zs_min = min(sn_density[:,0])
zs_max = max(sn_density[:,0])
zp_min = zs_min
zp_max = zs_max
A = loadtxt('../mu_zs2zp.txt')
nrow, ncol = A.shape
zs = linspace(zs_min, zs_max, ncol+1)
zs = (zs[1:]+zs[:-1])/2.0
muzs = zeros(zs.shape)
for i in range(len(zs)):
muzs[i] = SC.dist_mu(zs[i])
zp = linspace(zp_min, zp_max, nrow)
muzp = matmul(A,muzs)
#plot(zp, muzp, '-r', label='photo-z')
#plot(zs, muzs, '-g', label='spec-z')
#legend(loc='best')
#show()
cnt = 0
sample_zp = []
sample_muzp = []
sample_muzperr = []
SC = SNeCosmology(H0=67.7, Omega_m=0.307, Omega_k=0.0, w0=-1.0, wa=0.0, z_sn_max=1.5, grid_size=100)
NumSNe = 20000
NullzErr = 1.0
sn_z = []
sn_mu= []
sn_dmu = []
sn_zerr= []
cnt = 0
while cnt < NumSNe:
zi = get_rand_z()
zerr = 0.02*(1.+zi)*randn()*NullzErr
if zi + zerr > 0.01 and zi + zerr <=1.299:
mui = SC.dist_mu(zi)
dmui = fun_dmu(zi)
dmui = 0.12
sn_z.append(zi+zerr)
sn_mu.append(mui+dmui*randn())
sn_dmu.append(dmui)
sn_zerr.append(0.02*(1.+zi))
cnt += 1
sn_z = array(sn_z)
sn_mu = array(sn_mu)
sn_dmu = array(sn_dmu)
sn_zerr= array(sn_zerr)
savetxt('JLA_mock.txt', hstack((sn_z.reshape(NumSNe,1), sn_mu.reshape(NumSNe,1), sn_dmu.reshape(NumSNe,1), sn_zerr.reshape(NumSNe,1))), fmt='%15.4f', delimiter=' ')
