def _prox(self, x, T):
# Time counter
t_init = time()
tol = self.tol
maxit = self.maxit
# TODO implement test_gamma
# Initialization
sol = x
if self.dim == 1:
r = op.grad(x * 0, dim=self.dim, **self.kwargs)
rr = deepcopy(r)
elif self.dim == 2:
r, s = op.grad(x * 0, dim=self.dim, **self.kwargs)
rr, ss = deepcopy(r), deepcopy(s)
elif self.dim == 3:
r, s, k = op.grad(x * 0, dim=self.dim, **self.kwargs)
rr, ss, kk = deepcopy(r), deepcopy(s), deepcopy(k)
elif self.dim == 4:
r, s, k, u = op.grad(x * 0, dim=self.dim, **self.kwargs)
rr, ss, kk, uu = deepcopy(r), deepcopy(s), deepcopy(k), deepcopy(u)
if self.dim >= 1:
pold = r
if self.dim >= 2:
qold = s
if self.dim >= 3:
kold = k
if self.dim >= 4:
uold = u
told, prev_obj = 1., 0.
# Initialization for weights
if self.dim >= 1:
try:
wx = self.kwargs["wx"]
except (KeyError, TypeError):
wx = 1.
if self.dim >= 2:
try:
wy = self.kwargs["wy"]
except (KeyError, TypeError):
wy = 1.
if self.dim >= 3:
try:
wz = self.kwargs["wz"]
except (KeyError, TypeError):
wz = 1.
if self.dim >= 4:
try:
wt = self.kwargs["wt"]
except (KeyError, TypeError):
wt = 1.
if self.dim == 1:
mt = wx
elif self.dim == 2:
mt = np.maximum(wx, wy)
elif self.dim == 3:
mt = np.maximum(wx, np.maximum(wy, wz))
elif self.dim == 4:
mt = np.maximum(np.maximum(wx, wy), np.maximum(wz, wt))
if self.verbosity in ['LOW', 'HIGH', 'ALL']:
print("Proximal TV Operator")
iter = 0
while iter <= maxit:
# Current Solution
if self.dim == 1:
sol = x - T * op.div(rr, **self.kwargs)
elif self.dim == 2:
sol = x - T * op.div(rr, ss, **self.kwargs)
elif self.dim == 3:
sol = x - T * op.div(rr, ss, kk, **self.kwargs)
elif self.dim == 4:
sol = x - T * op.div(rr, ss, kk, uu, **self.kwargs)
# Objective function value
obj = 0.5 * np.power(np.linalg.norm(x[:] - sol[:]), 2) + \
T * np.sum(self._eval(sol), axis=0)
rel_obj = np.abs(obj - prev_obj) / obj
prev_obj = obj
if self.verbosity in ['HIGH', 'ALL']:
print("Iter: ", iter, " obj = ", obj, " rel_obj = ", rel_obj)
# Stopping criterion
if rel_obj < tol:
crit = "TOL_EPS"
break
# Update divergence vectors and project
if self.dim == 1:
dx = op.grad(sol, dim=self.dim, **self.kwargs)
r -= 1. / (4 * T * mt**2) * dx
weights = np.maximum(1, np.abs(r))
elif self.dim == 2:
dx, dy = op.grad(sol, dim=self.dim, **self.kwargs)
r -= (1. / (8. * T * mt**2.)) * dx
s -= (1. / (8. * T * mt**2.)) * dy
weights = np.maximum(
1,
np.sqrt(np.power(np.abs(r), 2) + np.power(np.abs(s), 2)))
elif self.dim == 3:
dx, dy, dz = op.grad(sol, dim=self.dim, **self.kwargs)
r -= 1. / (12. * T * mt**2) * dx
s -= 1. / (12. * T * mt**2) * dy
k -= 1. / (12. * T * mt**2) * dz
weights = np.maximum(
1,
np.sqrt(
np.power(np.abs(r), 2) + np.power(np.abs(s), 2) +
np.power(np.abs(k), 2)))
elif self.dim == 4:
dx, dy, dz, dt = op.grad(sol, dim=self.dim, **self.kwargs)
r -= 1. / (16 * T * mt**2) * dx
s -= 1. / (16 * T * mt**2) * dy
k -= 1. / (16 * T * mt**2) * dz
u -= 1. / (16 * T * mt**2) * dt
weights = np.maximum(
1,
np.sqrt(
np.power(np.abs(r), 2) + np.power(np.abs(s), 2) +
np.power(np.abs(k), 2) + np.power(np.abs(u), 2)))
# FISTA update
t = (1 + np.sqrt(4 * told**2)) / 2.
