def test_array_as_explicit_call():
assert ZeroArray(3, 2, 4).as_explicit() == ImmutableDenseNDimArray.zeros(
3, 2, 4)
assert OneArray(3, 2, 4).as_explicit() == ImmutableDenseNDimArray(
[1 for i in range(3 * 2 * 4)]).reshape(3, 2, 4)
k = Symbol("k")
X = ArraySymbol("X", k, 3, 2)
raises(ValueError, lambda: X.as_explicit())
raises(ValueError, lambda: ZeroArray(k, 2, 3).as_explicit())
raises(ValueError, lambda: OneArray(2, k, 2).as_explicit())
A = ArraySymbol("A", 3, 3)
B = ArraySymbol("B", 3, 3)
texpr = tensorproduct(A, B)
assert isinstance(texpr, ArrayTensorProduct)
assert texpr.as_explicit() == tensorproduct(A.as_explicit(),
B.as_explicit())
texpr = tensorcontraction(A, (0, 1))
assert isinstance(texpr, ArrayContraction)
assert texpr.as_explicit() == A[0, 0] + A[1, 1] + A[2, 2]
texpr = tensordiagonal(A, (0, 1))
assert isinstance(texpr, ArrayDiagonal)
assert texpr.as_explicit() == ImmutableDenseNDimArray(
[A[0, 0], A[1, 1], A[2, 2]])
texpr = permutedims(A, [1, 0])
assert isinstance(texpr, PermuteDims)
assert texpr.as_explicit() == permutedims(A.as_explicit(), [1, 0])
def as_explicit(self):
if any(not isinstance(i, (int, Integer)) for i in self.shape):
raise ValueError(
"cannot express explicit array with symbolic shape")
data = [
self[i] for i in itertools.product(*[range(j) for j in self.shape])
]
return ImmutableDenseNDimArray(data).reshape(*self.shape)
def test_array_symbol_and_element():
A = ArraySymbol("A", 2)
A0 = ArrayElement(A, (0,))
A1 = ArrayElement(A, (1,))
assert A.as_explicit() == ImmutableDenseNDimArray([A0, A1])
A2 = tensorproduct(A, A)
assert A2.shape == (2, 2)
# TODO: not yet supported:
# assert A2.as_explicit() == Array([[A[0]*A[0], A[1]*A[0]], [A[0]*A[1], A[1]*A[1]]])
A3 = tensorcontraction(A2, (0, 1))
assert A3.shape == ()
# TODO: not yet supported:
# assert A3.as_explicit() == Array([])
A = ArraySymbol("A", 2, 3, 4)
Ae = A.as_explicit()
assert Ae == ImmutableDenseNDimArray(
[[[ArrayElement(A, (i, j, k)) for k in range(4)] for j in range(3)] for i in range(2)])
p = permutedims(A, Permutation(0, 2, 1))
assert isinstance(p, PermuteDims)
def inv(self):
"""
Returns inverse of the transformation matrix
Returns
-------
~sympy.tensor.array.ndim_array.NDimArray
Inverse of the matrix.
"""
if self._inv is not None:
return self._inv
tmp_array = sympy_to_np_array(self)
tmp_array = np.reciprocal(tmp_array)
self._inv = ImmutableDenseNDimArray(tmp_array)
return self._inv
def test_array_as_explicit_matrix_symbol():
A = MatrixSymbol("A", 3, 3)
B = MatrixSymbol("B", 3, 3)
texpr = tensorproduct(A, B)
assert isinstance(texpr, ArrayTensorProduct)
assert texpr.as_explicit() == tensorproduct(A.as_explicit(),
B.as_explicit())
texpr = tensorcontraction(A, (0, 1))
assert isinstance(texpr, ArrayContraction)
assert texpr.as_explicit() == A[0, 0] + A[1, 1] + A[2, 2]
texpr = tensordiagonal(A, (0, 1))
assert isinstance(texpr, ArrayDiagonal)
assert texpr.as_explicit() == ImmutableDenseNDimArray(
[A[0, 0], A[1, 1], A[2, 2]])
texpr = permutedims(A, [1, 0])
assert isinstance(texpr, PermuteDims)
assert texpr.as_explicit() == permutedims(A.as_explicit(), [1, 0])
def as_explicit(self):
data = [self[i] for i in itertools.product(*[range(j) for j in self.shape])]
return ImmutableDenseNDimArray(data).reshape(*self.shape)
def as_explicit(self):
if any(not i.is_Integer for i in self.shape):
raise ValueError("Cannot return explicit form for symbolic shape.")
return ImmutableDenseNDimArray([
S.One for i in range(reduce(operator.mul, self.shape))
]).reshape(*self.shape)