def get_eq_from_eig(m):
''' get the equilibrium frequencies from the matrix. the eq freqs are the left eigenvector corresponding to eigenvalue of 0.
Code here is largely taken from Bloom. See here - https://github.com/jbloom/phyloExpCM/blob/master/src/submatrix.py, specifically in the fxn StationaryStates
'''
(w, v) = linalg.eig(m, left=True, right=False)
max_i = 0
max_w = w[max_i]
for i in range(1, len(w)):
if w[i] > max_w:
max_w = w[i]
max_i = i
assert( abs(max_w) < ZERO ), "Maximum eigenvalue is not close to zero."
max_v = v[:,max_i]
max_v /= np.sum(max_v)
eq_freqs = max_v.real # these are the stationary frequencies
# SOME SANITY CHECKS
assert np.allclose(np.zeros(61), np.dot(eq_freqs, m)) # should be true since eigenvalue of zero
pi_inv = np.diag(1.0 / eq_freqs)
s = np.dot(m, pi_inv)
assert np.allclose(m, np.dot(s, np.diag(eq_freqs)), atol=ZERO, rtol=1e-5), "exchangeability and equilibrium does not recover matrix"
# And for some impressive overkill, double check pi_i*q_ij = pi_j*q_ji
for i in range(61):
pi_i = eq_freqs[i]
for j in range(61):
pi_j = eq_freqs[j]
forward = pi_i * m[i][j]
backward = pi_j * m[j][i]
assert(abs(forward - backward) < ZERO), "Detailed balance violated."
return eq_freqs
def test_scal(vector_array):
v = vector_array
for ind in valid_inds(v):
if v.len_ind(ind) != v.len_ind_unique(ind):
with pytest.raises(Exception):
c = v.copy()
c[ind].scal(1.)
continue
ind_complement_ = ind_complement(v, ind)
c = v.copy()
c[ind].scal(1.)
assert len(c) == len(v)
assert np.all(almost_equal(c, v))
c = v.copy()
c[ind].scal(0.)
assert np.all(almost_equal(c[ind], v.zeros(v.len_ind(ind))))
assert np.all(almost_equal(c[ind_complement_], v[ind_complement_]))
for x in (1., 1.4, np.random.random(v.len_ind(ind))):
c = v.copy()
c[ind].scal(x)
assert np.all(almost_equal(c[ind_complement_], v[ind_complement_]))
assert np.allclose(c[ind].sup_norm(), v[ind].sup_norm() * abs(x))
assert np.allclose(c[ind].l2_norm(), v[ind].l2_norm() * abs(x))
if hasattr(v, 'data'):
y = v.data.copy()
if NUMPY_INDEX_QUIRK and len(y) == 0:
pass
else:
if isinstance(x, np.ndarray) and not isinstance(ind, Number):
x = x[:, np.newaxis]
y[ind] *= x
assert np.allclose(c.data, y)
def test_pitch_contour():
# Generate some random pitch
fs = 8000
times = np.linspace(0, 5, num=5 * fs, endpoint=True)
noise = scipy.ndimage.gaussian_filter1d(np.random.randn(len(times)),
sigma=256)
freqs = 440.0 * 2.0**(16 * noise)
# negate a bunch of sequences
idx = np.unique(np.random.randint(0, high=len(times), size=32))
for start, end in zip(idx[::2], idx[1::2]):
freqs[start:end] *= -1
# Test with inferring duration
x = mir_eval.sonify.pitch_contour(times, freqs, fs)
assert len(x) == fs * 5
# Test with an explicit duration
# This forces the interpolator to go off the end of the sampling grid,
# which should result in a constant sequence in the output
x = mir_eval.sonify.pitch_contour(times, freqs, fs, length=fs * 7)
assert len(x) == fs * 7
assert np.allclose(x[-fs * 2:], x[-fs * 2])
# Test with an explicit duration and a fixed offset
# This forces the interpolator to go off the beginning of
# the sampling grid, which should result in a constant output
x = mir_eval.sonify.pitch_contour(times + 5.0, freqs, fs, length=fs * 7)
assert len(x) == fs * 7
assert np.allclose(x[:fs * 5], x[0])
def test_cache_key(self):
def fn(x):
return x ** 2
f = Gradient(fn)
self.assertTrue(np.allclose(f(3), 0.0))
self.assertTrue(np.allclose(f(3.0), 6.0))
def _validate_covars(covars, cvtype, nmix, n_dim):
from scipy import linalg
if cvtype == 'spherical':
if len(covars) != nmix:
raise ValueError("'spherical' covars must have length nmix")
elif np.any(covars <= 0):
raise ValueError("'spherical' covars must be non-negative")
elif cvtype == 'tied':
if covars.shape != (n_dim, n_dim):
raise ValueError("'tied' covars must have shape (n_dim, n_dim)")
elif (not np.allclose(covars, covars.T)
or np.any(linalg.eigvalsh(covars) <= 0)):
raise ValueError("'tied' covars must be symmetric, "
"positive-definite")
elif cvtype == 'diag':
if covars.shape != (nmix, n_dim):
raise ValueError("'diag' covars must have shape (nmix, n_dim)")
elif np.any(covars <= 0):
raise ValueError("'diag' covars must be non-negative")
elif cvtype == 'full':
if covars.shape != (nmix, n_dim, n_dim):
raise ValueError("'full' covars must have shape "
"(nmix, n_dim, n_dim)")
for n, cv in enumerate(covars):
if (not np.allclose(cv, cv.T)
or np.any(linalg.eigvalsh(cv) <= 0)):
raise ValueError("component %d of 'full' covars must be "
"symmetric, positive-definite" % n)
def __unit_test_onset_function(metric):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
# First, test for a warning on empty onsets
metric(np.array([]), np.arange(10))
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
assert str(w[-1].message) == "Reference onsets are empty."
metric(np.arange(10), np.array([]))
assert len(w) == 2
assert issubclass(w[-1].category, UserWarning)
assert str(w[-1].message) == "Estimated onsets are empty."
# And that the metric is 0
assert np.allclose(metric(np.array([]), np.array([])), 0)
# Now test validation function - onsets must be 1d ndarray
onsets = np.array([[1., 2.]])
nose.tools.assert_raises(ValueError, metric, onsets, onsets)
# onsets must be in seconds (so not huge)
onsets = np.array([1e10, 1e11])
nose.tools.assert_raises(ValueError, metric, onsets, onsets)
# onsets must be sorted
onsets = np.array([2., 1.])
nose.tools.assert_raises(ValueError, metric, onsets, onsets)
# Valid onsets which are the same produce a score of 1 for all metrics
onsets = np.arange(10, dtype=np.float)
assert np.allclose(metric(onsets, onsets), 1)
def order_segments(segments):
'''Piece the segments together in order, return a list of vertices
'''
segments = [np.array(seg).tolist() for seg in segments]
if not segments:
return
verts = [segments[0][0], segments[0][1]]
original = range(1, len(segments))
while original:
match = False
for ind, segment in enumerate(segments):
if not ind in original:
continue
pt1, pt2 = segment
if np.allclose(pt1, verts[-1]):
verts.append(pt2)
original.remove(ind)
match = True
elif np.allclose(pt2, verts[-1]):
verts.append(pt1)
original.remove(ind)
match = True
if not match:
verts.append(verts[0])
return np.array(verts)
return np.array(verts)
def __init__(self, x, y):
if len(x.shape) == 2:
x_row = x[0]
assert np.allclose(x_row, x)
x = x_row
else:
assert len(x.shape) == 1
if len(y.shape) == 2:
y_col = y[:, 0]
assert np.allclose(y_col, y.T)
y = y_col
else:
assert len(y.shape) == 1
self.nx = len(x)
self.ny = len(y)
self.dx = x[1] - x[0]
self.dy = y[1] - y[0]
self.x_origin = x[0]
self.y_origin = y[0]
self.width = x[-1] - x[0]
self.height = y[-1] - y[0]
def testFull(self, num_best=None, shardsize=100):
if self.cls == similarities.Similarity:
index = self.cls(None, corpus, num_features=len(dictionary), shardsize=shardsize)
else:
index = self.cls(corpus, num_features=len(dictionary))
if isinstance(index, similarities.MatrixSimilarity):
expected = numpy.array([
[0.57735026, 0.57735026, 0.57735026, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.40824831, 0.0, 0.40824831, 0.40824831, 0.40824831, 0.40824831, 0.40824831, 0.0, 0.0, 0.0, 0.0],
[0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.5, 0.5, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.40824831, 0.0, 0.0, 0.0, 0.81649661, 0.0, 0.40824831, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.57735026, 0.57735026, 0.0, 0.0, 0.57735026, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1., 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.70710677, 0.70710677, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.57735026, 0.57735026, 0.57735026],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.57735026, 0.0, 0.0, 0.0, 0.0, 0.57735026, 0.57735026],
], dtype=numpy.float32)
# HACK: dictionary can be in different order, so compare in sorted order
self.assertTrue(numpy.allclose(sorted(expected.flat), sorted(index.index.flat)))
index.num_best = num_best
query = corpus[0]
sims = index[query]
expected = [(0, 0.99999994), (2, 0.28867513), (3, 0.23570226), (1, 0.23570226)][ : num_best]
# convert sims to full numpy arrays, so we can use allclose() and ignore
# ordering of items with the same similarity value
expected = matutils.sparse2full(expected, len(index))
if num_best is not None: # when num_best is None, sims is already a numpy array
sims = matutils.sparse2full(sims, len(index))
self.assertTrue(numpy.allclose(expected, sims))
if self.cls == similarities.Similarity:
index.destroy()
def test_distmod():
# WMAP7 but with Omega_relativisitic = 0
tcos = core.FlatLambdaCDM(70.4, 0.272, Tcmb0=0.0)
core.set_current(tcos)
assert np.allclose(tcos.distmod([1, 5]).value, [44.124857, 48.40167258])
assert np.allclose(funcs.distmod([1, 5], cosmo=tcos).value,
[44.124857, 48.40167258])
def test_massivenu_density():
# Testing neutrino density calculation
# Simple test cosmology, where we compare rho_nu and rho_gamma
# against the exact formula (eq 24/25 of Komatsu et al. 2011)
# computed using Mathematica. The approximation we use for f(y)
# is only good to ~ 0.5% (with some redshift dependence), so that's
# what we test to.
ztest = np.array([0.0, 1.0, 2.0, 10.0, 1000.0])
nuprefac = 7.0 / 8.0 * (4.0 / 11.0) ** (4.0 / 3.0)