if self.dim >= 1:
p = r / weights
r = p + (told - 1) / t * (p - pold)
pold = p
rr = deepcopy(r)
if self.dim >= 2:
q = s / weights
s = q + (told - 1) / t * (q - qold)
ss = deepcopy(s)
qold = q
if self.dim >= 3:
o = k / weights
k = o + (told - 1) / t * (o - kold)
kk = deepcopy(k)
kold = o
if self.dim >= 4:
m = u / weights
u = m + (told - 1) / t * (m - uold)
uu = deepcopy(u)
uold = m
told = t
iter += 1
try:
type(crit) == str
except NameError:
crit = "MAX_IT"
t_end = time()
exec_time = t_end - t_init
if self.verbosity in ['HIGH', 'ALL']:
print("Prox_TV: obj = {0}, rel_obj = {1}, {2}, iter = {3}".format(
obj, rel_obj, crit, iter))
print("exec_time = ", exec_time)
return sol
def test_div(self):
# Sanity check
self.assertRaises(ValueError, operators.div)
# Divergence tests
# test with 1dim matrices
dx = np.array([1, 2, 3, 4, 5])
# test without weights
nptest.assert_array_equal(np.array([1, 1, 1, 1, -4]),
operators.div(dx))
# test with weights
weights = {'wx': 2, 'wy': 3, 'wz': 4, 'wt': 2}
nptest.assert_array_equal(np.array([2, 2, 2, 2, -8]),
operators.div(dx, **weights))
# test with 2dim matrices
dx = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
dy = np.array([[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]])
# test without weights
x_mat = np.array([[1, 2, 3, 4], [4, 4, 4, 4], [-5, -6, -7, -8]])
xy_mat = np.array([[14, 3, 4, -11], [21, 5, 5, -15], [16, -5, -6,
-31]])
nptest.assert_array_equal(x_mat, operators.div(dx))
nptest.assert_array_equal(xy_mat, operators.div(dx, dy))
# test with weights
xy_mat_w = np.array([[41, 7, 9, -37], [59, 11, 11, -49],
[53, -9, -11, -85]])
nptest.assert_array_equal(xy_mat_w, operators.div(dx, dy, **weights))
# test with 3dim matrices (3x3x3)
dx = np.array([[[1, 10, 19], [2, 11, 20], [3, 12, 21]],
[[4, 13, 22], [5, 14, 23], [6, 15, 24]],
[[7, 16, 25], [8, 17, 26], [9, 18, 27]]])
dy = np.array([[[1, 10, 19], [2, 11, 20], [3, 12, 21]],
[[4, 13, 22], [5, 14, 23], [6, 15, 24]],
[[7, 16, 25], [8, 17, 26], [9, 18, 27]]])
dz = np.array([[[1, 10, 19], [2, 11, 20], [3, 12, 21]],
[[4, 13, 22], [5, 14, 23], [6, 15, 24]],
[[7, 16, 25], [8, 17, 26], [9, 18, 27]]])
# test without weights
x_mat = np.array([[[1, 10, 19], [2, 11, 20], [3, 12, 21]],
[[3, 3, 3], [3, 3, 3], [3, 3, 3]],
[[-4, -13, -22], [-5, -14, -23], [-6, -15, -24]]])
xy_mat = np.array([[[2, 20, 38], [3, 12, 21], [1, 1, 1]],
[[7, 16, 25], [4, 4, 4], [-2, -11, -20]],
[[3, 3, 3], [-4, -13, -22], [-14, -32, -50]]])
xyz_mat = np.array([[[3, 29, 28], [5, 21, 10], [4, 10, -11]],
[[11, 25, 12], [9, 13, -10], [4, -2, -35]],
[[10, 12, -13], [4, -4, -39], [-5, -23, -68]]])
xyzt_mat = np.array([[[9, 86, 55], [15, 61, -1], [12, 27, -66]],
[[34, 81, 20], [29, 45, -47], [15, 0, -123]],
