def plot_bf(i, args, bfpath, savepath=None):
cosmo, h, Omega_m = get_cosmo(i)
M = args['M']
params = np.loadtxt(bfpath)
colors = [
plt.get_cmap("seismic")(ci) for ci in np.linspace(1.0, 0.0, len(zs))
]
fig, ax = plt.subplots(2, sharex=True)
for j in range(len(zs)):
Mlo, Mhi, N, Mave = AD.building_box_binned_mass_function(i, j).T
edge = 10**np.concatenate((Mlo, Mhi[-1:]))
cov = AD.building_box_binned_mass_function_covariance(i, j)
err = np.sqrt(cov.diagonal())
s2 = args['sigma2s'][j]
s2t = args['sigma2tops'][j]
s2b = args['sigma2bots'][j]
d, e, f, g = model_swap(params, args, x[j])
dndM = massfunction._dndM_sigma2_precomputed(M, s2, s2t, s2b, Omega_m,
d, e, f, g)
Nmodel = massfunction.n_in_bins(edge, M, dndM) * volume
chi2 = np.dot(N - Nmodel, np.dot(np.linalg.inv(cov), N - Nmodel))
print "Box%d snap%d chi2 = %.2f" % (i, j, chi2)
ax[0].errorbar(Mave, N, err, c=colors[j], marker='.', ls='')
ax[0].loglog(Mave, Nmodel, ls='-', c=colors[j])
pd = (Nmodel - N) / Nmodel
pde = err / Nmodel
ax[1].errorbar(Mave, pd, pde, c=colors[j])
ax[0].set_ylim(1, 1e6)
yl = .11
ax[1].set_ylim(-yl, yl)
plt.subplots_adjust(hspace=0)
if savepath:
plt.gcf().savefig(savepath)
else:
plt.show()
def test_special():
sigma2 = peaks.sigma2_at_M(M, k, p, Omega_m)
Mt = M * (1 - 1e-6 * 0.5)
Mb = M * (1 + 1e-6 * 0.5)
sigma2t = peaks.sigma2_at_M(Mt, k, p, Omega_m)
sigma2b = peaks.sigma2_at_M(Mb, k, p, Omega_m)
d, e, f, g = 2.16087369917, 1.18309392312, 0.133881834517, -0.0263615354323
n = mf._dndM_sigma2_precomputed(M, sigma2, sigma2t, sigma2b, Omega_m, d, e,
f, g)
npt.assert_array_less(n[1:], n[:-1])
def lnlike(params, args):
x = args['x'] # a - 0.5
Om = args['Omega_m']
M = args['M']
s2s = args['sigma2s']
s2ts = args['sigma2tops']
s2bs = args['sigma2bots']
Ns = args['Ns'] #Number of halos in the sim
edges = args['edges'] #Msun/h
icovs = args['icovs']
LL = 0
for i in range(len(x)):
d, e, f, g = model_swap(params, args, x[i])
dndM = massfunction._dndM_sigma2_precomputed(M, s2s[i], s2ts[i],
s2bs[i], Om, d, e, f, g)
N = massfunction.n_in_bins(edges[i], M, dndM) * volume
X = Ns[i] - N
LL += np.dot(X, np.dot(icovs[i], X))
return -0.5 * LL
def test_special():
sigma2 = peaks.sigma2_at_M(M, k, p, Omega_m)
dsigma2dM = peaks.dsigma2dM_at_M(M, k, p, Omega_m)
d, e, f, g = 2.16087369917, 1.18309392312, 0.133881834517, -0.0263615354323
n = mf._dndM_sigma2_precomputed(M, sigma2, dsigma2dM, Omega_m, d, e, f, g)
npt.assert_array_less(n[1:], n[:-1])