# First try 3 massive neutrinos, all 100 eV -- note this is a universe
# seriously dominated by neutrinos!
tcos = core.FlatLambdaCDM(75.0, 0.25, Tcmb0=3.0, Neff=3,
m_nu=u.Quantity(100.0, u.eV))
assert tcos.has_massive_nu
assert tcos.Neff == 3
nurel_exp = nuprefac * tcos.Neff * np.array([171969, 85984.5, 57323,
15633.5, 171.801])
assert np.allclose(tcos.nu_relative_density(ztest), nurel_exp, rtol=5e-3)
assert np.allclose(tcos.efunc([0.0, 1.0]), [1.0, 7.46144727668], rtol=5e-3)
# Next, slightly less massive
tcos = core.FlatLambdaCDM(75.0, 0.25, Tcmb0=3.0, Neff=3,
m_nu=u.Quantity(0.25, u.eV))
nurel_exp = nuprefac * tcos.Neff * np.array([429.924, 214.964, 143.312,
39.1005, 1.11086])
assert np.allclose(tcos.nu_relative_density(ztest), nurel_exp,
rtol=5e-3)
# For this one also test Onu directly
onu_exp = np.array([0.01890217, 0.05244681, 0.0638236,
0.06999286, 0.1344951])
assert np.allclose(tcos.Onu(ztest), onu_exp, rtol=5e-3)
# And fairly light
tcos = core.FlatLambdaCDM(80.0, 0.30, Tcmb0=3.0, Neff=3,
m_nu=u.Quantity(0.01, u.eV))
nurel_exp = nuprefac * tcos.Neff * np.array([17.2347, 8.67345, 5.84348,
1.90671, 1.00021])
assert np.allclose(tcos.nu_relative_density(ztest), nurel_exp,
rtol=5e-3)
onu_exp = np.array([0.00066599, 0.00172677, 0.0020732,
0.00268404, 0.0978313])
assert np.allclose(tcos.Onu(ztest), onu_exp, rtol=5e-3)
assert np.allclose(tcos.efunc([1.0, 2.0]), [1.76225893, 2.97022048],
rtol=1e-4)
assert np.allclose(tcos.inv_efunc([1.0, 2.0]), [0.5674535, 0.33667534],
rtol=1e-4)
# Now a mixture of neutrino masses, with non-integer Neff
tcos = core.FlatLambdaCDM(80.0, 0.30, Tcmb0=3.0, Neff=3.04,
m_nu=u.Quantity([0.0, 0.01, 0.25], u.eV))
nurel_exp = nuprefac * tcos.Neff * np.array([149.386233, 74.87915, 50.0518,
14.002403, 1.03702333])
assert np.allclose(tcos.nu_relative_density(ztest), nurel_exp,
rtol=5e-3)
onu_exp = np.array([0.00584959, 0.01493142, 0.01772291,
0.01963451, 0.10227728])
assert np.allclose(tcos.Onu(ztest), onu_exp, rtol=5e-3)
def test_tcmb():
cosmo = core.FlatLambdaCDM(70.4, 0.272, Tcmb0=3.0)
assert np.allclose(cosmo.Tcmb0.value, 3.0)
assert np.allclose(cosmo.Tcmb(2).value, 9.0)
z = [0.0, 1.0, 2.0, 3.0, 9.0]
assert np.allclose(cosmo.Tcmb(z).value,
[3.0, 6.0, 9.0, 12.0, 30.0], rtol=1e-6)
def test_tnu():
cosmo = core.FlatLambdaCDM(70.4, 0.272, Tcmb0=3.0)
assert np.allclose(cosmo.Tnu0.value, 2.1412975665108247, rtol=1e-6)
assert np.allclose(cosmo.Tnu(2).value, 6.423892699532474, rtol=1e-6)
z = [0.0, 1.0, 2.0, 3.0]
assert np.allclose(cosmo.Tnu(z), [2.14129757, 4.28259513,
6.4238927, 8.56519027], rtol=1e-6)
def test_return_internal_type(self):
dtype = self.dtype
if dtype is None:
dtype = theano.config.floatX
rng = np.random.RandomState(utt.fetch_seed())
x = np.asarray(rng.uniform(0, 1, [2, 4]), dtype=dtype)
x = self.cast_value(x)
x_ref = self.ref_fct(x)
x_shared = self.shared_constructor(x, borrow=False)
total = self.theano_fct(x_shared)
total_func = theano.function([], total)
# in this case we can alias with the internal value
x = x_shared.get_value(borrow=True, return_internal_type=True)
assert self.test_internal_type(x)
x /= .5
# this is not required by the contract but it is a feature we can
# implement for some type of SharedVariable.
assert np.allclose(self.ref_fct(x), total_func())
x = x_shared.get_value(borrow=False, return_internal_type=True)
assert self.test_internal_type(x)
assert x is not x_shared.container.value
x /= .5
# this is required by the contract
assert not np.allclose(self.ref_fct(x), total_func())
def test_shared_do_alias(self):
dtype = self.dtype
if dtype is None:
dtype = theano.config.floatX
rng = np.random.RandomState(utt.fetch_seed())
x = np.asarray(rng.uniform(1, 2, [4, 2]), dtype=dtype)
x = self.cast_value(x)
x_ref = self.ref_fct(x)
x_shared = self.shared_constructor(x, borrow=True)
total = self.theano_fct(x_shared)
total_func = theano.function([], total)
total_val = total_func()
assert np.allclose(self.ref_fct(x), total_val)
x /= .5
# not required by the contract but it is a feature we've implemented
if self.shared_borrow_true_alias:
assert np.allclose(self.ref_fct(x), total_func())
else:
assert np.allclose(x_ref, total_func())
def test_voja_modulate(Simulator, nl_nodirect, seed):
"""Tests that voja's rule can be modulated on/off."""
n = 200
learned_vector = np.asarray([0.5])
def control_signal(t):
"""Modulates the learning on/off."""
return 0 if t < 0.5 else -1
m = nengo.Network(seed=seed)
with m:
m.config[nengo.Ensemble].neuron_type = nl_nodirect()
control = nengo.Node(output=control_signal)
u = nengo.Node(output=learned_vector)
x = nengo.Ensemble(n, dimensions=len(learned_vector))
conn = nengo.Connection(
u, x, synapse=None, learning_rule_type=Voja(None))
nengo.Connection(control, conn.learning_rule, synapse=None)
p_enc = nengo.Probe(conn.learning_rule, 'scaled_encoders')
with Simulator(m) as sim:
sim.run(1.0)
tend = sim.trange() > 0.5
# Check that encoders stop changing after 0.5s
assert np.allclose(sim.data[p_enc][tend], sim.data[p_enc][-1])
# Check that encoders changed during first 0.5s
i = np.where(tend)[0][0] # first time point after changeover
assert not np.allclose(sim.data[p_enc][0], sim.data[p_enc][i])
def _test_pes(Simulator, nl, plt, seed,
pre_neurons=False, post_neurons=False, weight_solver=False,
vin=np.array([0.5, -0.5]), vout=None, n=200,
function=None, transform=np.array(1.), rate=1e-3):
vout = np.array(vin) if vout is None else vout
model = nengo.Network(seed=seed)
with model:
model.config[nengo.Ensemble].neuron_type = nl()
u = nengo.Node(output=vin)
v = nengo.Node(output=vout)
a = nengo.Ensemble(n, dimensions=u.size_out)
b = nengo.Ensemble(n, dimensions=u.size_out)
e = nengo.Ensemble(n, dimensions=v.size_out)
nengo.Connection(u, a)
bslice = b[:v.size_out] if v.size_out < u.size_out else b
pre = a.neurons if pre_neurons else a
post = b.neurons if post_neurons else bslice
conn = nengo.Connection(pre, post,
function=function, transform=transform,
learning_rule_type=PES(rate))
if weight_solver:
conn.solver = nengo.solvers.LstsqL2(weights=True)
nengo.Connection(v, e, transform=-1)
nengo.Connection(bslice, e)
nengo.Connection(e, conn.learning_rule)
b_p = nengo.Probe(bslice, synapse=0.03)
e_p = nengo.Probe(e, synapse=0.03)
weights_p = nengo.Probe(conn, 'weights', sample_every=0.01)
corr_p = nengo.Probe(conn.learning_rule, 'correction', synapse=0.03)
with Simulator(model) as sim:
sim.run(0.5)
t = sim.trange()
weights = sim.data[weights_p]
plt.subplot(311)
plt.plot(t, sim.data[b_p])
plt.ylabel("Post decoded value")
plt.subplot(312)
plt.plot(t, sim.data[e_p])
plt.ylabel("Error decoded value")
plt.subplot(313)
plt.plot(t, sim.data[corr_p] / rate)
plt.ylabel("PES correction")
plt.xlabel("Time (s)")
tend = t > 0.4
assert np.allclose(sim.data[b_p][tend], vout, atol=0.05)
assert np.allclose(sim.data[e_p][tend], 0, atol=0.05)
assert np.allclose(sim.data[corr_p][tend] / rate, 0, atol=0.05)
assert not np.allclose(weights[0], weights[-1], atol=1e-5)
def test_dt_dependence(Simulator, plt, learning_rule, seed, rng):
"""Learning rules should work the same regardless of dt."""
m, activity_p, trans_p = learning_net(
learning_rule, nengo.Network(seed=seed), rng)
trans_data = []
# Using dts greater near tau_ref (0.002 by default) causes learning to
# differ due to lowered presynaptic firing rate
dts = (0.0001, 0.001)
colors = ('b', 'g', 'r')
for c, dt in zip(colors, dts):
with Simulator(m, dt=dt) as sim:
sim.run(0.1)
trans_data.append(sim.data[trans_p])
plt.subplot(2, 1, 1)
plt.plot(sim.trange(dt=0.01), sim.data[trans_p][..., 0], c=c)
plt.subplot(2, 1, 2)
plt.plot(sim.trange(), sim.data[activity_p], c=c)
plt.subplot(2, 1, 1)
plt.xlim(right=sim.trange()[-1])
plt.ylabel("Connection weight")
plt.subplot(2, 1, 2)
plt.xlim(right=sim.trange()[-1])
plt.ylabel("Presynaptic activity")
assert np.allclose(trans_data[0], trans_data[1], atol=3e-3)
assert not np.allclose(sim.data[trans_p][0], sim.data[trans_p][-1])
def test_reset(Simulator, learning_rule, plt, seed, rng):
"""Make sure resetting learning rules resets all state."""
m, activity_p, trans_p = learning_net(
learning_rule, nengo.Network(seed=seed), rng)
with Simulator(m) as sim:
sim.run(0.1)
sim.run(0.2)
first_t = sim.trange()
first_t_trans = sim.trange(dt=0.01)
first_activity_p = np.array(sim.data[activity_p], copy=True)
first_trans_p = np.array(sim.data[trans_p], copy=True)
sim.reset()
sim.run(0.3)
plt.subplot(2, 1, 1)
plt.ylabel("Neural activity")
plt.plot(first_t, first_activity_p, c='b')
plt.plot(sim.trange(), sim.data[activity_p], c='g')
plt.subplot(2, 1, 2)
plt.ylabel("Connection weight")
plt.plot(first_t_trans, first_trans_p[..., 0], c='b')
plt.plot(sim.trange(dt=0.01), sim.data[trans_p][..., 0], c='g')
assert np.allclose(sim.trange(), first_t)
assert np.allclose(sim.trange(dt=0.01), first_t_trans)
assert np.allclose(sim.data[activity_p], first_activity_p)
assert np.allclose(sim.data[trans_p], first_trans_p)
def test_bprop(self):
r = []
context = Context()
for i in xrange(self.N):
a = self.get_random_array()
a_gpu = Connector(GpuMatrix.from_npa(a, 'float'), bu_device_id=context)
vpooling_block = MeanPoolingBlock(a_gpu, axis=0)
voutput, dL_dvoutput = vpooling_block.output.register_usage(context, context)
_dL_voutput = self.get_random_array((dL_dvoutput.nrows, dL_dvoutput.ncols))
GpuMatrix.from_npa(_dL_voutput, 'float').copy_to(context, dL_dvoutput)
hpooling_block = MeanPoolingBlock(a_gpu, axis=1)
houtput, dL_dhoutput = hpooling_block.output.register_usage(context, context)
_dL_houtput = self.get_random_array((dL_dhoutput.nrows, dL_dhoutput.ncols))
GpuMatrix.from_npa(_dL_houtput, 'float').copy_to(context, dL_dhoutput)
vpooling_block.fprop()
vpooling_block.bprop()
dL_dmatrix = vpooling_block.dL_dmatrix.to_host()
r.append(np.allclose(dL_dmatrix,
np.repeat(_dL_voutput/a.shape[0], a.shape[0], 0),
atol=1e-6))
hpooling_block.fprop()
hpooling_block.bprop()
hpooling_block.dL_dmatrix.to_host()
dL_dmatrix = hpooling_block.dL_dmatrix.to_host()
r.append(np.allclose(dL_dmatrix,
np.repeat(_dL_houtput/a.shape[1], a.shape[1], 1),
atol=1e-6))
self.assertEqual(sum(r), 2 * self.N)
def test_outer_v(self):
# Check that the augmentation vectors behave as expected
outer_v = []
x0, count_0 = do_solve(outer_k=6, outer_v=outer_v)
assert_(len(outer_v) > 0)
assert_(len(outer_v) <= 6)
x1, count_1 = do_solve(outer_k=6, outer_v=outer_v)
assert_(count_1 == 2, count_1)
assert_(count_1 < count_0/2)
assert_(allclose(x1, x0, rtol=1e-14))
# ---
outer_v = []
x0, count_0 = do_solve(outer_k=6, outer_v=outer_v, store_outer_Av=False)
assert_(array([v[1] is None for v in outer_v]).all())
assert_(len(outer_v) > 0)
assert_(len(outer_v) <= 6)
x1, count_1 = do_solve(outer_k=6, outer_v=outer_v)
assert_(count_1 == 3, count_1)
assert_(count_1 < count_0/2)
assert_(allclose(x1, x0, rtol=1e-14))
def test_warp_reproject_dst_bounds(runner, tmpdir):
"""--bounds option works."""
