def test_dimensions(self):
bm = BlockMatrix(2, 2)
self.assertTrue(bm.has_undefined_row_sizes())
self.assertTrue(bm.has_undefined_col_sizes())
with self.assertRaises(NotFullyDefinedBlockMatrixError):
shape = bm.shape
with self.assertRaises(NotFullyDefinedBlockMatrixError):
bm.set_block(0, 0, BlockMatrix(2, 2))
with self.assertRaises(NotFullyDefinedBlockMatrixError):
row_sizes = bm.row_block_sizes()
with self.assertRaises(NotFullyDefinedBlockMatrixError):
col_sizes = bm.col_block_sizes()
bm2 = BlockMatrix(2, 2)
bm2.set_block(0, 0, coo_matrix((2, 2)))
bm2.set_block(1, 1, coo_matrix((2, 2)))
bm3 = bm2.copy()
bm.set_block(0, 0, bm2)
bm.set_block(1, 1, bm3)
self.assertFalse(bm.has_undefined_row_sizes())
self.assertFalse(bm.has_undefined_col_sizes())
self.assertEqual(bm.shape, (8, 8))
bm.set_block(0, 0, None)
self.assertFalse(bm.has_undefined_row_sizes())
self.assertFalse(bm.has_undefined_col_sizes())
self.assertEqual(bm.shape, (8, 8))
self.assertTrue(np.all(bm.row_block_sizes() == np.ones(2) * 4))
self.assertTrue(np.all(bm.col_block_sizes() == np.ones(2) * 4))
self.assertTrue(
np.all(bm.row_block_sizes(copy=False) == np.ones(2) * 4))
self.assertTrue(
np.all(bm.col_block_sizes(copy=False) == np.ones(2) * 4))
def test_lt(self):
v = BlockVector(2)
a = np.ones(5)
b = np.zeros(9)
v.set_block(0, a)
v.set_block(1, b)
flags = v < 1
v.set_block(0, a - 1)
v.set_block(1, b + 1)
self.assertEqual(v.nblocks, flags.nblocks)
for bid, blk in enumerate(flags):
self.assertTrue(np.allclose(blk, v.get_block(bid)))
v.set_block(0, a + 1)
v.set_block(1, b - 1)
flags = v < np.ones(v.size)
v.set_block(0, a - 1)
v.set_block(1, b + 1)
self.assertEqual(v.nblocks, flags.nblocks)
for bid, blk in enumerate(flags):
self.assertTrue(np.allclose(blk, v.get_block(bid)))
v.set_block(0, a + 1)
v.set_block(1, b - 1)
vv = v.copy()
vv.fill(1.0)
flags = v < vv
v.set_block(0, a - 1)
v.set_block(1, b + 1)
self.assertEqual(v.nblocks, flags.nblocks)
for bid, blk in enumerate(flags):
self.assertTrue(np.allclose(blk, v.get_block(bid)))
def test_compress(self):
v = self.ones
v = BlockVector(2)
a = np.ones(5)
b = np.zeros(9)
v.set_block(0, a)
v.set_block(1, b)
c = v.compress(v < 1)
v2 = BlockVector(2)
b = np.zeros(9)
v2.set_block(0, np.ones(0))
v2.set_block(1, b)
self.assertEqual(c.nblocks, v.nblocks)
for bid, blk in enumerate(c):
self.assertTrue(np.allclose(blk, v2.get_block(bid)))
flags = v < 1
c = v.compress(flags.flatten())
self.assertEqual(c.nblocks, v.nblocks)
for bid, blk in enumerate(c):
self.assertTrue(np.allclose(blk, v2.get_block(bid)))
with self.assertRaises(Exception) as context:
v.compress(1.0)
def __init__(self, A1, A2, c1, c2, N, dt):
self._A1 = A1
self._A2 = A2
self._c1 = c1
self._c2 = c2
self._N = N
self._dt = dt
self._input_names = ['F1_{}'.format(t) for t in range(1, N)]
self._input_names.extend(['F2_{}'.format(t) for t in range(1, N)])
self._input_names.extend(['h1_{}'.format(t) for t in range(0, N)])
self._input_names.extend(['h2_{}'.format(t) for t in range(0, N)])
self._output_names = ['F12_{}'.format(t) for t in range(0, N)]
self._output_names.extend(['Fo_{}'.format(t) for t in range(0, N)])
self._equality_constraint_names = [
'h1bal_{}'.format(t) for t in range(1, N)
]
