def X_testHODLHD():
''' Make sure that the powerspectra `specmulator.onlyhod.X_testHODLHD`
are sensible.
'''
# read in theta_i's of the LHD
lhd = onlyHOD.HOD_LHD(prior='sinha2017prior_narrow',
method='mdu',
samples=40)
lhd_test = onlyHOD.testHOD_LHD(prior='sinha2017prior_narrow', samples=20)
# read P(k|theta_i) for theta_i in LHD average over
# 10 realizations to make less noisy
X_pk = []
for i in range(1, 11):
k, X_pk_i = onlyHOD.X_HODLHD(i,
prior='sinha2017prior_narrow',
samples=40,
karr=True)
X_pk.append(X_pk_i)
X_pk = np.mean(X_pk, axis=0)
# read P(k|theta_i) for theta_i in LHD test sample
# average over 10 realization to make less noisy
X_pk_test = []
for i in range(1, 11):
X_pk_test_i = onlyHOD.X_testHODLHD(i,
prior='sinha2017prior_narrow',
samples=20)
X_pk_test.append(X_pk_test_i)
X_pk_test = np.mean(X_pk_test, axis=0)
fig = plt.figure(figsize=(12, 6))
sub1 = fig.add_subplot(121)
sub2 = fig.add_subplot(122)
sub1.scatter(lhd[:, 3], lhd[:, 4], c='k')
for i in range(X_pk.shape[0]):
sub2.plot(k, k * X_pk[i, :], c='k', ls='--', lw=1)
for i in range(X_pk_test.shape[0]):
sub1.scatter([lhd_test[i, 3]], [lhd_test[i, 4]])
sub2.plot(k, k * X_pk_test[i, :])
sub1.set_xlabel('log $M_1$', fontsize=20)
sub1.set_xlim([12., 14.])
sub1.set_ylabel(r'$\alpha$', fontsize=20)
sub1.set_ylim([0.5, 1.5])
sub2.set_xlabel('$k$', fontsize=20)
sub2.set_xscale('log')
sub2.set_xlim([0.01, 0.5])
sub2.set_ylabel(r'$k P_0(k|\theta_i^{LHD})$', fontsize=20)
sub2.set_yscale('log')
f = ''.join([UT.fig_dir(), 'tests/test.X_testHODLHD.png'])
fig.savefig(f, bbox_inches='tight')
plt.close()
return None
def thetaLHD():
''' ***TESTED***
Test thetaLHD using the HOD parameter priors
'''
# from Sinha et al (2017) prior
theta_lbl = [
'log $M_\mathrm{min}$', '$\sigma_{\mathrm{log}\,M}$', 'log $M_0$',
'log $M_1$', r'$\alpha$'
]
HOD_range_min = [11., 0.001, 6., 12., 0.001]
HOD_range_max = [12.2, 1., 14., 14., 2.]
samples = 17
m_list = ['maximin', 'centermaximin', 'mdu', 'nohl']
for meth in m_list:
lhcube = lhd.thetaLHD([HOD_range_min, HOD_range_max],
samples=samples,
method=meth)
fig = plt.figure(figsize=(9, 9))
for i in range(lhcube.shape[1]):
for j in range(lhcube.shape[1]):
if i < j:
sub = fig.add_subplot(lhcube.shape[1], lhcube.shape[1],
lhcube.shape[1] * j + i + 1)
sub.scatter(lhcube[:, i], lhcube[:, j])
sub.set_xlim([HOD_range_min[i], HOD_range_max[i]])
if j == lhcube.shape[1] - 1:
sub.set_xlabel(theta_lbl[i], fontsize=20)
else:
sub.set_xticks([])
sub.set_ylim([HOD_range_min[j], HOD_range_max[j]])
if i == 0: sub.set_ylabel(theta_lbl[j], fontsize=20)
else: sub.set_yticks([])
elif i == j:
sub = fig.add_subplot(lhcube.shape[1], lhcube.shape[1],
lhcube.shape[1] * j + i + 1)
sub.hist(lhcube[:, i],
range=[HOD_range_min[i], HOD_range_max[i]],
normed=True)
sub.set_xlim([HOD_range_min[i], HOD_range_max[i]])
if i != 0: sub.set_yticks([])
else: sub.set_ylabel(theta_lbl[j], fontsize=20)
if i != lhcube.shape[1] - 1: sub.set_xticks([])
else: sub.set_xlabel(theta_lbl[i], fontsize=20)
f = ''.join([
UT.fig_dir(), 'tests/test.thetaLHD.HOD.', meth, '.',
str(samples), '.samples.png'
])
fig.savefig(f, bbox_inches='tight')
return None
def theta_test(HODrange='sinha2017prior_narrow', ):
''' Test the theta_test parameters
'''
if HODrange == 'sinha2017prior_narrow': # narrow alpha range
range_descrip = "Sinha et al (2017) prior with narrow alpha range"
HOD_range_min = [11., 0.001, 6., 12., 0.5]
HOD_range_max = [12.2, 1., 14., 14., 1.5]
theta_lbl = [
'log $M_\mathrm{min}$', '$\sigma_{\mathrm{log}\,M}$', 'log $M_0$',
'log $M_1$', r'$\alpha$'
]
elif HODrange == 'sinha2017prior':
range_descrip = "Sinha et al (2017) prior"
HOD_range_min = [11., 0.001, 6., 12., 0.001]
HOD_range_max = [12.2, 1., 14., 14., 2.]