srcname = 'tests/data/shade.tif'
outputname = str(tmpdir.join('test.tif'))
out_bounds = [-106.45036, 39.6138, -106.44136, 39.6278]
result = runner.invoke(
main_group, [
'warp', srcname, outputname, '--dst-crs', 'EPSG:4326',
'--res', 0.001, '--bounds'] + out_bounds)
assert result.exit_code == 0
assert os.path.exists(outputname)
with rasterio.open(outputname) as output:
assert output.crs == {'init': 'epsg:4326'}
assert np.allclose(output.bounds[0::3],
[-106.45036, 39.6278])
assert np.allclose([0.001, 0.001],
[output.transform.a, -output.transform.e])
# XXX: an extra row and column is produced in the dataset
# because we're using ceil instead of floor internally.
# Not necessarily a bug, but may change in the future.
assert np.allclose([output.bounds[2] - 0.001, output.bounds[1] + 0.001],
[-106.44136, 39.6138])
assert output.width == 10
assert output.height == 15
def testSymmetry(self):
"""Verify that the projection is symmetrical about the equator
"""
for minDec in (-5.0, -1.0, 0.5):
maxDec = minDec + 2.0
config = EquatSkyMap.ConfigClass()
config.decRange = minDec, maxDec
skyMap = EquatSkyMap(config)
for tractInfo in skyMap[0:1]:
numPatches = tractInfo.getNumPatches()
midXIndex = numPatches[0] / 2
minPixelPosList = []
maxPixelPosList = []
maxYInd = numPatches[1] - 1
for xInd in (0, midXIndex, numPatches[0] - 1):
minDecPatchInfo = tractInfo.getPatchInfo((xInd,0))
minDecPosBox = afwGeom.Box2D(minDecPatchInfo.getOuterBBox())
minPixelPosList += [
minDecPosBox.getMin(),
afwGeom.Point2D(minDecPosBox.getMaxX(), minDecPosBox.getMinY()),
]
maxDecPatchInfo = tractInfo.getPatchInfo((xInd, maxYInd))
maxDecPosBox = afwGeom.Box2D(maxDecPatchInfo.getOuterBBox())
maxPixelPosList += [
maxDecPosBox.getMax(),
afwGeom.Point2D(maxDecPosBox.getMinX(), maxDecPosBox.getMaxY()),
]
wcs = tractInfo.getWcs()
minDecList = [wcs.pixelToSky(pos).getPosition(afwGeom.degrees)[1] for pos in minPixelPosList]
maxDecList = [wcs.pixelToSky(pos).getPosition(afwGeom.degrees)[1] for pos in maxPixelPosList]
self.assertTrue(numpy.allclose(minDecList, minDecList[0]))
self.assertTrue(numpy.allclose(maxDecList, maxDecList[0]))
self.assertTrue(minDecList[0] <= minDec)
self.assertTrue(maxDecList[0] >= maxDec)
def test_connected(Simulator):
m = nengo.Network(label='test_connected', seed=123)
with m:
input = nengo.Node(output=lambda t: np.sin(t), label='input')
output = nengo.Node(output=lambda t, x: np.square(x),
size_in=1,
label='output')
nengo.Connection(input, output, synapse=None) # Direct connection
p_in = nengo.Probe(input, 'output')
p_out = nengo.Probe(output, 'output')
sim = Simulator(m)
runtime = 0.5
sim.run(runtime)
with Plotter(Simulator) as plt:
t = sim.trange()
plt.plot(t, sim.data[p_in], label='sin')
plt.plot(t, sim.data[p_out], label='sin squared')
plt.plot(t, np.sin(t), label='ideal sin')
plt.plot(t, np.sin(t) ** 2, label='ideal squared')
plt.legend(loc='best')
plt.savefig('test_node.test_connected.pdf')
plt.close()
sim_t = sim.trange()
sim_sin = sim.data[p_in].ravel()
sim_sq = sim.data[p_out].ravel()
t = 0.001 * np.arange(len(sim_t))
assert np.allclose(sim_t, t)
# 1-step delay
assert np.allclose(sim_sin[1:], np.sin(t[:-1]))
assert np.allclose(sim_sq[1:], sim_sin[:-1] ** 2)
def test_normalization():
"""Test that `match_template` gives the correct normalization.
Normalization gives 1 for a perfect match and -1 for an inverted-match.
This test adds positive and negative squares to a zero-array and matches
the array with a positive template.
"""
n = 5
N = 20
ipos, jpos = (2, 3)
ineg, jneg = (12, 11)
image = np.full((N, N), 0.5)
image[ipos:ipos + n, jpos:jpos + n] = 1
image[ineg:ineg + n, jneg:jneg + n] = 0
# white square with a black border
template = np.zeros((n + 2, n + 2))
template[1:1 + n, 1:1 + n] = 1
result = match_template(image, template)
# get the max and min results.
sorted_result = np.argsort(result.flat)
iflat_min = sorted_result[0]
iflat_max = sorted_result[-1]
min_result = np.unravel_index(iflat_min, result.shape)
max_result = np.unravel_index(iflat_max, result.shape)
# shift result by 1 because of template border
assert np.all((np.array(min_result) + 1) == (ineg, jneg))
assert np.all((np.array(max_result) + 1) == (ipos, jpos))
assert np.allclose(result.flat[iflat_min], -1)
assert np.allclose(result.flat[iflat_max], 1)
def test_constant_scalar(Simulator, nl, plt, seed):
"""A Network that represents a constant value."""
N = 30
val = 0.5
m = nengo.Network(label='test_constant_scalar', seed=seed)
with m:
m.config[nengo.Ensemble].neuron_type = nl()
input = nengo.Node(output=val, label='input')
A = nengo.Ensemble(N, 1)
nengo.Connection(input, A)
in_p = nengo.Probe(input, 'output')
A_p = nengo.Probe(A, 'decoded_output', synapse=0.1)
sim = Simulator(m, dt=0.001)
sim.run(1.0)
t = sim.trange()
plt.plot(t, sim.data[in_p], label='Input')
plt.plot(t, sim.data[A_p], label='Neuron approximation, pstc=0.1')
plt.ylim([0, 1.05 * val])
plt.legend(loc=0)
assert np.allclose(sim.data[in_p], val, atol=.1, rtol=.01)
assert np.allclose(sim.data[A_p][-10:], val, atol=.1, rtol=.01)
def isSkewSymmetric(A):
"""
Returns True if input matrix is skew-symmetric.
Parameter
---------
A : array-like
The input matrix.
Returns
-------
isSkewSymmetric : bool
Returns True if the matrix is skew-symmetric; False otherwise.
See Also
--------
isSquare, isSymmetric, isUpperTriangular, isLowerTriangular, isDiagonal
"""
assert isSquare(A), 'Input matrix should be a square matrix.'
if np.allclose(A, -1*A.T) and np.allclose(A.diagonal(),0):
return True
else:
return False
def test_warp_no_reproject_bounds_res(runner, tmpdir):
srcname = 'tests/data/shade.tif'
outputname = str(tmpdir.join('test.tif'))
out_bounds = [-11850000, 4810000, -11849000, 4812000]
result = runner.invoke(main_group, [
'warp', srcname, outputname, '--res', 30, '--bounds'] + out_bounds)
assert result.exit_code == 0
assert os.path.exists(outputname)
with rasterio.open(srcname) as src:
with rasterio.open(outputname) as output:
assert output.crs == src.crs
assert np.allclose(output.bounds, out_bounds)
assert np.allclose([30, 30], [output.transform.a, -output.transform.e])
assert output.width == 34
assert output.height == 67
# dst-bounds should be an alias to bounds
outputname = str(tmpdir.join('test2.tif'))
out_bounds = [-11850000, 4810000, -11849000, 4812000]
result = runner.invoke(main_group, [
'warp', srcname, outputname, '--res', 30, '--dst-bounds'] + out_bounds)
assert result.exit_code == 0
assert os.path.exists(outputname)
with rasterio.open(srcname) as src:
with rasterio.open(outputname) as output:
assert np.allclose(output.bounds, out_bounds)
def test_hv(self):
def fn(x):
return np.dot(x, x).sum()
x = np.ones((3, 3))
F = HessianVector(fn)
self.assertTrue(np.allclose(x * 6, F(x, vectors=x)))
self.assertTrue(np.allclose(x * 2, F(x[0], vectors=x[0])))
def svd_example():
a = np.floor(np.random.rand(4, 4)*20-6)
logger.info("Matrix A:\n %s", a)
b = np.floor(np.random.rand(4, 1)*20-6)
logger.info("Matrix B:\n %s", b)
u, s, v_t = np.linalg.svd(a) # SVD decomposition of A
logger.info("Matrix U:\n %s", u)
logger.info("Matrix S:\n %s", s)
logger.info("Matrix V(transpose:\n %s", u)
logger.info("Computing inverse using linalg.pinv")
# Computing the inverse using pinv
inv_pinv = np.linalg.pinv(a)
logger.info("pinv:\n %s", inv_pinv)
# Computing inverse using matrix decomposition
logger.info("Computing inverse using svd matrix decomposition")
inv_svd = np.dot(np.dot(v_t.T, np.linalg.inv(np.diag(s))), u.T)
logger.info("svd inverse:\n %s", inv_svd)
logger.info("comparing the results from pinv and svd_inverse:\n %s",
np.allclose(inv_pinv, inv_svd))
logger.info("Sol1: Solving x using pinv matrix... x=A^-1 x b")
result_pinv_x = np.dot(inv_pinv, b)
logger.info("Sol2: Solving x using svd_inverse matrix... x=A^-1 x b")
result_svd_x = np.dot(inv_svd, b)
if not np.allclose(result_pinv_x, result_svd_x):
raise ValueError('Should have been True')
def test_slerp_datum(self):
lon0, lat0 = (183, 0)
lon1, lat1 = (179, 0)
res = slerp(lon0, lat0, lon1, lat1, 0.5)
res %= 360
self.assertTrue(np.allclose(res, (181, 0)))
def test_notasglove(self):
words = "inception monkey earlgrey"
pred = self.emb.predict([self.emb * x for x in words.split()])
gpred = self.glove.predict([self.glove * x for x in words.split()])
self.assertFalse(np.allclose(pred, gpred))
def test_sameasbase(self):
words = "inception monkey earlgrey"
pred = self.emb.predict([self.emb * x for x in words.split()])
gpred = self.baseemb.predict([self.baseemb * x for x in words.split()])
self.assertTrue(np.allclose(pred, gpred))
def test_sameasglove(self):
words = "key the a his"
pred = self.emb.predict([self.emb * x for x in words.split()])
gpred = self.glove.predict([self.glove * x for x in words.split()])
self.assertTrue(np.allclose(pred, gpred))
def test_notasbase(self):
words = "key the a his"
pred = self.emb.predict([self.emb * x for x in words.split()])
gpred = self.baseemb.predict([self.baseemb * x for x in words.split()])
self.assertFalse(np.allclose(pred, gpred))
def run_mapping():
basepath = os.path.normpath(os.getcwd())
configpath = os.path.join(basepath, 'inputs')
print "os.getcwd()", os.getcwd()
globals2 = {}
locals2 = {}
fname = "inputs.user_inputs"
command_module = __import__("inputs.user_inputs", globals2, locals2, ['*'])
required_inputs = {
'xref' : None,
'nastran_call' : None,
}
for key in required_inputs:
if key not in command_module.__dict__:
msg = 'fname=%r doesnt contain %r' % (fname, key)
value = command_module.__dict__[key]
required_inputs[key] = value
#print key, value
nastran_call = required_inputs['nastran_call']
xref = required_inputs['xref']
print "nastran_call = %r" % nastran_call
#print "globals2 =", globals2
print "xref =", xref
#print "locals2 =", locals2
#print "globals()", globals()
asfd
workpath = os.path.join(basepath, 'outputsFinal')
# load mapping
cart3dLoads = os.path.join(workpath, 'Cart3d_35000_0.825_10_0_0_0_0.i.triq')
bdfModel = os.path.join(configpath,'aeroModel_mod.bdf')
bdfModelOut = os.path.join(workpath, 'fem_loads_3.bdf')
# mappingMatrix.new.out - stored in workpath
# deflection mapping
cart3dGeom = os.path.join(configpath, 'Cart3d_bwb.i.tri')
cart3dGeom2 = os.path.join(workpath, 'Components.i.tri')
bdf = os.path.join(workpath, 'fem3.bdf')
op2 = os.path.join(workpath, 'fem3.op2')
f06 = os.path.join(workpath, 'fem3.f06')
assert os.path.exists(bdf), '%r doesnt exist' % bdf
assert os.path.exists(bdfModel), '%r doesnt exist' % bdfModel
assert os.path.exists(cart3dGeom), '%r doesnt exist' % cart3dGeom
log.info("basepath = %s" % basepath)
os.chdir(workpath)
copyFile(cart3dGeom, 'Components.i.tri')
nodeList = [20037, 21140, 21787, 21028, 1151, 1886, 2018, 1477, 1023, 1116, 1201, 1116, 1201, 1828, 2589, 1373, 1315, 1571, 1507, 1532, 1317, 1327, 2011, 1445, 2352, 1564, 1878, 1402, 1196, 1234, 1252, 1679, 1926, 1274, 2060, 2365, 21486, 20018, 20890, 20035, 1393, 2350, 1487, 1530, 1698, 1782]
outfile = open('convergeDeflections.out', 'ab')
maxADeflectionOld = 0.
