def test_sub(key, mat, quad):
test = key[0]
trial = key[1]
m = mat((test[0](N, quad=quad), test[1]),
(trial[0](N, quad=quad), trial[1]))
mc = m - m
assert mc.scale == 0.0
m1 = shenfun.SparseMatrix(copy(dict(m)), m.shape)
m2 = shenfun.SparseMatrix(copy(dict(m)), m.shape)
mc = m1 - m2
assert mc.scale == 0.0
def test_iadd(key, mat, quad):
test = key[0]
trial = key[1]
m = mat((test[0](N, quad=quad), test[1]),
(trial[0](N, quad=quad), trial[1]))
mc = deepcopy(m)
m += mc
assert m.scale == 2.0
m1 = shenfun.SparseMatrix(deepcopy(dict(m)), m.shape)
m2 = shenfun.SparseMatrix(deepcopy(dict(m)), m.shape)
m1 += m2
assert m1.scale == 2.0
def test_mul2():
mat = shenfun.SparseMatrix({0: 1}, (3, 3))
v = np.ones(3)
c = mat * v
assert np.allclose(c, 1)
mat = shenfun.SparseMatrix({-2:1, -1:1, 0: 1, 1:1, 2:1}, (3, 3))
c = mat * v
assert np.allclose(c, 3)
SD = shenfun.Basis(8, "L", bc=(0, 0), plan=True, scaled=True)
u = shenfun.TrialFunction(SD)
v = shenfun.TestFunction(SD)
mat = shenfun.inner(shenfun.grad(u), shenfun.grad(v))
z = shenfun.Function(SD, val=1)
c = mat * z
assert np.allclose(c[:6], 1)
def test_rmul(key, mat, quad):
test = key[0]
trial = key[1]
m = mat((test[0](N, quad=quad), test[1]),
(trial[0](N, quad=quad), trial[1]))
mc = m*2.
assert mc.scale == 2.0
mat = shenfun.SparseMatrix(deepcopy(dict(m)), m.shape)
mc = mat*2.
assert mc.scale == 2.0
def test_add(key, mat, quad):
test = key[0]
trial = key[1]
m = mat((test[0](N, quad=quad), test[1]),
(trial[0](N, quad=quad), trial[1]))
mc = m + m
assert mc.scale == 2.0
mat = shenfun.SparseMatrix(deepcopy(dict(m)), m.shape)
mc = m + mat
for key, val in six.iteritems(mc):
assert np.allclose(val, m[key]*2)
def test_imul(key, mat, quad):
test = key[0]
trial = key[1]
mat = mat((test[0](N, quad=quad), test[1]),
(trial[0](N, quad=quad), trial[1]))
mc = deepcopy(dict(mat))
mat *= 2
assert mat.scale == 2.0
mat = shenfun.SparseMatrix(deepcopy(dict(mc)), mat.shape)
mat *= 2
assert mat.scale == 2.0
def test_div(key, mat, quad):
test = key[0]
trial = key[1]
m = mat((test[0](N, quad=quad), test[1]),
(trial[0](N, quad=quad), trial[1]))
mc = m / 2.
assert mc.scale == 0.5
mat = shenfun.SparseMatrix(copy(dict(m)), m.shape)
mc = mat / 2.
assert mc.scale == 0.5