[[41, 58, -33], [25, 11, -111], [0, -45, -198]]])
nptest.assert_array_equal(x_mat, operators.div(dx))
nptest.assert_array_equal(xy_mat, operators.div(dx, dy))
nptest.assert_array_equal(xyz_mat, operators.div(dx, dy, dz))
# test with weights
xyz_mat_w = np.array([[[9, 86, 55], [15, 61, -1], [12, 27, -66]],
[[34, 81, 20], [29, 45, -47], [15, 0, -123]],
[[41, 58, -33], [25, 11, -111], [0, -45, -198]]])
nptest.assert_array_equal(xyz_mat_w,
operators.div(dx, dy, dz, **weights))
# test with 4d matrices (3x3x3x3)
dx = np.array([[[[1, 28, 55], [10, 37, 64], [19, 46, 73]],
[[2, 29, 56], [11, 38, 65], [20, 47, 74]],
[[3, 30, 57], [12, 39, 66], [21, 48, 75]]],
[[[4, 31, 58], [13, 40, 67], [22, 49, 76]],
[[5, 32, 59], [14, 41, 68], [23, 50, 77]],
[[6, 33, 60], [15, 42, 69], [24, 51, 78]]],
[[[7, 34, 61], [16, 43, 70], [25, 52, 79]],
[[8, 35, 62], [17, 44, 71], [26, 53, 80]],
[[9, 36, 63], [18, 45, 72], [27, 54, 81]]]])
dy = np.array([[[[1, 28, 55], [10, 37, 64], [19, 46, 73]],
[[2, 29, 56], [11, 38, 65], [20, 47, 74]],
[[3, 30, 57], [12, 39, 66], [21, 48, 75]]],
[[[4, 31, 58], [13, 40, 67], [22, 49, 76]],
[[5, 32, 59], [14, 41, 68], [23, 50, 77]],
[[6, 33, 60], [15, 42, 69], [24, 51, 78]]],
[[[7, 34, 61], [16, 43, 70], [25, 52, 79]],
[[8, 35, 62], [17, 44, 71], [26, 53, 80]],
[[9, 36, 63], [18, 45, 72], [27, 54, 81]]]])
dz = np.array([[[[1, 28, 55], [10, 37, 64], [19, 46, 73]],
[[2, 29, 56], [11, 38, 65], [20, 47, 74]],
[[3, 30, 57], [12, 39, 66], [21, 48, 75]]],
[[[4, 31, 58], [13, 40, 67], [22, 49, 76]],
[[5, 32, 59], [14, 41, 68], [23, 50, 77]],
[[6, 33, 60], [15, 42, 69], [24, 51, 78]]],
[[[7, 34, 61], [16, 43, 70], [25, 52, 79]],
[[8, 35, 62], [17, 44, 71], [26, 53, 80]],
[[9, 36, 63], [18, 45, 72], [27, 54, 81]]]])
dt = np.array([[[[1, 28, 55], [10, 37, 64], [19, 46, 73]],
[[2, 29, 56], [11, 38, 65], [20, 47, 74]],
[[3, 30, 57], [12, 39, 66], [21, 48, 75]]],
[[[4, 31, 58], [13, 40, 67], [22, 49, 76]],
[[5, 32, 59], [14, 41, 68], [23, 50, 77]],
[[6, 33, 60], [15, 42, 69], [24, 51, 78]]],
[[[7, 34, 61], [16, 43, 70], [25, 52, 79]],
[[8, 35, 62], [17, 44, 71], [26, 53, 80]],
[[9, 36, 63], [18, 45, 72], [27, 54, 81]]]])
# test without weights
x_mat = np.array([[[[1, 28, 55], [10, 37, 64], [19, 46, 73]],
[[2, 29, 56], [11, 38, 65], [20, 47, 74]],
[[3, 30, 57], [12, 39, 66], [21, 48, 75]]],
[[[3, 3, 3], [3, 3, 3], [3, 3, 3]],
[[3, 3, 3], [3, 3, 3], [3, 3, 3]],
[[3, 3, 3], [3, 3, 3], [3, 3, 3]]],
[[[-4, -31, -58], [-13, -40, -67], [-22, -49, -76]],
[[-5, -32, -59], [-14, -41, -68], [-23, -50, -77]],
[[-6, -33, -60], [-15, -42, -69], [-24, -51,
-78]]]])
xy_mat = np.array([[[[2, 56, 110], [20, 74, 128], [38, 92, 146]],
[[3, 30, 57], [12, 39, 66], [21, 48, 75]],
[[1, 1, 1], [1, 1, 1], [1, 1, 1]]],