theta_lbl = [
'log $M_\mathrm{min}$', '$\sigma_{\mathrm{log}\,M}$', 'log $M_0$',
'log $M_1$', r'$\alpha$'
]
else:
raise NotImplementedError
# LHD thetas
theta_lhd = lhd.HOD_LHD(HODrange=HODrange, samples=40, method='mdu')
# test thetas
theta_test = lhd.HOD_LHD_test(HODrange=HODrange, samples=20, seed=1)
fig = plt.figure(figsize=(4 * theta_test.shape[1],
4 * theta_test.shape[1]))
for i in range(theta_test.shape[1]):
for j in range(theta_test.shape[1]):
sub = fig.add_subplot(theta_test.shape[1], theta_test.shape[1],
1 + j + theta_test.shape[1] * i)
if i != j:
sub.scatter(theta_lhd[:, j], theta_lhd[:, i], c='k')
sub.scatter(theta_test[:, j], theta_test[:, i], c='C1')
sub.set_xlim([HOD_range_min[j], HOD_range_max[j]])
sub.set_ylim([HOD_range_min[i], HOD_range_max[i]])
if i != theta_test.shape[1] - 1: sub.set_xticklabels([])
else: sub.set_xlabel(theta_lbl[j], fontsize=20)
if j != 0: sub.set_yticklabels([])
else: sub.set_ylabel(theta_lbl[i], fontsize=20)
f = ''.join([UT.fig_dir(), 'tests/theta_test.png'])
fig.savefig(f, bbox_inches='tight')
plt.close()
return None
def LHD():
''' *** TESTED ***
Testing the different methods of LHD
'''
dim = 5
samples = 17
m_list = ['maximin', 'centermaximin', 'mdu', 'nohl']
for meth in m_list:
lhcube = lhd.LHD(dim, samples=samples, method=meth, niter=1000)
fig = plt.figure(figsize=(9, 9))
for i in range(lhcube.shape[1]):
for j in range(lhcube.shape[1]):
if i < j:
sub = fig.add_subplot(lhcube.shape[1], lhcube.shape[1],
lhcube.shape[1] * j + i + 1)
sub.scatter(lhcube[:, i], lhcube[:, j])
sub.set_xlim([0., 1.])
if j == lhcube.shape[1] - 1:
sub.set_xticks([0., 0.5, 1.])
sub.set_xlabel(r'$\theta_' + str(i) + '$', fontsize=20)
else:
sub.set_xticks([])
sub.set_ylim([0., 1.])
if i == 0:
sub.set_yticks([0., 0.5, 1.])
sub.set_ylabel(r'$\theta_' + str(j) + '$', fontsize=20)
else:
sub.set_yticks([])
elif i == j:
sub = fig.add_subplot(lhcube.shape[1], lhcube.shape[1],
lhcube.shape[1] * j + i + 1)
sub.hist(lhcube[:, i], range=[0., 1], normed=True)
sub.set_xlim([0., 1.])
if i != 0:
sub.set_yticks([])
if i != lhcube.shape[1] - 1:
sub.set_xticks([])
f = ''.join([
UT.fig_dir(), 'tests/test.LHD.', meth, '.',
str(dim), 'dim.',
str(samples), '.samples.png'
])
fig.savefig(f, bbox_inches='tight')
return None
def HOD_LHD(prior='sinha2017prior_narrow', samples=17):
''' test parameter values in the LHD and the test samples
'''
hodprior = LHDhod.HODprior(prior) # HOD prior
hodrange = hodprior.range()
# parameter values of the LHD
theta_lhd = LHDhod.HOD_LHD(prior=prior,
samples=samples,
method='mdu',
overwrite=False)
assert theta_lhd.shape[0] == samples
assert theta_lhd.shape[1] == len(hodprior.labels)
# parameter values of the test sample
theta_test = LHDhod.testHOD_LHD(prior=prior, samples=20, overwrite=False)
assert theta_test.shape[1] == len(hodprior.labels)
fig = plt.figure(figsize=(4 * theta_lhd.shape[1], 4 * theta_lhd.shape[1]))
for i in range(theta_lhd.shape[1]):
for j in range(theta_lhd.shape[1]):
sub = fig.add_subplot(theta_lhd.shape[1], theta_lhd.shape[1],
1 + j + theta_lhd.shape[1] * i)
if i != j:
sub.scatter(theta_lhd[:, j], theta_lhd[:, i], c='k')
sub.scatter(theta_test[:, j], theta_test[:, i], c='C1')
sub.set_xlim(np.array(hodrange)[:, j])
sub.set_ylim(np.array(hodrange)[:, i])
if i != theta_lhd.shape[1] - 1: sub.set_xticklabels([])
else: sub.set_xlabel(hodprior.labels[j], fontsize=20)
if j != 0: sub.set_yticklabels([])
else: sub.set_ylabel(hodprior.labels[i], fontsize=20)
f = ''.join([UT.fig_dir(), 'tests/thetaHOD_lhd.png'])
fig.savefig(f, bbox_inches='tight')
plt.close()
return None