self._equality_constraint_names.extend(
['h2bal_{}'.format(t) for t in range(1, N)])
# inputs
self._F1 = np.zeros(N) # we don't use the first one
self._F2 = np.zeros(N) # we don't use the first one
self._h1 = np.zeros(N)
self._h2 = np.zeros(N)
# outputs
self._F12 = np.zeros(N)
self._Fo = np.zeros(N)
# multipliers
self._eq_con_mult_values = np.ones(2 * (N - 1))
self._output_con_mult_values = np.ones(2 * N)
def test_set_blocks(self):
v = self.ones
blocks = [np.ones(s) * i for i, s in enumerate(self.list_sizes_ones)]
v.set_blocks(blocks)
for i, s in enumerate(self.list_sizes_ones):
self.assertEqual(v.get_block(i).size, s)
self.assertEqual(v.get_block(i).shape, (s, ))
res = np.ones(s) * i
self.assertListEqual(v.get_block(i).tolist(), res.tolist())
def test_has_none(self):
v = self.ones
self.assertFalse(v.has_none)
v = BlockVector(3)
v.set_block(0, np.ones(2))
v.set_block(2, np.ones(3))
self.assertTrue(v.has_none)
v.set_block(1, np.ones(2))
self.assertFalse(v.has_none)
def test_reduce_ufuncs(self):
v = BlockVector(2)
a = np.ones(3) * 0.5
b = np.ones(2) * 0.8
v.set_block(0, a)
v.set_block(1, b)
reduce_funcs = [np.sum, np.max, np.min, np.prod, np.mean]
for fun in reduce_funcs:
self.assertAlmostEqual(fun(v), fun(v.flatten()))
other_funcs = [np.all, np.any, np.std, np.ptp]
for fun in other_funcs:
self.assertAlmostEqual(fun(v), fun(v.flatten()))
def test_all(self):
v = BlockVector(2)
a = np.ones(5)
b = np.ones(3)
v.set_block(0, a)
v.set_block(1, b)
self.assertTrue(v.all())
v = BlockVector(2)
a = np.zeros(5)
b = np.zeros(3)
v.set_block(0, a)
v.set_block(1, b)
self.assertFalse(v.all())
def test_matvec_1(self):
rank = comm.Get_rank()
rank_ownership = np.array([[0, -1, -1, 0], [-1, 1, -1, 1],
[-1, -1, 2, 2], [0, 1, 2, -1]])
m = MPIBlockMatrix(4, 4, rank_ownership, comm)
sub_m = np.array([[1, 0], [0, 1]])
sub_m = coo_matrix(sub_m)
m.set_block(rank, rank, sub_m.copy())
m.set_block(rank, 3, sub_m.copy())
m.set_block(3, rank, sub_m.copy())
m.set_block(3, 3, sub_m.copy())
rank_ownership = np.array([0, 1, 2, -1])
v = MPIBlockVector(4, rank_ownership, comm)
sub_v = np.ones(2)
v.set_block(rank, sub_v.copy())
v.set_block(3, sub_v.copy())
res = m.dot(v)
self.assertIsInstance(res, MPIBlockVector)
self.assertTrue(np.array_equal(res.get_block(rank), sub_v * 2))
self.assertTrue(np.array_equal(res.get_block(3), sub_v * 4))
self.assertTrue(
np.array_equal(res.rank_ownership, np.array([0, 1, 2, -1])))
self.assertFalse(res.has_none)
def test_get_block_vector_for_dot_product_5(self):
rank = comm.Get_rank()
rank_ownership = np.array([[1, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]])
m = MPIBlockMatrix(4, 3, rank_ownership, comm)
sub_m = np.array([[1, 0], [0, 1]])
sub_m = coo_matrix(sub_m)
if rank == 0:
m.set_block(3, rank, sub_m.copy())
elif rank == 1:
m.set_block(0, 0, sub_m.copy())
m.set_block(rank, rank, sub_m.copy())
m.set_block(3, rank, sub_m.copy())
else:
m.set_block(rank, rank, sub_m.copy())
m.set_block(3, rank, sub_m.copy())
v = BlockVector(3)
sub_v = np.ones(2)
for ndx in range(3):
v.set_block(ndx, sub_v.copy())
res = m._get_block_vector_for_dot_product(v)
self.assertIs(res, v)
v_flat = v.flatten()
res = m._get_block_vector_for_dot_product(v_flat)
self.assertIsInstance(res, BlockVector)