[[[7, 34, 61], [16, 43, 70], [25, 52, 79]],
[[4, 4, 4], [4, 4, 4], [4, 4, 4]],
[[-2, -29, -56], [-11, -38, -65], [-20, -47,
-74]]],
[[[3, 3, 3], [3, 3, 3], [3, 3, 3]],
[[-4, -31, -58], [-13, -40, -67], [-22, -49, -76]],
[[-14, -68, -122], [-32, -86, -140],
[-50, -104, -158]]]])
xyz_mat = np.array([[[[3, 84, 165], [29, 83, 137], [28, 55, 82]],
[[5, 59, 113], [21, 48, 75], [10, 10, 10]],
[[4, 31, 58], [10, 10, 10], [-11, -38, -65]]],
[[[11, 65, 119], [25, 52, 79], [12, 12, 12]],
[[9, 36, 63], [13, 13, 13], [-10, -37, -64]],
[[4, 4, 4], [-2, -29, -56], [-35, -89, -143]]],
[[[10, 37, 64], [12, 12, 12], [-13, -40, -67]],
[[4, 4, 4], [-4, -31, -58], [-39, -93, -147]],
[[-5, -32, -59], [-23, -77, -131],
[-68, -149, -230]]]])
xyzt_mat = np.array([[[[4, 111, 137], [39, 110, 100], [47, 82, 36]],
[[7, 86, 84], [32, 75, 37], [30, 37, -37]],
[[7, 58, 28], [22, 37, -29], [10, -11, -113]]],
[[[15, 92, 88], [38, 79, 39], [34, 39, -37]],
[[14, 63, 31], [27, 40, -28], [13, -10, -114]],
[[10, 31, -29], [13, -2, -98], [-11, -62,
-194]]],
[[[17, 64, 30], [28, 39, -31], [12, -13, -119]],
[[12, 31, -31], [13, -4, -102], [-13, -66,
-200]],
[[4, -5, -95], [-5, -50, -176],
[-41, -122, -284]]]])
nptest.assert_array_equal(x_mat, operators.div(dx))
nptest.assert_array_equal(xy_mat, operators.div(dx, dy))
nptest.assert_array_equal(xyz_mat, operators.div(dx, dy, dz))
nptest.assert_array_equal(xyzt_mat, operators.div(dx, dy, dz, dt))
# test with weights
xyzt_mat_w = np.array([[[[11, 306, 439], [106, 275, 282],
[93, 136, 17]],
[[19, 231, 281], [83, 169, 93], [39, -1,
-203]],
[[18, 147, 114], [51, 54, -105],
[-24, -147, -432]]],
[[[42, 277, 350], [107, 216, 163],
[64, 47, -132]],
[[39, 191, 181], [73, 99, -37],
[-1, -101, -363]],
[[27, 96, 3], [30, -27, -246],
[-75, -258, -603]]],
[[[55, 230, 243], [90, 139, 26],
[17, -60, -299]],
[[41, 133, 63], [45, 11, -185],
[-59, -219, -541]],
[[18, 27, -126], [-9, -126, -405],
[-144, -387, -792]]]])
nptest.assert_array_equal(xyzt_mat_w,
operators.div(dx, dy, dz, dt, **weights))
def _prox(self, x, T):
# Time counter
t_init = time()
tol = self.tol
maxit = self.maxit
# TODO implement test_gamma
# Initialization
sol = x
if self.dim == 1:
r = op.grad(x * 0, dim=self.dim, **self.kwargs)
rr = deepcopy(r)
elif self.dim == 2:
r, s = op.grad(x * 0, dim=self.dim, **self.kwargs)
rr, ss = deepcopy(r), deepcopy(s)
elif self.dim == 3:
r, s, k = op.grad(x * 0, dim=self.dim, **self.kwargs)
rr, ss, kk = deepcopy(r), deepcopy(s), deepcopy(k)
elif self.dim == 4:
r, s, k, u = op.grad(x * 0, dim=self.dim, **self.kwargs)
rr, ss, kk, uu = deepcopy(r), deepcopy(s), deepcopy(k), deepcopy(u)
if self.dim >= 1:
pold = r
if self.dim >= 2:
qold = s
if self.dim >= 3:
kold = k
if self.dim >= 4:
uold = u
told, prev_obj = 1., 0.
