def test_sub_two_scalars(self):
a = Ad_array(1, 0)
b = Ad_array(3, 0)
c = a - b
self.assertTrue(c.val == -2 and c.jac == 0)
self.assertTrue(a.val == 1 and a.jac == 0)
self.assertTrue(b.val == 3 and a.jac == 0)
def test_sub_two_ad_variables(self):
a = Ad_array(4, 1.0)
b = Ad_array(9, 3)
c = a - b
self.assertTrue(np.allclose(c.val, -5) and np.allclose(c.jac, -2))
self.assertTrue(a.val == 4 and np.allclose(a.jac, 1.0))
self.assertTrue(b.val == 9 and b.jac == 3)
def test_add_two_scalars(self):
a = Ad_array(1, 0)
b = Ad_array(-10, 0)
c = a + b
self.assertTrue(c.val == -9 and c.jac == 0)
self.assertTrue(a.val == 1 and a.jac == 0)
self.assertTrue(b.val == -10 and b.jac == 0)
def test_mul_scal_ad_scal(self):
a = Ad_array(3, 0)
b = Ad_array(2, 0)
c = a * b
self.assertTrue(c.val == 6 and c.jac == 0)
self.assertTrue(a.val == 3 and a.jac == 0)
self.assertTrue(b.val == 2 and b.jac == 0)
def test_mul_ad_var_ad_var(self):
a = Ad_array(3, 3)
b = Ad_array(2, -4)
c = a * b
self.assertTrue(c.val == 6 and c.jac == -6)
self.assertTrue(a.val == 3 and a.jac == 3)
self.assertTrue(b.val == 2 and b.jac == -4)
def apply(self, mobility_inner, direction_inner, mobility_bound,
direction_bound):
"""Compute transmissibility via upwinding over faces. Use monotonicityexpr for
deciding directionality.
Idea: 'face value' = 'left cell value' * Heaviside('flux from left')
+ 'right cell value' * Heaviside('flux from right').
"""
# TODO only implemented for scalar relative permeabilities so far
# TODO so far not for periodic bondary conditions.
# Rename internal properties
hs = self._heaviside
cf_inner = self._cf_inner
cf_is_dir = self._cf_is_dir
# Determine direction-determining cell values to the left(0) and right(1) of each face.
# Use Dirichlet boundary data where suitable.
# Neglect Neumann boundaries since face transmissibilities at Neumann boundary data
# anyhow does not play a role.
# Determine the Jacobian manually - only in the interior of the domain.
if isinstance(direction_inner, Ad_array):
dir_f_val = [
cf_inner[i] * direction_inner.val for i in range(0, 2)
]
for i in range(0, 2):
dir_f_val[i][cf_is_dir[i]] = direction_bound[cf_is_dir[i]]
dir_f_jac = [
cf_inner[i] * direction_inner.jac for i in range(0, 2)
]
dir_f = [Ad_array(dir_f_val[i], dir_f_jac[i]) for i in range(0, 2)]
else:
dir_f = [cf_inner[i] * direction_inner for i in range(0, 2)]
for i in range(0, 2):
dir_f[i][cf_is_dir[i]] = direction_bound[cf_is_dir[i]]
# Do the same for the mobility as for the direction-determining arrays.
if isinstance(mobility_inner, Ad_array):
mob_f_val = [cf_inner[i] * mobility_inner.val for i in range(0, 2)]
for i in range(0, 2):
mob_f_val[i][cf_is_dir[i]] = mobility_bound[cf_is_dir[i]]
mob_f_jac = [cf_inner[i] * mobility_inner.jac for i in range(0, 2)]
mob_f = [Ad_array(mob_f_val[i], mob_f_jac[i]) for i in range(0, 2)]
else:
mob_f = [cf_inner[i] * mobility_inner for i in range(0, 2)]
for i in range(0, 2):
mob_f[i][cf_is_dir[i]] = mobility_bound[cf_is_dir[i]]
# Evaluate the Heaviside function of the "flux directions".
hs_f_01 = hs(dir_f[0] - dir_f[1])
hs_f_10 = hs(dir_f[1] - dir_f[0])
# Determine the face mobility by utilizing the general idea (see above).
face_mobility = mob_f[0] * hs_f_01 + mob_f[1] * hs_f_10
return face_mobility
def _apply_ad(self, cellwise_field) -> Ad_array:
""" Compute transmissibility via harmonic averaging over faces."""
