# In[38]: dndlnm_B2 = hm.dndlnM(M = masses2, ps = psfromtkcbscb2 , z = 2.,bgtype="cb",cosmo= M000s, deltac=1.686)/M000s.h**3 dndlnm_M2 = hm.dndlnM(M = masses2, ps = psfromtkcbscb2 , z = 2.,bgtype="cb",cosmo = M000s, fittingform="MICE")/M000s.h**3 print dndlnm_B2, dndlnm_M2 print np.shape(dndlnm_B2) print 1./8. # Out[38]: # 0.295094350742 0.779390315288 0.807 1.795 # [ 8.69840997e-03 5.74194925e-03 3.62316898e-03 2.18669780e-03 # 1.27152405e-03 7.13534935e-04 3.82356121e-04 1.92706374e-04 # 9.00314410e-05 3.81860774e-05 1.49713792e-05 5.08301266e-06 # 1.40307142e-06 3.45923170e-07 6.94611686e-08 1.67266650e-08] [ 8.09230756e-03 5.33340159e-03 3.36490446e-03 2.03425732e-03 # 1.18760520e-03 6.71018535e-04 3.63331163e-04 1.85832054e-04 # 8.85716560e-05 3.85895834e-05 1.56552636e-05 5.55795206e-06 # 1.62826867e-06 4.31161409e-07 9.46077053e-08 2.47736866e-08] # (16,) # 0.125 # # In[109]: plt.plot(masses, hm.dndlnM(M = masses, ps = psfromtkcbscb, cosmo = M000s)/psu.dndlnM(masses, ps = psfromtkcbscb,cosmo = M000)) plt.xscale('log') plt.ylim(0.9,1.1) print M000s.h**3
for i,rad in enumerate(Rad) : s = fs.sigmar ( Om = M000.Om0, ns = 0.963, R = rad, N=sumannorm , k = k , Tk = Tk , hubble = 0.71 ) #print rad , s sumansigmar[i] = s #print sumansigmar for i, mass in enumerate(MassesinhoverM) : sumansigmaM = fs.sigmam ( Om = M000.Om0, ns = 0.963, M= mass, N=sumannorm , k = k , Tk = Tk , hubble = 0.71 ) sumanlogsigmaM = fs.logsigm ( Om = M000.Om0 , ns = 0.963, M= mass, N = sumannorm, k = k, Tk = Tk, sigmam = sumansigmaM, hubble = 0.71 ) sumanfsigma[i] = MF.MF_fit(sumansigmaM , z = 0.) sumandndlnM [i] = sumanfsigma [i]*(sumanrhoc*M000.Om0*sumanlogsigmaM)/mass #sumandndlnM [i] = sumanfsigma [i]*(sumanlogsigmaM) sumansigmam [ i] = sumansigmaM sumandlsidlm [ i] = sumanlogsigmaM massfnpkM000 = psu.dndlnM(M= Masses , ps = psfrompk , cosmo = M000) #massfnpkM000p = psu.dndlnM(M= Masses , ps = psfrompk, cosmo = M000p) #sigma as a function of distance sigmarfig, sigmar_ax0 , sigmar_ax1 = pu.settwopanel() sigmar_ax0.plot(Rad, sumansigmar, "o",label = "Suman TF") sigmar_ax0.plot(Rad, psu.sigma(R = Rad, ps = psfrompk, cosmo = M000),"+", label = "PS") sigmar_ax0.set_ylabel(r'$\sigma (R) $') sigmar_ax0.legend(loc= "best", numpoints =1) sigmar_ax1.plot(Rad, sumansigmar/ psu.sigma(R = Rad, ps = psfrompk, cosmo = M000),"+", label = "sumanTF/PS") sigmar_ax1.set_xlabel(r'$R \/(h^{-1}Mpc) $') #sigma as a function of halo mass sigmaMfig, sigmaM_ax0 , sigmaM_ax1 = pu.settwopanel() sigmaM_ax0.plot(MassesinhoverM , sumansigmam, "o",label = 'Suman TF')