nIterations = 30
iCart = 1
for i in range(1, nIterations):
strI = '_' + str(i)
assert os.path.exists('Components.i.tri')
#if i==iCart:
if 0:
# run cart3d
log.info("---running Cart3d #%s---" % i)
sys.stdout.flush()
failFlag = os.system('./COMMAND > command.out') # runs cart3d.i.tri, makes Components.i.triq
assert failFlag == 0, 'Cart3d ./COMMAND failed on iteration #%s' % i
moveFile('Components.i.triq', cart3dLoads)
copyFile(cart3dLoads, cart3dLoads+strI)
copyFile('forces.dat', 'forces.dat' + strI)
copyFile('moments.dat', 'moments.dat' + strI)
copyFile('loadsCC.dat', 'loadsCC.dat' + strI)
copyFile('history.dat', 'history.dat' + strI)
os.remove('Components.i.tri') # verifies new Components.i.tri gets created
sys.stdout.flush()
# map deflections
run_map_loads(cart3dLoads, bdfModel, bdfModelOut) # maps loads
copyFile(bdfModelOut, bdfModelOut + strI)
# run nastran
log.info("---running Nastran #%s---" % i)
sys.stdout.flush()
#failFlag = os.system('nastran scr=yes bat=no fem3.bdf') # runs fem3.bdf with fem_loads_3.bdf
#assert failFlag == 0,'nastran failed on iteration #%s' % i
copyFile('fem3.op2', 'fem3.op2' + strI)
copyFile('fem3.f06', 'fem3.f06' + strI)
os.remove(bdfModelOut) # cleans up fem_loads.bdf
# map deflections
(wA, wS) = run_map_deflections(nodeList, bdf, op2, cart3dGeom, cart3dGeom2, log=log)
assert os.path.exists('Components.i.tri')
os.remove(op2) # verifies new fem3.op2 was created
os.remove(f06) # verifies new fem3.f06 was created
# post-processing
(maxAID, maxADeflection) = maxDict(wA)
maxSID = '???'
maxADeflection = wA[maxAID]
maxSDeflection = max(wS)[0,0]
log.info( "AERO - i=%s maxAID=%s maxADeflection=%s" %(i, maxAID, maxADeflection))
log.info( "STRUCTURE - i=%s maxSID=%s maxSDeflection=%s" %(i, maxSID, maxSDeflection))
outfile.write("AERO - i=%s maxAID=%s maxADeflection=%s\n" %(i, maxAID, maxADeflection))
outfile.write("STRUCTURE - i=%s maxSID=%s maxSDeflection=%s\n" %(i, maxSID, maxSDeflection))
msg = '\n'+'*'*80+'\n'
msg += 'finished iteration #%s\n' %(i)
msg += '*'*80+'\n'
log.info(msg)
if allclose(maxADeflection, maxADeflectionOld, atol=0.001):
break
maxADeflectionOld = copy.deepcopy(maxADeflection)
iCart += 1
sys.stdout.flush()
outfile.close()
log.info('---finished runMapping.py---')
def _run_interface(self, runtime):
# Load image, orient as RAS
fname = self.inputs.in_file
orig_img = nb.load(fname)
reoriented = nb.as_closest_canonical(orig_img)
# Set target shape information
target_zooms = np.array(self.inputs.target_zooms)
target_shape = np.array(self.inputs.target_shape)
target_span = target_shape * target_zooms
zooms = np.array(reoriented.header.get_zooms()[:3])
shape = np.array(reoriented.shape[:3])
xyz_unit = reoriented.header.get_xyzt_units()[0]
if xyz_unit == 'unknown':
# Common assumption; if we're wrong, unlikely to be the only thing that breaks
xyz_unit = 'mm'
# Set a 0.05mm threshold to performing rescaling
atol = {'meter': 1e-5, 'mm': 0.01, 'micron': 10}[xyz_unit]
# Rescale => change zooms
# Resize => update image dimensions
rescale = not np.allclose(zooms, target_zooms, atol=atol)
resize = not np.all(shape == target_shape)
if rescale or resize:
target_affine = np.eye(4, dtype=reoriented.affine.dtype)
if rescale:
scale_factor = target_zooms / zooms
target_affine[:3, :3] = reoriented.affine[:3, :3].dot(
np.diag(scale_factor))
else:
target_affine[:3, :3] = reoriented.affine[:3, :3]
if resize:
# The shift is applied after scaling.
# Use a proportional shift to maintain relative position in dataset
size_factor = target_span / (zooms * shape)
# Use integer shifts to avoid unnecessary interpolation
offset = (reoriented.affine[:3, 3] * size_factor -
reoriented.affine[:3, 3])
target_affine[:3,
3] = reoriented.affine[:3,
3] + offset.astype(int)
else:
target_affine[:3, 3] = reoriented.affine[:3, 3]
data = nli.resample_img(reoriented, target_affine,
target_shape).get_data()
reoriented = reoriented.__class__(data, target_affine,
reoriented.header)
# Image may be reoriented, rescaled, and/or resized
if reoriented is not orig_img:
out_name = fname_presuffix(fname,
suffix='_ras',
newpath=runtime.cwd)
reoriented.to_filename(out_name)
else:
out_name = fname
self._results['out_file'] = out_name
return runtime
def _make_graph(self):
self.logger.info("Generating training graph on {} GPUs ...".format(self.cfg.nr_gpus))
weights_initializer = slim.xavier_initializer()
biases_initializer = tf.constant_initializer(0.)
biases_regularizer = tf.no_regularizer
weights_regularizer = tf.contrib.layers.l2_regularizer(self.cfg.weight_decay)
tower_grads = []
with tf.variable_scope(tf.get_variable_scope()):
for i in range(self.cfg.nr_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('tower_%d' % i) as name_scope:
# Force all Variables to reside on the CPU.
with slim.arg_scope([slim.model_variable, slim.variable], device='/device:CPU:0'):
with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, \
slim.conv2d_transpose, slim.separable_conv2d,
slim.fully_connected],
weights_regularizer=weights_regularizer,
biases_regularizer=biases_regularizer,
weights_initializer=weights_initializer,
biases_initializer=biases_initializer):
# loss over single GPU
self.net.make_network(is_train=True)
if i == self.cfg.nr_gpus - 1:
loss = self.net.get_loss(include_wd=True)
else:
loss = self.net.get_loss()
self._input_list.append( self.net.get_inputs() )
tf.get_variable_scope().reuse_variables()
if i == 0:
if self.cfg.nr_gpus > 1 and self.cfg.bn_train is True:
self.logger.warning("BN is calculated only on single GPU.")
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, name_scope)
with tf.control_dependencies(extra_update_ops):
grads = self._optimizer.compute_gradients(loss)
else:
grads = self._optimizer.compute_gradients(loss)
final_grads = []
with tf.variable_scope('Gradient_Mult') as scope:
for grad, var in grads:
scale = 1.
if self.cfg.double_bias and '/biases:' in var.name:
scale *= 2.
if not np.allclose(scale, 1.):
grad = tf.multiply(grad, scale)
final_grads.append((grad, var))
tower_grads.append(final_grads)
if len(tower_grads) > 1:
grads = sum_gradients(tower_grads)
else:
grads = tower_grads[0]
if False:
variable_averages = tf.train.ExponentialMovingAverage(0.9999)
variables_to_average = (tf.trainable_variables() + tf.moving_average_variables())
variables_averages_op = variable_averages.apply(variables_to_average)
apply_gradient_op = self._optimizer.apply_gradients(grads)
train_op = tf.group(apply_gradient_op, variables_averages_op, *extra_update_ops)
else:
apply_gradient_op = self._optimizer.apply_gradients(grads)
train_op = tf.group(apply_gradient_op, *extra_update_ops)
return train_op
def find_erange(e):
for i, j in enumerate(erange_unique):
if np.allclose(j, e):
return i
def test_slerp(self):
lon0, lat0 = (0, 0)
lon1, lat1 = (0, 1)
self.assertTrue(
np.allclose(slerp(lon0, lat0, lon1, lat1, 0.5), (0, 0.5)))
def test_compute_ring_normal(self):
# FIXME might break with different version of rdkit
normal = rgf.compute_ring_normal(self.cycle4, range(4))
self.assertTrue(
np.allclose(np.abs(normal / np.linalg.norm(normal)), [0, 0, 1]))
def find_trange(t):
for i, j in enumerate(trange_unique):
if np.allclose(j, t, rtol=1e-15):
return i
def test_calc_collapse_structures1(self):
edm = EventDamageModel([0.0]*17, [0.0]*17, [0.0]*17,
[0.0]*17, [0.0]*17)
edm.struct_damage = num.zeros(17,num.float)
edm.contents_damage = num.zeros(17,num.float)
collapse_probability = {0.4:[0], #0
0.6:[1], #1
0.5:[2], #1
0.25:[3,4], #1
0.1:[5,6,7,8], #0
0.2:[9,10,11,12,13,14,15,16]} #2
assert num.allclose(edm.max_depths, 0.0)
assert num.allclose(edm.shore_distances, 0.0)
assert num.allclose(edm.walls, 0.0)
assert num.allclose(edm.struct_costs, 0.0)
assert num.allclose(edm.content_costs, 0.0)
edm._calc_collapse_structures(collapse_probability, verbose_csv=True)
# Random numbers are not stable between Python2 and Python3 - even with the same seed seed(17, version=1)
# See https://stackoverflow.com/questions/11929701/why-is-seeding-the-random-generator-not-stable-between-versions-of-python
if system_tools.major_version == 2:
assert num.allclose(edm.struct_damage, [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]), 'Expected %s' % edm.struct_damage
assert num.allclose(edm.contents_damage, [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]), 'Expected %s' % edm.contents_damage
self.assertTrue(edm.struct_damage[0] == 0.0 and
edm.contents_damage[0] == 0.0,
'Error!')