# References to private variables
data = self._data
tpfa = self._tpfa
if data.get("Aavatsmark_transmissibilities", False):
raise RuntimeError(
"AD version of Aavatsmark_transmissibilities not implemented.")
# Get connectivity and grid based data
ci = tpfa.ci
ci_periodic = tpfa.ci_periodic
fc_cc = tpfa.fc_cc
dist_face_cell = np.power(np.power(fc_cc, 2).sum(axis=0), 0.5)
# Consider two cases: scalar and tensor valued fields.
# assert (cellwise_field.val, np.ndarray)
# Case 1: Scalar valued fields.
if len(cellwise_field.val.shape) == 1:
t_cf_val = cellwise_field.val[ci]
t_cf_jac = cellwise_field.jac[ci]
t_cf_val /= dist_face_cell
t_cf_jac /= dist_face_cell
# Case 2: Tensor valued fields.
elif len(cellwise_field.val.shape) == 3 and all(
[cellwise_field.val.shape[i] == 3 for i in range(0, 2)]):
t_cf_tensor_val = cellwise_field.val[::, ::, ci]
t_cf_tensor_jac = cellwise_field.jac[::, ::, ci]
tn_cf_val = (t_cf_tensor_val * fc_cc).sum(axis=1)
tn_cf_jac = (t_cf_tensor_jac * fc_cc).sum(axis=1)
ntn_cf_val = (tn_cf_val * fc_cc).sum(axis=0)
ntn_cf_jac = (tn_cf_jac * fc_cc).sum(axis=0)
dist_face_cell_3 = np.power(dist_face_cell, 3)
t_cf_val = np.divide(ntn_cf_val, dist_face_cell_3)
t_cf_jac = np.divide(ntn_cf_jac, dist_face_cell_3)
else:
raise RuntimeError("Type of cell-wise field not supported.")
# Continue with AD representation and utilize chain rule.
t_face = Ad_array(t_cf_val, sps.diags(t_cf_jac).tocsc())
# The final harmonic averaging using a linear operator representation of bincount.
# TODO test!
t_face = (self.bincount_fi_periodic * dist_face_cell) * (
(self.bincount_fi_periodic * t_face**(-1))**(-1))