# Initialization for weights
if self.dim >= 1:
try:
wx = self.kwargs["wx"]
except (KeyError, TypeError):
wx = 1.
if self.dim >= 2:
try:
wy = self.kwargs["wy"]
except (KeyError, TypeError):
wy = 1.
if self.dim >= 3:
try:
wz = self.kwargs["wz"]
except (KeyError, TypeError):
wz = 1.
if self.dim >= 4:
try:
wt = self.kwargs["wt"]
except (KeyError, TypeError):
wt = 1.
if self.dim == 1:
mt = wx
elif self.dim == 2:
mt = np.maximum(wx, wy)
elif self.dim == 3:
mt = np.maximum(wx, np.maximum(wy, wz))
elif self.dim == 4:
mt = np.maximum(np.maximum(wx, wy), np.maximum(wz, wt))
if self.verbosity in ['LOW', 'HIGH', 'ALL']:
print("Proximal TV Operator")
iter = 0
while iter <= maxit:
# Current Solution
if self.dim == 1:
sol = x - T * op.div(rr, **self.kwargs)
elif self.dim == 2:
sol = x - T * op.div(rr, ss, **self.kwargs)
elif self.dim == 3:
sol = x - T * op.div(rr, ss, kk, **self.kwargs)
elif self.dim == 4:
sol = x - T * op.div(rr, ss, kk, uu, **self.kwargs)
# Objective function value
obj = 0.5 * np.power(np.linalg.norm(x[:] - sol[:]), 2) + \
T * np.sum(self._eval(sol), axis=0)
rel_obj = np.abs(obj - prev_obj) / obj
prev_obj = obj
if self.verbosity in ['HIGH', 'ALL']:
print("Iter: ", iter, " obj = ", obj, " rel_obj = ", rel_obj)
# Stopping criterion
if rel_obj < tol:
crit = "TOL_EPS"
break
# Update divergence vectors and project
if self.dim == 1:
dx = op.grad(sol, dim=self.dim, **self.kwargs)
r -= 1. / (4 * T * mt**2) * dx
weights = np.maximum(1, np.abs(r))
elif self.dim == 2:
dx, dy = op.grad(sol, dim=self.dim, **self.kwargs)
r -= (1. / (8. * T * mt**2.)) * dx
s -= (1. / (8. * T * mt**2.)) * dy
weights = np.maximum(1, np.sqrt(np.power(np.abs(r), 2) +
np.power(np.abs(s), 2)))
elif self.dim == 3:
dx, dy, dz = op.grad(sol, dim=self.dim, **self.kwargs)
r -= 1. / (12. * T * mt**2) * dx
s -= 1. / (12. * T * mt**2) * dy
k -= 1. / (12. * T * mt**2) * dz
weights = np.maximum(1, np.sqrt(np.power(np.abs(r), 2) +
np.power(np.abs(s), 2) +
np.power(np.abs(k), 2)))
elif self.dim == 4:
dx, dy, dz, dt = op.grad(sol, dim=self.dim, **self.kwargs)
r -= 1. / (16 * T * mt**2) * dx
s -= 1. / (16 * T * mt**2) * dy
k -= 1. / (16 * T * mt**2) * dz
u -= 1. / (16 * T * mt**2) * dt
weights = np.maximum(1, np.sqrt(np.power(np.abs(r), 2) +
np.power(np.abs(s), 2) +
np.power(np.abs(k), 2) +
np.power(np.abs(u), 2)))
# FISTA update
t = (1 + np.sqrt(4 * told**2)) / 2.