self.assertTrue(edm.struct_damage[1] == 1.0 and
edm.contents_damage[1] == 1.0,
'Error!')
self.assertTrue(edm.struct_damage[2] == 1.0 and
edm.contents_damage[2] == 1.0,
'Error!')
self.assertTrue(edm.struct_damage[3] + edm.struct_damage[4] == 1.0 and
edm.contents_damage[3] + edm.contents_damage[4] ==1.0,
'Error!')
elif system_tools.major_version == 3:
assert num.allclose(edm.struct_damage, [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0]), 'Expected %s' % edm.struct_damage
assert num.allclose(edm.contents_damage, [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0]), 'Expected %s' % edm.contents_damage
self.assertTrue(edm.struct_damage[0] == 0.0 and
edm.contents_damage[0] == 0.0,
'Error!')
self.assertTrue(edm.struct_damage[1] == 1.0 and
edm.contents_damage[1] == 1.0,
'Error!')
self.assertTrue(edm.struct_damage[2] == 0.0 and
edm.contents_damage[2] == 0.0,
'Error!')
self.assertTrue(edm.struct_damage[3] + edm.struct_damage[4] == 0 and
edm.contents_damage[3] + edm.contents_damage[4] ==0,
'Error!')
else:
raise Exception('Unknown python version: %s' % system_tools.version)
sum_struct = 0.0
sum_contents = 0.0
for i in [5,6,7,8]:
sum_struct += edm.struct_damage[i]
sum_contents += edm.contents_damage[i]
#print("", end=' ')
self.assertTrue(sum_struct == 0.0 and sum_contents == 0.0,
'Error!')
sum_struct = 0.0
sum_contents = 0.0
for i in [9,10,11,12,13,14,15,16]:
sum_struct += edm.struct_damage[i]
sum_contents += edm.contents_damage[i]
self.assertTrue( sum_struct == 2.0 and sum_contents == 2.0,
'Error!')
def test_multi_channel_xcorr():
from eqcorrscan.utils.correlate import get_stream_xcorr
chans = ['EHZ', 'EHN', 'EHE']
stas = ['COVA', 'FOZ', 'LARB', 'GOVA', 'MTFO', 'MTBA']
n_templates = 20
stream_len = 10000
template_len = 200
templates = []
stream = Stream()
for station in stas:
for channel in chans:
stream += Trace(data=np.random.randn(stream_len))
stream[-1].stats.channel = channel
stream[-1].stats.station = station
for i in range(n_templates):
template = Stream()
for station in stas:
for channel in chans:
template += Trace(data=np.random.randn(template_len))
template[-1].stats.channel = channel
template[-1].stats.station = station
templates.append(template)
print("Running time serial")
tic = time.time()
multichannel_normxcorr = get_stream_xcorr(
"time_domain", concurrency=None)
cccsums_t_s, no_chans, chans = multichannel_normxcorr(
templates=templates, stream=stream)
toc = time.time()
print('Time-domain in serial took: %f seconds' % (toc-tic))
print("Running time parallel")
tic = time.time()
multichannel_normxcorr = get_stream_xcorr(
"time_domain", concurrency="multiprocess")
cccsums_t_p, no_chans, chans = multichannel_normxcorr(
templates=templates, stream=stream, cores=4)
toc = time.time()
print('Time-domain in parallel took: %f seconds' % (toc-tic))
print("Running frequency serial")
tic = time.time()
multichannel_normxcorr = get_stream_xcorr(
"fftw", concurrency=None)
cccsums_f_s, no_chans, chans = multichannel_normxcorr(
templates=templates, stream=stream)
toc = time.time()
print('Frequency-domain in serial took: %f seconds' % (toc-tic))
print("Running frequency parallel")
tic = time.time()
multichannel_normxcorr = get_stream_xcorr(
"fftw", concurrency="multiprocess")
cccsums_f_p, no_chans, chans = multichannel_normxcorr(
templates=templates, stream=stream, cores=4)
toc = time.time()
print('Frequency-domain in parallel took: %f seconds' % (toc-tic))
print("Running frequency openmp parallel")
tic = time.time()
multichannel_normxcorr = get_stream_xcorr(
"fftw", concurrency="concurrent")
cccsums_f_op, no_chans, chans = multichannel_normxcorr(
templates=templates, stream=stream, cores=4)
toc = time.time()
print('Frequency-domain in parallel took: %f seconds' % (toc-tic))
print("Running frequency openmp parallel outer")
tic = time.time()
multichannel_normxcorr = get_stream_xcorr(
"fftw", concurrency="concurrent")
cccsums_f_outer_op, no_chans, chans = multichannel_normxcorr(
templates=templates, stream=stream, cores=1, cores_outer=4)
toc = time.time()
print('Frequency-domain in parallel took: %f seconds' % (toc-tic))
print("Finished")
assert(np.allclose(cccsums_t_s, cccsums_t_p, atol=0.00001))
assert(np.allclose(cccsums_f_s, cccsums_f_p, atol=0.00001))
assert(np.allclose(cccsums_f_s, cccsums_f_op, atol=0.00001))
assert(np.allclose(cccsums_f_s, cccsums_f_outer_op, atol=0.00001))
assert(np.allclose(cccsums_t_p, cccsums_f_s, atol=0.001))
def eval_transport(sim):
print("evaluating transport...")
name = ex[sim.idxsim]
gwtname = "gwt_" + name
fpth = os.path.join(sim.simpath, "{}.ucn".format(gwtname))
try:
cobj = flopy.utils.HeadFile(
fpth, precision="double", text="CONCENTRATION"
)
conc = cobj.get_data()
except:
assert False, 'could not load data from "{}"'.format(fpth)
# This is the answer to this problem. These concentrations are for
# time step 200.
cres1 = [
[
[
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
9.99999999e-01,
9.99999999e-01,
9.99999998e-01,
9.99999996e-01,
9.99999992e-01,
9.99999986e-01,
9.99999974e-01,
9.99999953e-01,
9.99999916e-01,
9.99999853e-01,
9.99999744e-01,
9.99999560e-01,
9.99999254e-01,
9.99998750e-01,
9.99997930e-01,
9.99996616e-01,
9.99994534e-01,
9.99991280e-01,
9.99986256e-01,
9.99978596e-01,
9.99967064e-01,
9.99949912e-01,
9.99924710e-01,
9.99888122e-01,
9.99835630e-01,
9.99761195e-01,
9.99656858e-01,
9.99512258e-01,
9.99314094e-01,
9.99045508e-01,
9.98685418e-01,
9.98207806e-01,
9.97580979e-01,
9.96766847e-01,
9.95720240e-01,
9.94388311e-01,
9.92710079e-01,
9.90616155e-01,
9.88028708e-01,
9.84861727e-01,
9.81021610e-01,
9.76408132e-01,
9.70915801e-01,
9.64435624e-01,
9.56857251e-01,
9.48071482e-01,
9.37973073e-01,
9.26463768e-01,
9.13455460e-01,
8.98873378e-01,
8.82659167e-01,
8.64773747e-01,
8.45199820e-01,
8.23943904e-01,
8.01037798e-01,
7.76539388e-01,
7.50532734e-01,
7.23127419e-01,
6.94457149e-01,
6.64677639e-01,
6.33963857e-01,
6.02506698e-01,
5.70509218e-01,
5.38182550e-01,
5.05741631e-01,
4.73400912e-01,
4.41370157e-01,
4.09850495e-01,
3.79030822e-01,
3.49084661e-01,
3.20167556e-01,
2.92415050e-01,
2.65941266e-01,
2.40838116e-01,
2.17175095e-01,
1.94999626e-01,
1.74337914e-01,
1.55196221e-01,
1.37562492e-01,
1.21408260e-01,
1.06690730e-01,
9.33549731e-02,
8.13361604e-02,
7.05617550e-02,
6.09536208e-02,
5.24299924e-02,
4.49072719e-02,
3.83016284e-02,
3.25303834e-02,
2.75131759e-02,
2.31729100e-02,
1.94364916e-02,
1.62353686e-02,
1.35058926e-02,
1.11895205e-02,
9.23287950e-03,
7.58771614e-03,
6.21075097e-03,
5.06346009e-03,
4.11180172e-03,
3.32590494e-03,
2.67973538e-03,
2.15075039e-03,
1.71955399e-03,
1.36956006e-03,
1.08666981e-03,
8.58968428e-04,
6.76443820e-04,
5.30729288e-04,
4.14870946e-04,
3.23119730e-04,
2.50747289e-04,
1.93884515e-04,
1.49381177e-04,
1.14684894e-04,
8.77376133e-05,
6.68877051e-05,
5.08158521e-05,
3.84730011e-05,
2.90287464e-05,
2.18286673e-05,
1.63592796e-05,
1.22194132e-05,
9.09697239e-06,
6.75017149e-06,
4.99246589e-06,
3.68051556e-06,
2.70462002e-06,
1.98115573e-06,
1.44662627e-06,
1.05300391e-06,
7.64099584e-07,
5.52747307e-07,
3.98630253e-07,
2.86609767e-07,
2.05446572e-07,
1.46826341e-07,
1.04620334e-07,
7.43267373e-08,
5.26502675e-08,
3.71870946e-08,
2.61896435e-08,
1.83917063e-08,
1.28789035e-08,
8.99310023e-09,
6.26214049e-09,
4.34838583e-09,
3.01116258e-09,
2.07945770e-09,
1.43213687e-09,
9.83662737e-10,
6.73819846e-10,
4.60347508e-10,
3.13675549e-10,
2.13175248e-10,
1.44498191e-10,
9.76935212e-11,
6.58802074e-11,
4.43137016e-11,
2.97319183e-11,
1.98983717e-11,
1.32840240e-11,
8.84640833e-12,
4.39186836e-12,
]
]
]
cres1 = np.array(cres1)
cres2 = [
[
[
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
9.99999999e-01,
9.99999999e-01,
1.00000000e00,
1.00000000e00,
1.00000000e00,
9.99999998e-01,
9.99999993e-01,
9.99999994e-01,
1.00000000e00,
1.00000002e00,
1.00000002e00,
9.99999999e-01,
9.99999962e-01,
9.99999940e-01,
9.99999973e-01,
1.00000007e00,
1.00000018e00,
1.00000017e00,
9.99999959e-01,
9.99999556e-01,
9.99999217e-01,
9.99999365e-01,
1.00000035e00,
1.00000203e00,
1.00000340e00,