# Project column space of t.jac onto the actual cell
# TODO is there not a better way to create the projection matrix? By correct indexing?
c = np.arange(len(ci_periodic))
proj = sps.coo_matrix((np.ones_like(c), (c, ci_periodic))).tocsr()
t_face.jac = t_face.jac * proj
return t_face
def test_copy_vector(self):
a = Ad_array(np.ones((3, 1)), np.ones((3, 1)))
b = a.copy()
self.assertTrue(np.allclose(a.val, b.val))
self.assertTrue(np.allclose(a.jac, b.jac))
a.val[0] = 3
a.jac[2] = 4
self.assertTrue(np.allclose(b.val, np.ones((3, 1))))
self.assertTrue(np.allclose(b.jac, np.ones((3, 1))))
def test_copy_scalar(self):
a = Ad_array(1, 0)
b = a.copy()
self.assertTrue(a.val == b.val)
self.assertTrue(a.jac == b.jac)
a.val = 2
a.jac = 3
self.assertTrue(b.val == 1)
self.assertTrue(b.jac == 0)
def test_add_var_with_scal(self):
a = Ad_array(3, 2)
b = 3
c = a + b
self.assertTrue(np.allclose(c.val, 6) and np.allclose(c.jac, 2))
self.assertTrue(a.val == 3 and np.allclose(a.jac, 2))
self.assertTrue(b == 3)
def test_sign_advar(self):
a = Ad_array(np.array([1, -10, 3, -np.pi]), np.eye(4))
sign = af.sign(a)
self.assertTrue(np.all(sign == [1, -1, 1, -1]))
self.assertTrue(
np.allclose(a.val, [1, -10, 3, -np.pi])
and np.allclose(a.jac, np.eye(4)))
def test_mul_scar_ad_var(self):
a = Ad_array(3, 3)
b = 3
c = b * a
self.assertTrue(c.val == 9 and c.jac == 9)
self.assertTrue(a.val == 3 and a.jac == 3)
self.assertTrue(b == 3)
def test_log_sparse_jac(self):
val = np.array([1, 2, 3])
J = sps.csc_matrix(np.array([[3, 2, 1], [5, 6, 1], [2, 3, 5]]))
a = Ad_array(val, J)
b = af.log(a)
jac = np.dot(np.diag(1 / val), J.A)
self.assertTrue(np.all(b.val == np.log(val)) and np.all(b.jac == jac))
def test_sub_scal_with_var(self):
a = Ad_array(3, 2)
b = 3
c = b - a
self.assertTrue(np.allclose(c.val, 0) and np.allclose(c.jac, -2))
self.assertTrue(a.val == 3 and a.jac == 2)
self.assertTrue(b == 3)
def test_mul_sps_advar(self):
J = sps.csc_matrix(np.array([[1, 3, 1], [5, 0, 0], [5, 1, 2]]))
x = Ad_array(np.array([1, 2, 3]), J)
A = sps.csc_matrix(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
f = A * x
self.assertTrue(np.all(f.val == [14, 32, 50]))
self.assertTrue(np.all(f.jac == A * J.A))
def test_full_jac(self):
J1 = sps.csc_matrix(
np.array([[1, 3, 5], [1, 5, 1], [6, 2, 4], [2, 4, 1], [6, 2, 1]]))
J2 = sps.csc_matrix(np.array([[1, 2], [2, 5], [6, 0], [9, 9], [45,
2]]))
J = np.array([
[1, 3, 5, 1, 2],
[1, 5, 1, 2, 5],
[6, 2, 4, 6, 0],
[2, 4, 1, 9, 9],
[6, 2, 1, 45, 2],
])
a = Ad_array(np.array([1, 2, 3, 4, 5]),
J.copy()) # np.array([J1, J2]))
self.assertTrue(np.sum(a.full_jac() != J) == 0)
def test_mul_advar_vectors(self):
Ja = sps.csc_matrix(np.array([[1, 3, 1], [5, 0, 0], [5, 1, 2]]))
Jb = sps.csc_matrix(np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]))
a = Ad_array(np.array([1, 2, 3]), Ja)
b = Ad_array(np.array([1, 1, 1]), Jb)
A = sps.csc_matrix(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
f = A * a + b
jac = A * Ja + Jb
self.assertTrue(np.all(f.val == [15, 33, 51]))
self.assertTrue(np.sum(f.full_jac() != A * Ja + Jb) == 0)
self.assertTrue(
np.sum(Ja != sps.csc_matrix(
