# 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')