1.00000262e00,
9.99997675e-01,
9.99987864e-01,
9.99975605e-01,
9.99967699e-01,
9.99974537e-01,
1.00000558e00,
1.00005977e00,
1.00011022e00,
1.00008407e00,
9.99839670e-01,
9.99144904e-01,
9.97660969e-01,
9.94936340e-01,
9.90414434e-01,
9.83456888e-01,
9.73381978e-01,
9.59515273e-01,
9.41247475e-01,
9.18092979e-01,
8.89742279e-01,
8.56101925e-01,
8.17317280e-01,
7.73775496e-01,
7.26088513e-01,
6.75058258e-01,
6.21628023e-01,
5.66825293e-01,
5.11701685e-01,
4.57275459e-01,
4.04481176e-01,
3.54129825e-01,
3.06881347e-01,
2.63230002e-01,
2.23501864e-01,
1.87862728e-01,
1.56334150e-01,
1.28815062e-01,
1.05106442e-01,
8.49367728e-02,
6.79864524e-02,
5.39097837e-02,
4.23536725e-02,
3.29726109e-02,
2.54398934e-02,
1.94552984e-02,
1.47496575e-02,
1.10868391e-02,
8.26370985e-03,
6.10861676e-03,
4.47887907e-03,
3.25770405e-03,
2.35085553e-03,
1.68332282e-03,
1.19616223e-03,
8.43620069e-04,
5.90594884e-04,
4.10458367e-04,
2.83227080e-04,
1.94059595e-04,
1.32043648e-04,
8.92335599e-05,
5.98980167e-05,
3.99405773e-05,
2.64592197e-05,
1.74157687e-05,
1.13907502e-05,
7.40365644e-06,
4.78259593e-06,
3.07073947e-06,
1.95984426e-06,
1.24347214e-06,
7.84372275e-07,
4.91943284e-07,
3.06795247e-07,
1.90263904e-07,
1.17346736e-07,
7.19820791e-08,
4.39185249e-08,
2.66545669e-08,
1.60925890e-08,
9.66584556e-09,
5.77619748e-09,
3.43447585e-09,
2.03199468e-09,
1.19634152e-09,
7.00946025e-10,
4.08730000e-10,
2.37211756e-10,
1.37027700e-10,
7.87911071e-11,
4.50989518e-11,
2.56980023e-11,
1.45780272e-11,
8.23354736e-12,
4.63005332e-12,
2.59249113e-12,
1.44544628e-12,
8.02528982e-13,
4.43725011e-13,
2.44332619e-13,
1.33993251e-13,
7.31876077e-14,
3.98166029e-14,
2.15765206e-14,
1.16468205e-14,
6.26267665e-15,
3.35472405e-15,
1.79025576e-15,
9.51813339e-16,
5.04177652e-16,
2.66088975e-16,
1.39925789e-16,
7.33182482e-17,
3.82811880e-17,
1.99174186e-17,
1.03269036e-17,
5.33594407e-18,
2.74771548e-18,
1.41014305e-18,
7.21291388e-19,
3.67691580e-19,
1.86885340e-19,
9.45533475e-20,
4.79404225e-20,
2.37115056e-20,
1.27310694e-20,
2.67369800e-21,
]
]
]
cres2 = np.array(cres2)
cres3 = [
[
[
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
1.00000000e00,
9.99999999e-01,
9.99999999e-01,
9.99999998e-01,
9.99999997e-01,
9.99999996e-01,
9.99999992e-01,
9.99999988e-01,
9.99999977e-01,
9.99999965e-01,
9.99999935e-01,
9.99999903e-01,
9.99999820e-01,
9.99999731e-01,
9.99999499e-01,
9.99999255e-01,
9.99998614e-01,
9.99997941e-01,
9.99996190e-01,
9.99994359e-01,
9.99989643e-01,
9.99984740e-01,
9.99972298e-01,
9.99959473e-01,
9.99927548e-01,
9.99895008e-01,
9.99815884e-01,
9.99736350e-01,
9.99548166e-01,
9.99362087e-01,
9.98935116e-01,
9.98520712e-01,
9.97601139e-01,
9.96726795e-01,
9.94854670e-01,
9.93113734e-01,
9.89523166e-01,
9.86262124e-01,
9.79792542e-01,
9.74060601e-01,
9.63133239e-01,
9.53698327e-01,
9.36427537e-01,
9.21907768e-01,
8.96401341e-01,
8.75537178e-01,
8.40378779e-01,
8.12414499e-01,
7.67224282e-01,
7.32294666e-01,
6.78174275e-01,
6.37542814e-01,
5.77189801e-01,
5.33200175e-01,
4.70564614e-01,
4.26261319e-01,
3.65793900e-01,
3.24305464e-01,
2.70028089e-01,
2.33916037e-01,
1.88631553e-01,
1.59426651e-01,
1.24322194e-01,
1.02384383e-01,
7.71079810e-02,
6.18067033e-02,
4.49072754e-02,
3.50006334e-02,
2.45124747e-02,
1.85605248e-02,
1.25201360e-02,
9.20274911e-03,
5.97547120e-03,
4.26070056e-03,
2.66158628e-03,
1.83980553e-03,
1.10519401e-03,
7.40179413e-04,
4.27404601e-04,
2.77183690e-04,
1.53799502e-04,
9.65363514e-05,
5.14550934e-05,
3.12434950e-05,
1.59926916e-05,
9.38944096e-06,
4.61422039e-06,
2.61810100e-06,
1.23478054e-06,
6.76699550e-07,
3.06141480e-07,
1.61921388e-07,
7.02044355e-08,
3.57908578e-08,
1.48458395e-08,
7.27822667e-09,
2.87772343e-09,
1.35034049e-09,
5.04924114e-10,
2.24462051e-10,
7.79256525e-11,
3.19978311e-11,
9.80115906e-12,
3.42172760e-12,
7.38995558e-13,
1.04998282e-13,
-6.08063778e-14,
-7.00202752e-14,
-4.57797962e-14,
-2.73273037e-14,
-1.26821070e-14,
-5.66553096e-15,
-1.79362990e-15,
-4.40132769e-16,
1.05900605e-16,
2.30438418e-16,
2.68347207e-16,
2.60728717e-16,
2.32632994e-16,
1.97839789e-16,
1.63276013e-16,
1.32036738e-16,
1.05203244e-16,
8.28614219e-17,
6.46461341e-17,
5.00217396e-17,
3.84212334e-17,
2.93113868e-17,
2.22197912e-17,
1.67426198e-17,
1.25429101e-17,
9.34448747e-18,
6.92421024e-18,
5.10394011e-18,
3.74295077e-18,
2.73111210e-18,
1.98297560e-18,
1.43276787e-18,
1.03023499e-18,
7.37248326e-19,
5.25070294e-19,
3.72178066e-19,
2.62549962e-19,
1.84329161e-19,
1.28790859e-19,
8.95504779e-20,
6.19612940e-20,
4.26594479e-20,
2.92226046e-20,
1.99155401e-20,
1.35016962e-20,
9.10444343e-21,
4.57287749e-21,
]
]
]
cres3 = np.array(cres3)
creslist = [cres1, cres2, cres3]
assert np.allclose(
creslist[sim.idxsim], conc
), "simulated concentrations do not match with known solution."
return
def test_compute_ring_center(self):
# FIXME might break with different version of rdkit
self.assertTrue(
np.allclose(rgf.compute_ring_center(self.cycle4, range(4)), 0))
def helper_func(self, config_idx):
(in_ch, out_ch, k_size, stride, padding, has_bias, batch_size, height,
width) = self.configs[config_idx]
torch_conv2d = Conv2d(
in_ch,
out_ch,
k_size,
stride=stride,
padding=padding,
bias=has_bias)
torch_conv2d.type(torch.DoubleTensor)
conv2d_layer = Conv2DLayer(
in_ch, (k_size, k_size),
out_ch,
lambda t: torch.nn.init.normal(t, -1, 1),
stride=(stride, stride),
padding=(padding, padding),
bias=has_bias)
conv2d_layer.type(torch.DoubleTensor)
input_tensor = (torch.DoubleTensor(batch_size, in_ch, height, width)
.uniform_(-1, 1))
input_layer = Variable(input_tensor, requires_grad=True)
input_torch = Variable(input_tensor.clone(), requires_grad=True)
bias_tensor = torch.DoubleTensor(out_ch).uniform_(-1, 1)
weights = (torch.DoubleTensor(out_ch, in_ch, k_size, k_size).uniform_(
-1, 1))
torch_conv2d.weight.data.copy_(weights)
if has_bias:
torch_conv2d.bias.data.copy_(bias_tensor)
layer_weight_shape = (out_ch, in_ch * k_size * k_size)
conv2d_layer.kernels.data.copy_(weights.view(layer_weight_shape))
if has_bias:
conv2d_layer.bias.data.copy_(bias_tensor.view(out_ch, 1))
layer_result = conv2d_layer(input_layer)
layer_result_np = layer_result.data.numpy()
torch_result = torch_conv2d(input_torch)
torch_result_np = torch_result.data.numpy()
self.assertTrue(np.allclose(layer_result_np, torch_result_np))
# verify gradient
gradient = torch.DoubleTensor(layer_result.shape)
layer_result.backward(gradient)
torch_result.backward(gradient)
self.assertTrue(
np.allclose(
input_layer.grad.data.numpy(),
input_torch.grad.data.numpy(),
equal_nan=True))
layer_weight_grad = conv2d_layer.kernels.grad
torch_weight_grad = torch_conv2d.weight.grad.view(layer_weight_shape)
self.assertTrue(
np.allclose(
layer_weight_grad.data.numpy(),
torch_weight_grad.data.numpy(),
equal_nan=True))
if has_bias:
layer_bias_grad = conv2d_layer.bias.grad.view(out_ch)
torch_bias_grad = torch_conv2d.bias.grad.view(out_ch)
self.assertTrue(
np.allclose(
layer_bias_grad.data.numpy(),
torch_bias_grad.data.numpy(),
equal_nan=True))
def test_inundation_damage_list(self):
# create mesh
mesh_file = tempfile.mktemp(".tsh")
points = [[0.0,0.0],[6.0,0.0],[6.0,6.0],[0.0,6.0]]
m = Mesh()
m.add_vertices(points)
m.auto_segment()
m.generate_mesh(verbose=False)
m.export_mesh_file(mesh_file)
#Create shallow water domain
domain = Domain(mesh_file)
os.remove(mesh_file)
domain.default_order=2
#Set some field values
domain.set_quantity('elevation', elevation_function)
domain.set_quantity('friction', 0.03)
domain.set_quantity('xmomentum', 22.0)
domain.set_quantity('ymomentum', 55.0)
######################
# Boundary conditions
B = Transmissive_boundary(domain)
domain.set_boundary( {'exterior': B})
# This call mangles the stage values.
domain.distribute_to_vertices_and_edges()
domain.set_quantity('stage', 0.3)
#sww_file = tempfile.mktemp("")
domain.set_name('datatest' + str(time.time()))
domain.format = 'sww'
domain.smooth = True
domain.reduction = mean
sww = SWW_file(domain)
sww.store_connectivity()
sww.store_timestep()
domain.set_quantity('stage', -0.3)
domain.time = 2.