if self.dim >= 1:
p = r / weights
r = p + (told - 1) / t * (p - pold)
pold = p
rr = deepcopy(r)
if self.dim >= 2:
q = s / weights
s = q + (told - 1) / t * (q - qold)
ss = deepcopy(s)
qold = q
if self.dim >= 3:
o = k / weights
k = o + (told - 1) / t * (o - kold)
kk = deepcopy(k)
kold = o
if self.dim >= 4:
m = u / weights
u = m + (told - 1) / t * (m - uold)
uu = deepcopy(u)
uold = m
told = t
iter += 1
try:
type(crit) == str
except NameError:
crit = "MAX_IT"
t_end = time()
exec_time = t_end - t_init
if self.verbosity in ['HIGH', 'ALL']:
print("Prox_TV: obj = {0}, rel_obj = {1}, {2}, iter = {3}".format(
obj, rel_obj, crit, iter))
print("exec_time = ", exec_time)
return sol
def test_div(self):
# Sanity check
self.assertRaises(ValueError, operators.div)
# Divergence tests
# test with 1dim matrices
dx = np.array([1, 2, 3, 4, 5])
# test without weights
nptest.assert_array_equal(np.array([1, 1, 1, 1, -4]),
operators.div(dx))
# test with weights
weights = {'wx': 2, 'wy': 3, 'wz': 4, 'wt': 2}
nptest.assert_array_equal(np.array([2, 2, 2, 2, -8]),
operators.div(dx, **weights))
# test with 2dim matrices
dx = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
dy = np.array([[13, 14, 15, 16],
[17, 18, 19, 20],
[21, 22, 23, 24]])
# test without weights
x_mat = np.array([[1, 2, 3, 4], [4, 4, 4, 4], [-5, -6, -7, -8]])
xy_mat = np.array([[14, 3, 4, -11],
[21, 5, 5, -15],
[16, -5, -6, -31]])
nptest.assert_array_equal(x_mat, operators.div(dx))
nptest.assert_array_equal(xy_mat, operators.div(dx, dy))
# test with weights
xy_mat_w = np.array([[41, 7, 9, -37],
[59, 11, 11, -49],
[53, -9, -11, -85]])
nptest.assert_array_equal(xy_mat_w, operators.div(dx, dy, **weights))
# test with 3dim matrices (3x3x3)
dx = np.array([[[1, 10, 19], [2, 11, 20], [3, 12, 21]],
[[4, 13, 22], [5, 14, 23], [6, 15, 24]],
[[7, 16, 25], [8, 17, 26], [9, 18, 27]]])
dy = np.array([[[1, 10, 19], [2, 11, 20], [3, 12, 21]],
[[4, 13, 22], [5, 14, 23], [6, 15, 24]],
[[7, 16, 25], [8, 17, 26], [9, 18, 27]]])
dz = np.array([[[1, 10, 19], [2, 11, 20], [3, 12, 21]],
[[4, 13, 22], [5, 14, 23], [6, 15, 24]],
[[7, 16, 25], [8, 17, 26], [9, 18, 27]]])
# test without weights
x_mat = np.array([[[1, 10, 19], [2, 11, 20], [3, 12, 21]],
[[3, 3, 3], [3, 3, 3], [3, 3, 3]],
[[-4, -13, -22], [-5, -14, -23], [-6, -15, -24]]])
xy_mat = np.array([[[2, 20, 38], [3, 12, 21], [1, 1, 1]],
[[7, 16, 25], [4, 4, 4], [-2, -11, -20]],
[[3, 3, 3], [-4, -13, -22], [-14, -32, -50]]])
xyz_mat = np.array([[[3, 29, 28], [5, 21, 10], [4, 10, -11]],
[[11, 25, 12], [9, 13, -10], [4, -2, -35]],