for ndx in range(3):
block = res.get_block(ndx)
self.assertTrue(np.array_equal(block, sub_v))
def test_round(self):
v = BlockVector(2)
a = np.ones(5) * 1.1
b = np.ones(9) * 1.1
v.set_block(0, a)
v.set_block(1, b)
vv = v.round()
self.assertEqual(vv.nblocks, v.nblocks)
a = np.ones(5)
b = np.ones(9)
v.set_block(0, a)
v.set_block(1, b)
for bid, blk in enumerate(vv):
self.assertTrue(np.allclose(blk, v.get_block(bid)))
def test_get_block_vector_for_dot_product_2(self):
rank = comm.Get_rank()
rank_ownership = np.array([[1, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]])
m = MPIBlockMatrix(4, 3, rank_ownership, comm)
sub_m = np.array([[1, 0], [0, 1]])
sub_m = coo_matrix(sub_m)
if rank == 0:
m.set_block(3, rank, sub_m.copy())
elif rank == 1:
m.set_block(0, 0, sub_m.copy())
m.set_block(rank, rank, sub_m.copy())
m.set_block(3, rank, sub_m.copy())
else:
m.set_block(rank, rank, sub_m.copy())
m.set_block(3, rank, sub_m.copy())
rank_ownership = np.array([-1, 1, 2])
v = MPIBlockVector(3, rank_ownership, comm)
sub_v = np.ones(2)
v.set_block(0, sub_v.copy())
if rank != 0:
v.set_block(rank, sub_v.copy())
res = m._get_block_vector_for_dot_product(v)
self.assertIs(res, v)
def test_jacobian_d(self):
nlp = self.nlp
x = nlp.create_vector_x()
self.assertEqual(nlp.jacobian_d(x).shape, (0, 3))
x = self.nlp3.create_vector_x()
x.fill(1.0)
jac = self.nlp3.jacobian_d(x)
self.assertEqual(3, jac.shape[1])
self.assertEqual(3, jac.shape[0])
values = np.ones(7)
self.assertTrue(np.allclose(values, jac.data))
new_jac = self.nlp3.jacobian_d(x)
new_jac.data.fill(0.0)
self.nlp3.jacobian_d(x, out=new_jac)
self.assertTrue(np.allclose(values, new_jac.data))
jac_g = self.nlp3.jacobian_g(x)
new_jac = self.nlp3.jacobian_d(x, evaluated_jac_g=jac_g)
self.assertTrue(np.allclose(values, new_jac.data))
new_jac = self.nlp3.jacobian_d(x)
new_jac.data.fill(0.0)
self.nlp3.jacobian_d(x, out=new_jac, evaluated_jac_g=jac_g)
self.assertTrue(np.allclose(values, new_jac.data))
def test_get_block_vector_for_dot_product_3(self):
rank = comm.Get_rank()
rank_ownership = np.array([[1, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]])
m = MPIBlockMatrix(4, 3, rank_ownership, comm)
sub_m = np.array([[1, 0], [0, 1]])
sub_m = coo_matrix(sub_m)
if rank == 0:
m.set_block(3, rank, sub_m.copy())
elif rank == 1:
m.set_block(0, 0, sub_m.copy())
m.set_block(rank, rank, sub_m.copy())
m.set_block(3, rank, sub_m.copy())
else:
m.set_block(rank, rank, sub_m.copy())
m.set_block(3, rank, sub_m.copy())
rank_ownership = np.array([0, 1, 2])
v = MPIBlockVector(3, rank_ownership, comm)
sub_v = np.ones(2)
v.set_block(rank, sub_v.copy())
res = m._get_block_vector_for_dot_product(v)
self.assertIsNot(res, v)
self.assertTrue(np.array_equal(res.get_block(0), sub_v))
if rank == 0:
self.assertIsNone(res.get_block(1))
self.assertIsNone(res.get_block(2))
elif rank == 1:
self.assertTrue(np.array_equal(res.get_block(1), sub_v))
self.assertIsNone(res.get_block(2))
elif rank == 2:
self.assertTrue(np.array_equal(res.get_block(2), sub_v))
self.assertIsNone(res.get_block(1))
def test_eval_d(self):
x = np.ones(self.nlp3.nx)
res = np.array([2.0, 2.0, 3.0])
self.assertTrue(np.allclose(self.nlp3.evaluate_d(x), res))
res_ = self.nlp3.create_vector_y(subset='d')