sww.store_timestep()
#Create a csv file
csv_file = tempfile.mktemp(".csv")
fd = open(csv_file,'w',newline="")
writer = csv.writer(fd)
writer.writerow(['x','y',STR_VALUE_LABEL,CONT_VALUE_LABEL,'ROOF_TYPE',WALL_TYPE_LABEL, SHORE_DIST_LABEL])
writer.writerow([5.5,0.5,'10','130000','Metal','Timber',20])
writer.writerow([4.5,1.0,'150','76000','Metal','Double Brick',20])
writer.writerow([0.1,1.5,'100','76000','Metal','Brick Veneer',300])
writer.writerow([6.1,1.5,'100','76000','Metal','Brick Veneer',300])
fd.close()
extension = ".csv"
csv_fileII = tempfile.mktemp(extension)
fd = open(csv_fileII,'w',newline="")
writer = csv.writer(fd)
writer.writerow(['x','y',STR_VALUE_LABEL,CONT_VALUE_LABEL,'ROOF_TYPE',WALL_TYPE_LABEL, SHORE_DIST_LABEL])
writer.writerow([5.5,0.5,'10','130000','Metal','Timber',20])
writer.writerow([4.5,1.0,'150','76000','Metal','Double Brick',20])
writer.writerow([0.1,1.5,'100','76000','Metal','Brick Veneer',300])
writer.writerow([6.1,1.5,'100','76000','Metal','Brick Veneer',300])
fd.close()
sww_file = domain.get_name() + "." + domain.format
#print "sww_file",sww_file
marker='_gosh'
inundation_damage(sww_file, [csv_file, csv_fileII],
exposure_file_out_marker=marker,
verbose=False)
# Test one file
csv_handle = Exposure(csv_file[:-4]+marker+extension)
struct_loss = csv_handle.get_column(EventDamageModel.STRUCT_LOSS_TITLE)
#print "struct_loss",struct_loss
struct_loss = [float(x) for x in struct_loss]
#pprint(struct_loss)
assert num.allclose(struct_loss,[10.0, 150.0, 66.55333347876866, 0.0])
depth = csv_handle.get_column(EventDamageModel.MAX_DEPTH_TITLE)
#print "depth",depth
depth = [float(x) for x in depth]
assert num.allclose(depth, [3.000000011920929, 2.9166666785875957, 2.2666666785875957, -0.3])
# Test another file
csv_handle = Exposure(csv_fileII[:-4]+marker+extension)
struct_loss = csv_handle.get_column(EventDamageModel.STRUCT_LOSS_TITLE)
#print "struct_loss",struct_loss
struct_loss = [float(x) for x in struct_loss]
#pprint(struct_loss)
assert num.allclose(struct_loss, [10.0, 150.0, 66.553333478768664, 0.0])
depth = csv_handle.get_column(EventDamageModel.MAX_DEPTH_TITLE)
#print "depth",depth
depth = [float(x) for x in depth]
assert num.allclose(depth,[3.000000011920929, 2.9166666785875957, 2.2666666785875957, -0.3])
os.remove(sww.filename)
os.remove(csv_file)
os.remove(csv_fileII)
N = 1000
signal = np.array([random.uniform(0, 1) for i in range(N)])
fractionalTransformer = frft.FractionalFourierTransform()
# First Test: Identity Matrix:
print("""=========================================================
Test One: In this test the signal is given to the fractional
transform with ratio = 0, it is supposed to return the identity.
""")
testRes = fractionalTransformer.frft(
y=signal, alpha=0
)
if np.allclose(testRes, signal):
print("Identity Test Passed...!")
else:
print("X: Identity Test Failed...!")
print("""=========================================================
""")
# Second Test: Conventional Fourier Matrix:
print("""=========================================================
Test Two: In this test the signal is given to the fractional
transform with ratio = 1, it is supposed to return the conventional
Fourier Transform of the input.
""")
testRes = fractionalTransformer.frft(
de = []
for i in range(3):
mol._env[ptr+i] = coord[i] + inc
mf = scf.RHF(mol).run(conv_tol=1e-14)
e1a = mf.nuc_grad_method().kernel()
mol._env[ptr+i] = coord[i] - inc
mf = scf.RHF(mol).run(conv_tol=1e-14)
e1b = mf.nuc_grad_method().kernel()
mol._env[ptr+i] = coord[i]
de.append((e1a-e1b)/(2*inc))
return de
e2ref = [grad_full(ia, .5e-4) for ia in range(mol.natm)]
e2ref = numpy.asarray(e2ref).reshape(n3,n3)
print(numpy.linalg.norm(e2-e2ref))
print(abs(e2-e2ref).max())
print(numpy.allclose(e2,e2ref,atol=1e-6))
# \partial^2 E / \partial R \partial R'
e2 = hobj.partial_hess_elec(mf.mo_energy, mf.mo_coeff, mf.mo_occ)
e2 += hobj.hess_nuc(mol)
e2 = e2.transpose(0,2,1,3).reshape(n3,n3)
def grad_partial_R(ia, inc):
coord = mol.atom_coord(ia).copy()
ptr = mol._atm[ia,gto.PTR_COORD]
de = []
for i in range(3):
mol._env[ptr+i] = coord[i] + inc
e1a = mf.nuc_grad_method().kernel()
mol._env[ptr+i] = coord[i] - inc
e1b = mf.nuc_grad_method().kernel()
mol._env[ptr+i] = coord[i]
def test_batch(self):
numpy.random.seed(734)
d = 2
m = 2
n = 15
paths = [numpy.random.uniform(-1, 1, size=(6, d)) for i in range(n)]
pathArray15 = stack(paths)
pathArray1315 = numpy.reshape(pathArray15, (1, 3, 1, 5, 6, d))
sigs = [iisignature.sig(i, m) for i in paths]
sigArray = stack(sigs)
sigArray15 = iisignature.sig(pathArray15, m)
sigArray1315 = iisignature.sig(pathArray1315, m)
siglength = iisignature.siglength(d, m)
self.assertEqual(sigArray1315.shape, (1, 3, 1, 5, siglength))
self.assertTrue(
numpy.allclose(sigArray1315.reshape(n, siglength), sigs))
self.assertEqual(sigArray15.shape, (15, siglength))
self.assertTrue(numpy.allclose(sigArray15, sigs))
backsigs = [
iisignature.sigbackprop(i, j, m) for i, j in zip(sigs, paths)
]
backsigArray = stack(backsigs)
backsigs1315 = iisignature.sigbackprop(sigArray1315, pathArray1315, m)
self.assertEqual(backsigs1315.shape, (1, 3, 1, 5, 6, d))
self.assertTrue(
numpy.allclose(backsigs1315.reshape(n, 6, 2), backsigArray))
data = [numpy.random.uniform(size=(d, )) for i in range(n)]
dataArray1315 = stack(data).reshape((1, 3, 1, 5, d))
joined = [iisignature.sigjoin(i, j, m) for i, j in zip(sigs, data)]
joined1315 = iisignature.sigjoin(sigArray1315, dataArray1315, m)
self.assertEqual(joined1315.shape, (1, 3, 1, 5, siglength))
self.assertTrue(
numpy.allclose(joined1315.reshape(n, -1), stack(joined)))
backjoined = [
iisignature.sigjoinbackprop(i, j, k, m)
for i, j, k in zip(joined, sigs, data)
]
backjoinedArrays = [
stack([i[j] for i in backjoined]) for j in range(2)
]
backjoined1315 = iisignature.sigjoinbackprop(joined1315, sigArray1315,
dataArray1315, m)
self.assertEqual(backjoined1315[0].shape, sigArray1315.shape)
self.assertEqual(backjoined1315[1].shape, dataArray1315.shape)
self.assertTrue(
numpy.allclose(backjoined1315[0].reshape(n, -1),
backjoinedArrays[0]))
self.assertTrue(
numpy.allclose(backjoined1315[1].reshape(n, -1),
backjoinedArrays[1]))
scaled = [iisignature.sigscale(i, j, m) for i, j in zip(sigs, data)]
scaled1315 = iisignature.sigscale(sigArray1315, dataArray1315, m)
self.assertEqual(scaled1315.shape, (1, 3, 1, 5, siglength))
self.assertTrue(
numpy.allclose(scaled1315.reshape(n, -1), stack(scaled)))
backscaled = [
iisignature.sigscalebackprop(i, j, k, m)
for i, j, k in zip(scaled, sigs, data)
]
backscaledArrays = [
stack([i[j] for i in backscaled]) for j in range(2)
]
backscaled1315 = iisignature.sigscalebackprop(scaled1315, sigArray1315,
dataArray1315, m)
self.assertEqual(backscaled1315[0].shape, sigArray1315.shape)
self.assertEqual(backscaled1315[1].shape, dataArray1315.shape)
self.assertTrue(
numpy.allclose(backscaled1315[0].reshape(n, -1),
backscaledArrays[0]))
self.assertTrue(
numpy.allclose(backscaled1315[1].reshape(n, -1),
backscaledArrays[1]))
s_s = (iisignature.prepare(d, m,
"cosx"), iisignature.prepare(d, m, "coshx"))
for type in ("c", "o", "s", "x", "ch", "oh", "sh"):
s = s_s[1 if "h" in type else 0]
logsigs = [iisignature.logsig(i, s, type) for i in paths]
logsigArray = stack(logsigs)
logsigArray1315 = iisignature.logsig(pathArray1315, s, type)
self.assertEqual(logsigArray1315.shape,
(1, 3, 1, 5, logsigs[0].shape[0]), type)
self.assertTrue(
numpy.allclose(logsigArray1315.reshape(n, -1), logsigArray),
type)
if type in ("s", "x", "sh"):
backlogs = stack(
iisignature.logsigbackprop(i, j, s, type)
for i, j in zip(logsigs, paths))
backlogs1315 = iisignature.logsigbackprop(
logsigArray1315, pathArray1315, s, type)
self.assertEqual(backlogs1315.shape, backsigs1315.shape)
self.assertTrue(
numpy.allclose(backlogs1315.reshape(n, 6, d), backlogs),
type)
a = iisignature.rotinv2dprepare(m, "a")
rots = stack([iisignature.rotinv2d(i, a) for i in paths])
rots1315 = iisignature.rotinv2d(pathArray1315, a)
self.assertEqual(rots1315.shape, (1, 3, 1, 5, rots.shape[1]))
self.assertTrue(numpy.allclose(rots1315.reshape(n, -1), rots))
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
#1.1 Smoothing
from edge import conv, gaussian_kernel
# Define 3x3 Gaussian kernel with std = 1
kernel = gaussian_kernel(3, 1)
kernel_test = np.array(
[[ 0.05854983, 0.09653235, 0.05854983],
[ 0.09653235, 0.15915494, 0.09653235],
[ 0.05854983, 0.09653235, 0.05854983]]
)
# Test Gaussian kernel
if not np.allclose(kernel, kernel_test):
print('Incorrect values! Please check your implementation.')
# Test with different kernel_size and sigma
kernel_size = 5
sigma = 1.4
# Load image
img = io.imread('iguana.png', as_grey=True)
# Define 5x5 Gaussian kernel with std = sigma
kernel = gaussian_kernel(kernel_size, sigma)
# Convolve image with kernel to achieve smoothed effect
smoothed = conv(img, kernel)
def test_mlp(sort_sum_gradient):
fluid.set_flags({'FLAGS_sort_sum_gradient': sort_sum_gradient})
input_size = 5
paddle.seed(1)
mlp1 = MLP(input_size=input_size)
# generate the gradient of each step
mlp2 = MLP(input_size=input_size)
expected_weight1_grad = 0.
expected_bias1_grad = 0.
expected_weight2_grad = 0.
expected_bias2_grad = 0.
for batch_id in range(100):
x = paddle.uniform([10, input_size])
detach_x = x.detach()
clear_loss = mlp2(detach_x)
clear_loss.backward()
expected_weight1_grad = (expected_weight1_grad +
mlp2._linear1.weight.grad.numpy())
expected_bias1_grad = (expected_bias1_grad +
mlp2._linear1.bias.grad.numpy())
expected_weight2_grad = (expected_weight2_grad +
mlp2._linear2.weight.grad.numpy())
expected_bias2_grad = (expected_bias2_grad +
mlp2._linear2.bias.grad.numpy())
loss = mlp1(x)
loss.backward()
self.assertTrue(np.array_equal(loss.grad.numpy(), [1]))
self.assertTrue(
np.allclose(mlp1._linear1.weight.grad.numpy(),
expected_weight1_grad))
self.assertTrue(
np.allclose(mlp1._linear1.bias.grad.numpy(),
expected_bias1_grad))
self.assertTrue(
np.allclose(mlp1._linear2.weight.grad.numpy(),
expected_weight2_grad))
self.assertTrue(
np.allclose(mlp1._linear2.bias.grad.numpy(),
expected_bias2_grad))
mlp2.clear_gradients()
self.assertTrue(np.array_equal(clear_loss.grad.numpy(), [1]))
if ((batch_id + 1) % 10) % 2 == 0:
mlp1.clear_gradients()
expected_weight1_grad = 0.
expected_bias1_grad = 0.
expected_weight2_grad = 0.
expected_bias2_grad = 0.