[[10, 12, -13], [4, -4, -39], [-5, -23, -68]]])
xyzt_mat = np.array([[[9, 86, 55], [15, 61, -1], [12, 27, -66]],
[[34, 81, 20], [29, 45, -47], [15, 0, -123]],
[[41, 58, -33], [25, 11, -111], [0, -45, -198]]])
nptest.assert_array_equal(x_mat, operators.div(dx))
nptest.assert_array_equal(xy_mat, operators.div(dx, dy))
nptest.assert_array_equal(xyz_mat, operators.div(dx, dy, dz))
# test with weights
xyz_mat_w = np.array([[[9, 86, 55], [15, 61, -1], [12, 27, -66]],
[[34, 81, 20], [29, 45, -47], [15, 0, -123]],
[[41, 58, -33], [25, 11, -111], [0, -45, -198]]])
nptest.assert_array_equal(xyz_mat_w, operators.div(dx, dy, dz,
**weights))
# test with 4d matrices (3x3x3x3)
dx = np.array([[[[1, 28, 55], [10, 37, 64], [19, 46, 73]],
[[2, 29, 56], [11, 38, 65], [20, 47, 74]],
[[3, 30, 57], [12, 39, 66], [21, 48, 75]]],
[[[4, 31, 58], [13, 40, 67], [22, 49, 76]],
[[5, 32, 59], [14, 41, 68], [23, 50, 77]],
[[6, 33, 60], [15, 42, 69], [24, 51, 78]]],
[[[7, 34, 61], [16, 43, 70], [25, 52, 79]],
[[8, 35, 62], [17, 44, 71], [26, 53, 80]],
[[9, 36, 63], [18, 45, 72], [27, 54, 81]]]])
dy = np.array([[[[1, 28, 55], [10, 37, 64], [19, 46, 73]],
[[2, 29, 56], [11, 38, 65], [20, 47, 74]],
[[3, 30, 57], [12, 39, 66], [21, 48, 75]]],
[[[4, 31, 58], [13, 40, 67], [22, 49, 76]],
[[5, 32, 59], [14, 41, 68], [23, 50, 77]],
[[6, 33, 60], [15, 42, 69], [24, 51, 78]]],
[[[7, 34, 61], [16, 43, 70], [25, 52, 79]],
[[8, 35, 62], [17, 44, 71], [26, 53, 80]],
[[9, 36, 63], [18, 45, 72], [27, 54, 81]]]])
dz = np.array([[[[1, 28, 55], [10, 37, 64], [19, 46, 73]],
[[2, 29, 56], [11, 38, 65], [20, 47, 74]],
[[3, 30, 57], [12, 39, 66], [21, 48, 75]]],
[[[4, 31, 58], [13, 40, 67], [22, 49, 76]],
[[5, 32, 59], [14, 41, 68], [23, 50, 77]],
[[6, 33, 60], [15, 42, 69], [24, 51, 78]]],
[[[7, 34, 61], [16, 43, 70], [25, 52, 79]],
[[8, 35, 62], [17, 44, 71], [26, 53, 80]],
[[9, 36, 63], [18, 45, 72], [27, 54, 81]]]])
dt = np.array([[[[1, 28, 55], [10, 37, 64], [19, 46, 73]],
[[2, 29, 56], [11, 38, 65], [20, 47, 74]],
[[3, 30, 57], [12, 39, 66], [21, 48, 75]]],
[[[4, 31, 58], [13, 40, 67], [22, 49, 76]],
[[5, 32, 59], [14, 41, 68], [23, 50, 77]],
[[6, 33, 60], [15, 42, 69], [24, 51, 78]]],
[[[7, 34, 61], [16, 43, 70], [25, 52, 79]],
[[8, 35, 62], [17, 44, 71], [26, 53, 80]],
[[9, 36, 63], [18, 45, 72], [27, 54, 81]]]])
# test without weights
x_mat = np.array([[[[1, 28, 55], [10, 37, 64],
[19, 46, 73]],
[[2, 29, 56], [11, 38, 65],
[20, 47, 74]],
[[3, 30, 57], [12, 39, 66],
[21, 48, 75]]],
[[[3, 3, 3], [3, 3, 3],
[3, 3, 3]],
[[3, 3, 3], [3, 3, 3],
[3, 3, 3]],