self.nlp3.evaluate_d(x, out=res_)
self.assertTrue(np.allclose(res_, res))
x = np.ones(self.nlp3.nx)
res = np.array([2.0, 2.0, 3.0])
g = self.nlp3.evaluate_g(x)
res_eval = self.nlp3.evaluate_d(x, evaluated_g=g)
self.assertTrue(np.allclose(res_eval, res))
res_ = self.nlp3.create_vector_y(subset='d')
self.nlp3.evaluate_d(x, out=res_, evaluated_g=g)
self.assertTrue(np.allclose(res_, res))
def test_eval_g(self):
x = np.ones(self.nlp3.nx)
res = np.array([-1.0, -0.5, 2, 2, 3])
self.assertTrue(np.allclose(self.nlp3.evaluate_g(x), res))
res_ = self.nlp3.create_vector_y()
self.nlp3.evaluate_g(x, out=res_)
self.assertTrue(np.allclose(res_, res))
def test_copyto(self):
bm0 = self.basic_m.copy()
coo = bm0.tocoo()
coo.data.fill(1.0)
csr = coo.tocsr()
csc = coo.tocsc()
self.assertFalse(np.allclose(coo.toarray(), self.dense))
self.assertFalse(np.allclose(csr.toarray(), self.dense))
self.assertFalse(np.allclose(csc.toarray(), self.dense))
bm0.copyto(coo)
bm0.copyto(csr)
bm0.copyto(csc)
self.assertTrue(np.allclose(coo.toarray(), self.dense))
self.assertTrue(np.allclose(csr.toarray(), self.dense))
self.assertTrue(np.allclose(csc.toarray(), self.dense))
flat = np.ones((8, 8))
bm0.copyto(flat)
self.assertTrue(np.allclose(flat, self.dense))
bm = bm0.copy_structure()
bm0.copyto(bm)
self.assertTrue(np.allclose(bm.toarray(), self.dense))
bm.get_block(0, 0).data.fill(1.0)
self.assertAlmostEqual(bm0.toarray()[0, 0],
2) # this tests that a deep copy was done
self.assertAlmostEqual(bm.toarray()[0, 0], 1)
bm0.copyto(bm, deep=False)
bm.get_block(0, 0).data.fill(1.0)
self.assertAlmostEqual(bm0.toarray()[0, 0],
1) # this tests that a shallow copy was done
self.assertAlmostEqual(bm.toarray()[0, 0], 1)
def test_mean(self):
flat_v = np.ones(self.ones.size)
v = self.ones
self.assertEqual(v.mean(), flat_v.mean())
v = BlockVector(2)
with self.assertRaises(NotFullyDefinedBlockVectorError):
v_mean = v.mean()
def test_constructor(self):
v = BlockVector(2)
self.assertEqual(v.nblocks, 2)
self.assertEqual(v.bshape, (2, ))
with self.assertRaises(NotFullyDefinedBlockVectorError):
v_size = v.size
v.set_block(0, np.ones(2))
v.set_block(1, np.ones(4))
self.assertEqual(v.size, 6)
self.assertEqual(v.shape, (6, ))
with self.assertRaises(AssertionError):
v.set_block(0, None)
with self.assertRaises(Exception) as context:
BlockVector('hola')
def test_copy(self):
v = BlockVector(2)
a = np.ones(5)
b = np.zeros(9)
v.set_block(0, a)
v.set_block(1, b)
v2 = v.copy()
self.assertTrue(np.allclose(v.flatten(), v2.flatten()))
def test_multiply(self):
# check scalar multiplication
block = self.block_m
m = self.basic_m * 5.0
scipy_mat = bmat([[block, block], [None, block]], format='coo')
mulscipy_mat = scipy_mat * 5.0
dinopy_mat = m.tocoo()
drow = np.sort(dinopy_mat.row)
dcol = np.sort(dinopy_mat.col)
ddata = np.sort(dinopy_mat.data)
srow = np.sort(mulscipy_mat.row)
scol = np.sort(mulscipy_mat.col)
sdata = np.sort(mulscipy_mat.data)
self.assertListEqual(drow.tolist(), srow.tolist())
self.assertListEqual(dcol.tolist(), scol.tolist())
self.assertListEqual(ddata.tolist(), sdata.tolist())
m = 5.0 * self.basic_m
dinopy_mat = m.tocoo()
drow = np.sort(dinopy_mat.row)
dcol = np.sort(dinopy_mat.col)
ddata = np.sort(dinopy_mat.data)