np.array([[1, 3, 1], [5, 0, 0], [5, 1, 2]]))) == 0)
self.assertTrue(
np.sum(Jb != sps.csc_matrix(
np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]))) == 0)
def test_log_vector(self):
val = np.array([1, 2, 3])
J = sps.csc_matrix(np.array([[3, 2, 1], [5, 6, 1], [2, 3, 5]]))
a = Ad_array(val, J)
b = af.log(a)
jac = sps.diags(1 / val) * J
self.assertTrue(
np.all(b.val == np.log(val)) and np.all(b.jac.A == jac))
def concatenate(variables, axis=0):
vals = [var.val for var in variables]
jacs = np.array([var.jac for var in variables])
vals_stacked = np.concatenate(vals, axis=axis)
jacs_stacked = []
jacs_stacked = sps.vstack(jacs)
return Ad_array(vals_stacked, jacs_stacked)
def test_exp_vector(self):
val = np.array([1, 2, 3])
J = np.array([[3, 2, 1], [5, 6, 1], [2, 3, 5]])
a = Ad_array(val, sps.csc_matrix(J))
b = af.exp(a)
jac = np.dot(np.diag(np.exp(val)), J)
self.assertTrue(np.all(b.val == np.exp(val)) and np.all(b.jac == jac))
self.assertTrue(
np.all(J == np.array([[3, 2, 1], [5, 6, 1], [2, 3, 5]])))
def _ad_apply(self, face_transmissibility, potential, bc):
tpfa = self._tpfa
# Inner contribution
matrix_dictionary = tpfa.data[pp.DISCRETIZATION_MATRICES][tpfa.keyword]
flux_matrix = matrix_dictionary[tpfa.flux_matrix_key]
bound_flux_matrix = matrix_dictionary[tpfa.bound_flux_matrix_key]
# Compute flux and jacobian including bound flux data.
# Determine value of face transmissibilities.
if isinstance(face_transmissibility, Ad_array):
face_transmissibility_val = face_transmissibility.val
elif isinstance(face_transmissibility, np.ndarray):
face_transmissibility_val = face_transmissibility
else:
raise RuntimeError("Type not implemented.")
# How the flux and its jacobian are computed:
# flux.val = diag(face_transmissibility.val) * flux_matrix * potential.val
# = diag(flux_matrix * potential.val) * face_transmissibility.val; hence:
# Start assuming face_transmissibility is constant
flux_val = (sps.diags(face_transmissibility_val).tocsc() *
flux_matrix * potential.val)
flux_jac = (sps.diags(face_transmissibility_val).tocsc() *
flux_matrix * potential.jac)
# Boundary contribution - require some care with neumann boundary conditions.
# How the boundary flux and its jacobian is computed:
# bc_value = diag(t_b) * bound_flux_matrix * bc = diag(bound_flux_matrix * bc) * t_b
# For the jacobian use the latter formula.
t_b_val = face_transmissibility_val
# Enforce Neumann BCs by setting the transmissibility equal 1:
is_neu = tpfa.is_neu
t_b_val[is_neu] = 1
# flux_val += sps.diags(t_b_val).tocsc() * bound_flux_matrix * bc # TODO rm?
flux_val += t_b_val * (bound_flux_matrix * bc)
# Now account in the Jacobian for nonlinear face_transmissibility
if isinstance(face_transmissibility, Ad_array):
flux_jac += (sps.diags(flux_matrix * potential.val) *
face_transmissibility.jac)
# Boundary contribution - require some care with neumann boundary conditions
t_b_jac = face_transmissibility.jac
# Enforce Neumann BCs by setting the transmissibility equal 1:
neumann_rows = np.arange(len(t_b_val))[is_neu]
pp.utils.sparse_mat.zero_rows(t_b_jac, neumann_rows)
flux_jac += sps.diags(bound_flux_matrix * bc) * t_b_jac
