill_between(rm, abs(Xvh+SXvh), abs(Xvh-SXvh), color='g', alpha=0.2)
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pe ,= plt.plot(rm, Xvm, 'm-', ms=ms, mew=mew)
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plt.plot(rm, -Xvm, 'm--', ms=ms, mew=mew)
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pb ,= plt.plot(rm, Xvm, 'm-D', ms=ms, mew=mew)
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plt.plot(rm, -Xvm, 'mD', ms=ms, mew=mew)
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plt.fill_between(rm, abs(Xvm+SXvm), abs(Xvm-SXvm), color='m', alpha=0.2)
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pf ,= plt.plot(rm, Xvv, 'b-', ms=ms, mew=mew)
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plt.plot(rm, -Xvv, 'b--', ms=ms, mew=mew)
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pc ,= plt.plot(rm, Xvv, 'b-p', ms=1.3*ms, mew=mew)
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plt.plot(rm, -Xvv, 'bp', ms=ms, mew=1.3*mew)
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plt.fill_between(rm, abs(Xvv+SXvv), abs(Xvv-SXvv), color='b', alpha=0.2)
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plt.xlabel(r'$r \;[h^{-1}\mathrm{Mpc}]$')
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plt.ylabel(r'$\xi(r)$')
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@ -145,9 +112,9 @@ def computeXcor(catalog,
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plt.xscale('log')
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plt.yscale('log')
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plt.xlim(rmin,rmax)
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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)
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plt.legend([pa, pb, pc, pd, pe, pf],['gg', 'gm', 'mm', 'vg', 'vm', 'vv'],'best',prop={'size':12})
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plt.savefig(figDir+'/correlation_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
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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)
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plt.legend([pa, pb, pc],['tt', 'vt', 'vv'],'best',prop={'size':12})
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plt.savefig(figDir+'/correlation.eps', format='eps', bbox_inches="tight")
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plt.clf()
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