elif ((batch_id + 1) % 10) % 2 == 1:
mlp1.clear_gradients()
mlp1._linear1.weight._set_grad_ivar(
paddle.ones([input_size, 3]))
mlp1._linear2.weight._set_grad_ivar(paddle.ones([3, 4]))
expected_weight1_grad = 1.
expected_bias1_grad = 0.
expected_weight2_grad = 1.
expected_bias2_grad = 0.
def dotest(self, type):
m = 8
nPaths = 95
nAngles = 348
numpy.random.seed(775)
s = iisignature.rotinv2dprepare(m, type)
coeffs = iisignature.rotinv2dcoeffs(s)
angles = numpy.random.uniform(0, math.pi * 2, size=nAngles + 1)
angles[0] = 0
rotationMatrices = [
numpy.array([[math.cos(i), math.sin(i)],
[-math.sin(i), math.cos(i)]]) for i in angles
]
paths = [
numpy.random.uniform(-1, 1, size=(32, 2)) for i in range(nPaths)
]
samePathRotInvs = [
iisignature.rotinv2d(numpy.dot(paths[0], mtx), s)
for mtx in rotationMatrices
]
#check the length matches
(length, ) = samePathRotInvs[0].shape
self.assertEqual(length, sum(i.shape[0] for i in coeffs))
self.assertEqual(length, iisignature.rotinv2dlength(s))
if type == "a":
self.assertEqual(length, sumCentralBinomialCoefficient(m // 2))
self.assertLess(length, nAngles) #sanity check on the test itself
#check that the invariants are invariant
if 0:
print("\n", numpy.column_stack(samePathRotInvs[0:7]))
for i in range(nAngles):
if 0 and diff(samePathRotInvs[0], samePathRotInvs[1 + i]) > 0.01:
print(i)
print(samePathRotInvs[0] - samePathRotInvs[1 + i])
print(diff(samePathRotInvs[0], samePathRotInvs[1 + i]))
self.assertLess(diff(samePathRotInvs[0], samePathRotInvs[1 + i]),
0.01)
#check that the invariants match the coefficients
if 1:
sigLevel = iisignature.sig(paths[0],
m)[iisignature.siglength(2, m - 1):]
lowerRotinvs = 0 if 2 == m else iisignature.rotinv2dlength(
iisignature.rotinv2dprepare(m - 2, type))
#print("\n",numpy.dot(coeffs[-1],sigLevel),"\n",samePathRotInvs[0][lowerRotinvs:])
#print(numpy.dot(coeffs[-1],sigLevel)-samePathRotInvs[0][lowerRotinvs:])
self.assertTrue(
numpy.allclose(numpy.dot(coeffs[-1], sigLevel),
samePathRotInvs[0][lowerRotinvs:],
atol=0.000001))
#check that we are not missing invariants
if type == "a":
#print("\nrotinvlength=",length,"
#siglength=",iisignature.siglength(2,m))
sigOffsets = []
for path in paths:
samePathSigs = [
iisignature.sig(numpy.dot(path, mtx), m)
for mtx in rotationMatrices[1:70]
]
samePathSigsOffsets = [
i - samePathSigs[0] for i in samePathSigs[1:]
]
sigOffsets.extend(samePathSigsOffsets)
#print(numpy.linalg.svd(numpy.row_stack(sigOffsets))[1])
def split(a, dim, level):
start = 0
out = []
for m in range(1, level + 1):
levelLength = dim**m
out.append(a[:, start:(start + levelLength)])
start = start + levelLength
assert (start == a.shape[1])
return out
allOffsets = numpy.row_stack(sigOffsets)
#print (allOffsets.shape)
splits = split(allOffsets, 2, m)
#print()
rank_tolerance = 0.01 # this is hackish
#print
#([numpy.linalg.matrix_rank(i.astype("float64"),rank_tolerance)
#for
#i in splits])
#print ([i.shape for i in splits])
#print(numpy.linalg.svd(splits[-1])[1])
#sanity check on the test
self.assertLess(splits[-1].shape[1], splits[0].shape[0])
totalUnspannedDimensions = sum(
i.shape[1] - numpy.linalg.matrix_rank(i, rank_tolerance)
for i in splits)
self.assertEqual(totalUnspannedDimensions, length)
if 0: #This doesn't work - the rank of the whole thing is less than
#sigLength-totalUnspannedDimensions, which suggests that there are
#inter-level dependencies,
#even though the shuffle product dependencies aren't linear.
#I don't know why this is.
sigLength = iisignature.siglength(2, m)
numNonInvariant = numpy.linalg.matrix_rank(
numpy.row_stack(sigOffsets))
predictedNumberInvariant = sigLength - numNonInvariant
print(sigLength, length, numNonInvariant)
self.assertLess(sigLength, nAngles)
self.assertEqual(predictedNumberInvariant, length)
def test_pdf(self):
beta = Beta(np.ones(2), np.ones(2))
self.assertTrue(
np.allclose(beta.pdf(np.random.uniform(size=(5, 2))), 1.))
def test_apply_decay_pass(self):
"""Test _apply_land_decay against MATLAB reference."""
v_rel = {
4: 0.0038950967656296597,
-1: 0.0038950967656296597,
0: 0.0038950967656296597,
1: 0.0038950967656296597,
2: 0.0038950967656296597,
3: 0.0038950967656296597,
5: 0.0038950967656296597
}
p_rel = {
4: (1.0499941, 0.007978940084158488),
-1: (1.0499941, 0.007978940084158488),
0: (1.0499941, 0.007978940084158488),
1: (1.0499941, 0.007978940084158488),
2: (1.0499941, 0.007978940084158488),
3: (1.0499941, 0.007978940084158488),
5: (1.0499941, 0.007978940084158488)
}
tc_track = tc.TCTracks.from_processed_ibtracs_csv(TC_ANDREW_FL)
tc_track.data[0]['orig_event_flag'] = False
extent = tc_track.get_extent()
land_geom = climada.util.coordinates.get_land_geometry(extent=extent,
resolution=10)
tc.track_land_params(tc_track.data[0], land_geom)
tc_synth._apply_land_decay(tc_track.data,
v_rel,
p_rel,
land_geom,
s_rel=True,
check_plot=False)
p_ref = np.array([
1.010000000000000, 1.009000000000000, 1.008000000000000,
1.006000000000000, 1.003000000000000, 1.002000000000000,
1.001000000000000, 1.000000000000000, 1.000000000000000,
1.001000000000000, 1.002000000000000, 1.005000000000000,
1.007000000000000, 1.010000000000000, 1.010000000000000,
1.010000000000000, 1.010000000000000, 1.010000000000000,
1.010000000000000, 1.007000000000000, 1.004000000000000,
1.000000000000000, 0.994000000000000, 0.981000000000000,
0.969000000000000, 0.961000000000000, 0.947000000000000,
0.933000000000000, 0.922000000000000, 0.930000000000000,
0.937000000000000, 0.951000000000000, 0.947000000000000,
0.943000000000000, 0.948000000000000, 0.946000000000000,
0.941000000000000, 0.937000000000000, 0.955000000000000,
0.9741457117, 0.99244068917, 1.00086729492, 1.00545853355,
1.00818354609, 1.00941850023, 1.00986192053, 1.00998400565
]) * 1e3
self.assertTrue(
np.allclose(p_ref, tc_track.data[0].central_pressure.values))
v_ref = np.array([
0.250000000000000, 0.300000000000000, 0.300000000000000,
0.350000000000000, 0.350000000000000, 0.400000000000000,
0.450000000000000, 0.450000000000000, 0.450000000000000,
0.450000000000000, 0.450000000000000, 0.450000000000000,
0.450000000000000, 0.400000000000000, 0.400000000000000,
0.400000000000000, 0.400000000000000, 0.450000000000000,
0.450000000000000, 0.500000000000000, 0.500000000000000,
0.550000000000000, 0.650000000000000, 0.800000000000000,
0.950000000000000, 1.100000000000000, 1.300000000000000,
1.450000000000000, 1.500000000000000, 1.250000000000000,
1.300000000000000, 1.150000000000000, 1.150000000000000,
1.150000000000000, 1.150000000000000, 1.200000000000000,
1.250000000000000, 1.250000000000000, 1.200000000000000,
0.9737967353, 0.687255951, 0.4994850556, 0.3551480462,
0.2270548036, 0.1302099557, 0.0645385918, 0.0225325851
]) * 1e2
self.assertTrue(
np.allclose(v_ref, tc_track.data[0].max_sustained_wind.values))
cat_ref = tc.set_category(tc_track.data[0].max_sustained_wind.values,
tc_track.data[0].max_sustained_wind_unit)
self.assertEqual(cat_ref, tc_track.data[0].category)
def test_mean(self):
beta = Beta(np.ones(2) * 3, np.ones(2))
self.assertTrue(np.allclose(beta.mean, 0.75))
def test_exercise_2(hmm, simple):
hmm.train_supervised(simple.train)
assert np.allclose(hmm.initial_probs, np.array([2 / 3, 1 / 3]), rtol=tolerance)
assert np.allclose(hmm.transition_probs, np.array([[1 / 2, 0.], [1 / 2, 5 / 8]]), rtol=tolerance)
assert np.allclose(hmm.final_probs, np.array([0., 3 / 8]), rtol=tolerance)
assert np.allclose(hmm.emission_probs, np.array([[0.75, 0.25], [0.25, 0.375], [0., 0.375], [0., 0.]]), rtol=tolerance)
def test_boyd_non_skew7(self):
"""test_boyd_non_skew
This tests the Boyd routine with data obtained from culvertw application 1.1 by IceMindserer BD Parkinson,
calculation code by MJ Boyd
This tests the blockage code
"""
stage_0 = 15.0 #change
stage_1 = 14.0 #change
elevation_0 = 11.0
elevation_1 = 10.0
domain_length = 200.0
domain_width = 200.0
culvert_length = 20.0
culvert_width = 1.2
##culvert_height = 3.66
culvert_blockage = 1.0
culvert_losses = {'inlet':0.5, 'outlet':1.0, 'bend':0.0, 'grate':0.0, 'pier': 0.0, 'other': 0.0}
culvert_mannings = 0.013
culvert_apron = 0.0
enquiry_gap = 5.0
expected_Q = 0.0
expected_v = 0.0
expected_d = 0.0
domain = self._create_domain(d_length=domain_length,
d_width=domain_width,
dx = 5.0,
dy = 5.0,
elevation_0 = elevation_0,
elevation_1 = elevation_1,
stage_0 = stage_0,
stage_1 = stage_1)
#print 'Defining Structures'
ep0 = numpy.array([domain_length/2-culvert_length/2, 100.0])
ep1 = numpy.array([domain_length/2+culvert_length/2, 100.0])
culvert = Boyd_pipe_operator(domain,
losses=culvert_losses,
diameter=culvert_width,
blockage=culvert_blockage,
end_points=[ep0, ep1],
#height=culvert_height,
apron=culvert_apron,
enquiry_gap=enquiry_gap,
use_momentum_jet=False,
use_velocity_head=False,
manning=culvert_mannings,
logging=False,
label='1.2pipe',
verbose=False)
#culvert.determine_inflow_outflow()
( Q, v, d ) = culvert.discharge_routine()
if verbose:
print 'test_boyd_non_skew7'
print 'Q: ', Q, 'expected_Q: ', expected_Q
print 'v: ', v, 'expected_v: ', expected_v
print 'd: ', d, 'expected_d: ', expected_d
assert numpy.allclose(Q, expected_Q, rtol=1.0e-2, atol=1.0e-5) #inflow
assert numpy.allclose(v, expected_v, rtol=1.0e-2, atol=1.0e-5) #outflow velocity
assert numpy.allclose(d, expected_d, rtol=1.0e-2, atol=1.0e-5) #depth at outlet used to calc v
def test_var(self):
beta = Beta(np.ones(2) * 3, np.ones(2))
self.assertTrue(np.allclose(beta.var, np.ones(2) * 3 / 80))