return Ad_array(flux_val, flux_jac)
def test_advar_mul_vec(self):
x = Ad_array(np.array([1, 2, 3]), sps.diags([3, 2, 1]))
A = np.array([1, 3, 10])
f = x * A
sol = np.array([1, 6, 30])
jac = np.diag([3, 6, 10])
self.assertTrue(np.all(f.val == sol) and np.all(f.jac == jac))
self.assertTrue(
np.all(x.val == np.array([1, 2, 3]))
and np.all(x.jac == np.diag([3, 2, 1])))
def tanh(var):
if isinstance(var, Ad_array):
val = np.tanh(var.val)
jac = sps.diags(1 - np.tanh(var.val) ** 2).tocsc() * var.jac
return Ad_array(val, jac)
elif isinstance(var, Local_Ad_array):
val = np.tanh(var.val)
jac = (1 - np.tanh(var.val) ** 2) * var.jac
return Local_Ad_array(val, jac)
else:
return np.tanh(var)
def abs(var):
if isinstance(var, Ad_array):
val = np.abs(var.val)
jac = var.diagvec_mul_jac(sign(var))
return Ad_array(val, jac)
elif isinstance(var, Local_Ad_array):
val = np.abs(var.val)
jac = sign(var) * var.jac
return Local_Ad_array(val, jac)
else:
return np.abs(var)
def log(var):
if isinstance(var, Ad_array):
val = np.log(var.val)
der = var.diagvec_mul_jac(1 / var.val)
return Ad_array(val, der)
elif isinstance(var, Local_Ad_array):
val = np.log(var.val)
der = (1 / var.val) * var.jac
return Local_Ad_array(val, der)
else:
return np.log(var)
def test_advar_m_mul_vec_n(self):
x = Ad_array(np.array([1, 2, 3]), sps.diags([3, 2, 1]))
vec = np.array([1, 2])
R = sps.csc_matrix(np.array([[1, 0, 1], [0, 1, 0]]))
y = R * x
z = y * vec
Jy = np.array([[1, 0, 3], [0, 2, 0]])
Jz = np.array([[1, 0, 3], [0, 4, 0]])
self.assertTrue(np.all(y.val == [4, 2]))
self.assertTrue(np.sum(y.full_jac().A - Jy) == 0)
self.assertTrue(np.all(z.val == [4, 4]))
self.assertTrue(np.sum(z.full_jac().A - Jz) == 0)
def cos(var):
if isinstance(var, Ad_array):
val = np.cos(var.val)
# TODO use capabilties offered by forward_mode.py
jac = -sps.diags(np.sin(var.val)).tocsc() * var.jac
return Ad_array(val, jac)
elif isinstance(var, Local_Ad_array):
val = np.cos(var.val)
jac = -np.sin(var.val) * var.jac
return Local_Ad_array(val, jac)
else:
return np.cos(var)
def test_log_scalar_times_ad_var(self):
val = np.array([1, 2, 3])
J = sps.diags(np.array([1, 1, 1]))
a = Ad_array(val, J)
c = 2
b = af.log(c * a)
jac = sps.diags(1 / val) * J
self.assertTrue(
np.allclose(b.val, np.log(c * val))
and np.allclose(b.jac.A, jac.A))
self.assertTrue(np.all(a.val == [1, 2, 3]) and np.all(a.jac.A == J.A))
def test_rpower_advar_vector_scalar(self):
J = sps.csc_matrix(np.array([[1, 2], [2, 3], [0, 1]]))
a = Ad_array(np.array([1, 2, 3]), J)
b = 3**a
bJac = np.array([
[3 * np.log(3) * 1, 3 * np.log(3) * 2],
[9 * np.log(3) * 2, 9 * np.log(3) * 3],
[27 * np.log(3) * 0, 27 * np.log(3) * 1],
])
self.assertTrue(np.all(b.val == [3, 9, 27]))
self.assertTrue(np.all(b.jac.A == bJac))
def __call__(self, var, zerovalue: float = 0.5):
if isinstance(var, Ad_array):
val = np.heaviside(var.val, 0.0)
regularization = self._regularization(var)
jac = regularization.jac
return Ad_array(val, jac)
elif isinstance(var, Local_Ad_array):
val = np.heaviside(var.val, 0.0)
regularization = self._regularization(var)
jac = regularization.jac
return Local_Ad_array(val, jac)
else:
return np.heaviside(var)
