def test_SingleFunctionSurrogate(self):
pd = surrogate.ParamDim('mass', 1., 2.)
ps = surrogate.ParamSpace('params', [pd])
t = np.linspace(0., 10., 10)
ei = np.array([np.ones(10), 1*t])
nf = []
for i in range(2):
dummy = nodeFunction.DummyNodeFunction()
nf.append(nodeFunction.NodeFunction('node_%s'%(i),
node_function=dummy))
sfs = surrogate.SingleFunctionSurrogate('s', t, ps, ei, nf)
def check(x, x_nudged, sur):
res = sur(x)
ans = x_nudged*(1 + t)
for r, a in zip(res, ans):
self.assertEqual(r, a)
def check_cases(sur):
check(1.0, 1.0 + 1.e-12, sur)
check(np.array([1.0]), 1.0 + 1.e-12, sur)
check(1.0 - 1.e-13, 1.0 + 1.e-12, sur)
check_cases(sfs)
sfs.save(TEST_FILE)
sfs2 = surrogate.SingleFunctionSurrogate()
sfs2.load(TEST_FILE)
check_cases(sfs2)
def _test_ParamSpace_nudge(self, xmins, xmaxs, tols):
params = []
for xmin, xmax, tol in zip(xmins, xmaxs, tols):
pd = surrogate.ParamDim("x", xmin, xmax, rtol=tol/(xmax - xmin))
params.append(pd)
ps = surrogate.ParamSpace("param space", params)
# Nudge all
xmin = np.array([x for x in xmins])
xmax = np.array([x for x in xmaxs])
for i in range(len(xmins)):
xmin[i] -= 0.9 * tols[i]
xmax[i] += 0.9 * tols[i]
xmin = ps.nudge_params(xmin)
xmax = ps.nudge_params(xmax)
for i in range(len(xmins)):
self.assertEqual(xmin[i], xmins[i] + tols[i])
self.assertEqual(xmax[i], xmaxs[i] - tols[i])
# Nudge one
xmin = np.array([x for x in xmins])
xmax = np.array([x for x in xmaxs])
for i in range(len(xmins)):
xmin[i] -= 0.9 * tols[i]
xmax[i] += 0.9 * tols[i]
xmin = ps.nudge_params(xmin)
xmax = ps.nudge_params(xmax)
self.assertEqual(xmin[i], xmins[i] + tols[i])
self.assertEqual(xmax[i], xmaxs[i] - tols[i])
# Check Exceptions
for i in range(len(xmins)):
xmin = np.array([x for x in xmins])
xmax = np.array([x for x in xmaxs])
xmin[i] -= 1.1*tols[i]
xmax[i] += 1.1*tols[i]
with self.assertRaises(Exception):
ps.nudge_params(xmin)
with self.assertRaises(Exception):
ps.nudge_params(xmax)
# Check unmodified
x = np.array([x + tol for x, tol in zip(xmins, tols)])
check_params = [1*x]
for i in range(len(xmins)):
x[i] += 0.5*(xmaxs[i] - xmins[i]) - tols[i]
check_params.append(1*x)
for i in range(len(xmins)):
x[i] += 0.5*(xmaxs[i] - xmins[i]) - tols[i]
check_params.append(1*x)
for x in check_params:
x2 = ps.nudge_params(x)
for xi, xi2 in zip(x, x2):
self.assertEqual(xi, xi2)
return ps
def test_ParamSpace(self):
ps = self._test_ParamSpace_nudge([0.], [1.], [1.e-4])
ps = self._test_ParamSpace_nudge([1., 2., 100.],
[1.1, 100., 101.],
[1.e-4, 1.e-5, 1.e-6])
# Test saving/loading
ps.save(TEST_FILE)
ps2 = surrogate.ParamSpace("", [])
ps2.load(TEST_FILE)
self.assertEqual(ps.name, ps2.name)
self.assertEqual(ps.dim, ps2.dim)
for i in range(ps.dim):
pd = ps._params[i]
pd2 = ps2._params[i]
self.assertEqual(pd.min_val, pd2.min_val)
self.assertEqual(pd.max_val, pd2.max_val)
self.assertEqual(pd.tol, pd2.tol)
for i in range(12)
]
cim = [
np.load('%s/NRSurrogate_cim_%s.npy' % (d, i)).reshape(*dim)
for i in range(12)
]
mode_data = {k: (ei[i], cre[i], cim[i]) for i, k in enumerate(lm_modes)}
# Make parameter space
print 'building...'
q = surrogate.ParamDim('q', 1, 2)
chiA = surrogate.ParamDim('|chiA|', 0, 0.8)
theta = surrogate.ParamDim('theta', 0, np.pi)
phi = surrogate.ParamDim('phi', 0, 2 * np.pi)
chiBz = surrogate.ParamDim('chiB_z', -.8, .8)
param_space = surrogate.ParamSpace('4d2s', [q, chiA, theta, phi, chiBz])
knots = []
for xmin, xmax in zip(param_space.min_vals(), param_space.max_vals()):
knots.append(np.linspace(xmin, xmax, 5))
# Build surrogate
sur = surrogate.FastTensorSplineSurrogate(
name='%s%s' % (save_prefix, n),
domain=t,
param_space=param_space,
knot_vecs=knots,
mode_data=mode_data,
modes=lm_modes,
)
# Test evaluation
f = h5py.File('../surrogates/SpEC_q1_10_NoSpin_nu5thDegPoly_exclude_2_0.h5')
lm_modes = [(int(k[1]), int(k[4])) for k in f.keys()]
mode_data = {}
for (ell, m) in lm_modes:
print 'loading (%s, %s)...' % (ell, m)
g = f['l%s_m%s' % (ell, m)]
name = 'mode_%s_%s' % (ell, m)
mode_data[(ell, m)] = get_mode_surrogate_data(g, name)
t = np.arange(-2750.0, 100.00001, 0.1)
param_dim_q = surrogate.ParamDim('q', 1, 10)
param_space = surrogate.ParamSpace('Nonspinning_q_1_10', [param_dim_q])
sur = surrogate.MultiModalSurrogate(
name='SpEC_q1_10_nonspinning_surrogate',
domain=t,
param_space=param_space,
mode_data=mode_data,
mode_type='amp_phase',
modes=lm_modes,
)
print 'done making surrogate, testing...'
res = sur(1.1)
res2 = sur(9.9)
print 'saving...'
sur.save('new_SpEC_sur.h5')