[[3, 3, 3], [3, 3, 3],
[3, 3, 3]]],
[[[-4, -31, -58], [-13, -40, -67],
[-22, -49, -76]],
[[-5, -32, -59], [-14, -41, -68],
[-23, -50, -77]],
[[-6, -33, -60], [-15, -42, -69],
[-24, -51, -78]]]])
xy_mat = np.array([[[[2, 56, 110], [20, 74, 128],
[38, 92, 146]],
[[3, 30, 57], [12, 39, 66],
[21, 48, 75]],
[[1, 1, 1], [1, 1, 1],
[1, 1, 1]]],
[[[7, 34, 61], [16, 43, 70],
[25, 52, 79]],
[[4, 4, 4], [4, 4, 4],
[4, 4, 4]],
[[-2, -29, -56], [-11, -38, -65],
[-20, -47, -74]]],
[[[3, 3, 3], [3, 3, 3],
[3, 3, 3]],
[[-4, -31, -58], [-13, -40, -67],
[-22, -49, -76]],
[[-14, -68, -122], [-32, -86, -140],
[-50, -104, -158]]]])
xyz_mat = np.array([[[[3, 84, 165], [29, 83, 137],
[28, 55, 82]],
[[5, 59, 113], [21, 48, 75],
[10, 10, 10]],
[[4, 31, 58], [10, 10, 10],
[-11, -38, -65]]],
[[[11, 65, 119], [25, 52, 79],
[12, 12, 12]],
[[9, 36, 63], [13, 13, 13],
[-10, -37, -64]],
[[4, 4, 4], [-2, -29, -56],
[-35, -89, -143]]],
[[[10, 37, 64], [12, 12, 12],
[-13, -40, -67]],
[[4, 4, 4], [-4, -31, -58],
[-39, -93, -147]],
[[-5, -32, -59], [-23, -77, -131],
[-68, -149, -230]]]])
xyzt_mat = np.array([[[[4, 111, 137], [39, 110, 100],
[47, 82, 36]],
[[7, 86, 84], [32, 75, 37],
[30, 37, -37]],
[[7, 58, 28], [22, 37, -29],
[10, -11, -113]]],
[[[15, 92, 88], [38, 79, 39],
[34, 39, -37]],
[[14, 63, 31], [27, 40, -28],
[13, -10, -114]],
[[10, 31, -29], [13, -2, -98],
[-11, -62, -194]]],
[[[17, 64, 30], [28, 39, -31],
[12, -13, -119]],
[[12, 31, -31], [13, -4, -102],
[-13, -66, -200]],
[[4, -5, -95], [-5, -50, -176],
[-41, -122, -284]]]])
nptest.assert_array_equal(x_mat, operators.div(dx))
nptest.assert_array_equal(xy_mat, operators.div(dx, dy))
nptest.assert_array_equal(xyz_mat, operators.div(dx, dy, dz))
nptest.assert_array_equal(xyzt_mat, operators.div(dx, dy, dz, dt))
# test with weights
xyzt_mat_w = np.array([[[[11, 306, 439], [106, 275, 282],
[93, 136, 17]],
[[19, 231, 281], [83, 169, 93],
[39, -1, -203]],
[[18, 147, 114], [51, 54, -105],
[-24, -147, -432]]],
[[[42, 277, 350], [107, 216, 163],
[64, 47, -132]],
[[39, 191, 181], [73, 99, -37],
[-1, -101, -363]],
[[27, 96, 3], [30, -27, -246],
[-75, -258, -603]]],
[[[55, 230, 243], [90, 139, 26],
[17, -60, -299]],
[[41, 133, 63], [45, 11, -185],
[-59, -219, -541]],
[[18, 27, -126], [-9, -126, -405],
[-144, -387, -792]]]])
nptest.assert_array_equal(xyzt_mat_w, operators.div(dx, dy, dz, dt,
**weights))
def test_adjoint(self):
"""Test that 1D div op is the adjoint of grad."""
dx = operators.grad(np.eye(10), dim=1)
dxt = operators.div(np.eye(10))
nptest.assert_equal(dx.T, -dxt) # we use that definition