self.assertListEqual(drow.tolist(), srow.tolist())
self.assertListEqual(dcol.tolist(), scol.tolist())
self.assertListEqual(ddata.tolist(), sdata.tolist())
# check dot product with block vector
block = self.block_m
m = self.basic_m
scipy_mat = bmat([[block, block], [None, block]], format='coo')
x = BlockVector(2)
x.set_block(0, np.ones(block.shape[1], dtype=np.float64))
x.set_block(1, np.ones(block.shape[1], dtype=np.float64))
res_scipy = scipy_mat.dot(x.flatten())
res_dinopy = m * x
res_dinopy_flat = m * x.flatten()
self.assertListEqual(res_dinopy.tolist(), res_scipy.tolist())
self.assertListEqual(res_dinopy_flat.tolist(), res_scipy.tolist())
dense_mat = dinopy_mat.toarray()
self.basic_m *= 5.0
self.assertTrue(np.allclose(dense_mat, self.basic_m.toarray()))
def test_fill(self):
v = BlockVector(2)
a = np.arange(5)
b = np.arange(9)
v.set_block(0, a)
v.set_block(1, b)
v.fill(1.0)
c = np.ones(v.size)
self.assertListEqual(v.tolist(), c.tolist())
def test_copy_structure(self):
v = BlockVector(2)
a = np.ones(5)
b = np.zeros(9)
v.set_block(0, a)
v.set_block(1, b)
v2 = v.copy_structure()
self.assertEqual(v.get_block(0).size, v2.get_block(0).size)
self.assertEqual(v.get_block(1).size, v2.get_block(1).size)
def test_itruediv(self):
v = self.ones
v /= 3
self.assertTrue(np.allclose(v.flatten(), np.ones(v.size) / 3))
v.fill(1.0)
v /= v
self.assertTrue(np.allclose(v.flatten(), np.ones(v.size)))
v.fill(1.0)
v /= np.ones(v.size) * 2
self.assertTrue(np.allclose(v.flatten(), np.ones(v.size) / 2))
v = BlockVector(2)
a = np.ones(5)
b = np.arange(9, dtype=np.float64)
a_copy = a.copy()
b_copy = b.copy()
v.set_block(0, a)
v.set_block(1, b)
v /= 2.0
self.assertTrue(np.allclose(v.get_block(0), a_copy / 2.0))
self.assertTrue(np.allclose(v.get_block(1), b_copy / 2.0))
v = BlockVector(2)
a = np.ones(5)
b = np.zeros(9)
a_copy = a.copy()
b_copy = b.copy()
v.set_block(0, a)
v.set_block(1, b)
v2 = BlockVector(2)
v2.set_block(0, np.ones(5) * 2)
v2.set_block(1, np.ones(9) * 2)
v /= v2
self.assertTrue(np.allclose(v.get_block(0), a_copy / 2))
self.assertTrue(np.allclose(v.get_block(1), b_copy / 2))
self.assertTrue(np.allclose(v2.get_block(0), np.ones(5) * 2))
self.assertTrue(np.allclose(v2.get_block(1), np.ones(9) * 2))
with self.assertRaises(Exception) as context:
v *= 'hola'
def test_contains(self):
v = BlockVector(2)
a = np.ones(5)
b = np.zeros(9)
v.set_block(0, a)
v.set_block(1, b)
self.assertTrue(0 in v)
self.assertFalse(3 in v)
def test_clip(self):
v = BlockVector(3)
v2 = BlockVector(3)
a = np.zeros(5)
b = np.ones(3) * 5.0
c = np.ones(3) * 10.0
v.set_block(0, a)
v.set_block(1, b)
v.set_block(2, c)
v2.set_block(0, np.ones(5) * 4.0)
v2.set_block(1, np.ones(3) * 5.0)
v2.set_block(2, np.ones(3) * 9.0)
vv = v.clip(4.0, 9.0)
self.assertEqual(vv.nblocks, v.nblocks)
for bid, blk in enumerate(vv):
self.assertTrue(np.allclose(blk, v2.get_block(bid)))
def test_clone(self):
v = self.ones
w = v.clone()
self.assertListEqual(w.tolist(), v.tolist())
x = v.clone(4)
self.assertListEqual(x.tolist(), [4] * v.size)
y = x.clone(copy=False)
y.get_block(2)[-1] = 6
d = np.ones(y.size) * 4
d[-1] = 6
self.assertListEqual(y.tolist(), d.tolist())
self.assertListEqual(x.tolist(), d.tolist())
def test_interface(self):
# weird, this is really a test of the test class above
# but we could add code later, so...
iom = PressureDropModel()
iom.set_inputs(np.ones(4))
o = iom.evaluate_outputs()
expected_o = np.asarray([0.0, -1.0], dtype=np.float64)
self.assertTrue(np.array_equal(o, expected_o))
jac = iom.evaluate_derivatives()
expected_jac = np.asarray([[1, -1, 0, -2], [1, -1, -1, -4]], dtype=np.float64)
self.assertTrue(np.array_equal(jac.todense(), expected_jac))
def test_dot(self):
A_dense = self.basic_m.toarray()
A_block = self.basic_m
x = np.ones(A_dense.shape[1])
block_x = BlockVector(2)
block_x.set_block(0, np.ones(self.block_m.shape[1]))
block_x.set_block(1, np.ones(self.block_m.shape[1]))
flat_res = A_block.dot(x).flatten()
block_res = A_block.dot(block_x)
self.assertTrue(np.allclose(A_dense.dot(x), flat_res))
self.assertTrue(np.allclose(A_dense.dot(x), block_res.flatten()))
self.assertEqual(block_res.bshape[0], 2)
m = BlockMatrix(2, 2)
sub_m = np.array([[1, 0], [0, 1]])
sub_m = coo_matrix(sub_m)
m.set_block(0, 1, sub_m.copy())
m.set_block(1, 0, sub_m.copy())
x = np.arange(4)
res = m * x
self.assertTrue(np.allclose(res.flatten(), np.array([2, 3, 0, 1])))
def test_unary_ufuncs(self):
v = BlockVector(2)
a = np.ones(3) * 0.5
b = np.ones(2) * 0.8
v.set_block(0, a)
v.set_block(1, b)
v2 = BlockVector(2)
unary_funcs = [
np.log10, np.sin, np.cos, np.exp, np.ceil, np.floor, np.tan,
np.arctan, np.arcsin, np.arccos, np.sinh, np.cosh, np.abs, np.tanh,
np.arcsinh, np.arctanh, np.fabs, np.sqrt, np.log, np.log2,
np.absolute, np.isfinite, np.isinf, np.isnan, np.log1p,
np.logical_not, np.exp2, np.expm1, np.sign, np.rint, np.square,
np.positive, np.negative, np.rad2deg, np.deg2rad, np.conjugate,
np.reciprocal
]
for fun in unary_funcs:
v2.set_block(0, fun(v.get_block(0)))
v2.set_block(1, fun(v.get_block(1)))
res = fun(v)
self.assertIsInstance(res, BlockVector)
self.assertEqual(res.nblocks, 2)
for i in range(2):
self.assertTrue(np.allclose(res.get_block(i), v2.get_block(i)))
other_funcs = [np.cumsum, np.cumprod, np.cumproduct]
for fun in other_funcs:
res = fun(v)
self.assertIsInstance(res, BlockVector)
self.assertEqual(res.nblocks, 2)
self.assertTrue(np.allclose(fun(v.flatten()), res.flatten()))
with self.assertRaises(Exception) as context:
np.cbrt(v)
