N-dim array expressions#

Array expressions are expressions representing N-dimensional arrays, without evaluating them. These expressions represent in a certain way abstract syntax trees of operations on N-dimensional arrays.

Every N-dimensional array operator has a corresponding array expression object.

Table of correspondences:

Array operator	Array expression operator
tensorproduct	ArrayTensorProduct
tensorcontraction	ArrayContraction
tensordiagonal	ArrayDiagonal
permutedims	PermuteDims

实例#

ArraySymbol objects are the N-dimensional equivalent of MatrixSymbol objects in the matrix module:

>>> from sympy.tensor.array.expressions import ArraySymbol
>>> from sympy.abc import i, j, k
>>> A = ArraySymbol("A", (3, 2, 4))
>>> A.shape
(3, 2, 4)
>>> A[i, j, k]
A[i, j, k]
>>> A.as_explicit()
[[[A[0, 0, 0], A[0, 0, 1], A[0, 0, 2], A[0, 0, 3]],
  [A[0, 1, 0], A[0, 1, 1], A[0, 1, 2], A[0, 1, 3]]],
 [[A[1, 0, 0], A[1, 0, 1], A[1, 0, 2], A[1, 0, 3]],
  [A[1, 1, 0], A[1, 1, 1], A[1, 1, 2], A[1, 1, 3]]],
 [[A[2, 0, 0], A[2, 0, 1], A[2, 0, 2], A[2, 0, 3]],
  [A[2, 1, 0], A[2, 1, 1], A[2, 1, 2], A[2, 1, 3]]]]

Component-explicit arrays can be added inside array expressions:

>>> from sympy import Array
>>> from sympy import tensorproduct
>>> from sympy.tensor.array.expressions import ArrayTensorProduct
>>> a = Array([1, 2, 3])
>>> b = Array([i, j, k])
>>> expr = ArrayTensorProduct(a, b, b)
>>> expr
ArrayTensorProduct([1, 2, 3], [i, j, k], [i, j, k])
>>> expr.as_explicit() == tensorproduct(a, b, b)
True

Constructing array expressions from index-explicit forms#

Array expressions are index-implicit. This means they do not use any indices to represent array operations. The function convert_indexed_to_array( ... ) may be used to convert index-explicit expressions to array expressions. It takes as input two parameters: the index-explicit expression and the order of the indices:

>>> from sympy.tensor.array.expressions import convert_indexed_to_array
>>> from sympy import Sum
>>> A = ArraySymbol("A", (3, 3))
>>> B = ArraySymbol("B", (3, 3))
>>> convert_indexed_to_array(A[i, j], [i, j])
A
>>> convert_indexed_to_array(A[i, j], [j, i])
PermuteDims(A, (0 1))
>>> convert_indexed_to_array(A[i, j] + B[j, i], [i, j])
ArrayAdd(A, PermuteDims(B, (0 1)))
>>> convert_indexed_to_array(Sum(A[i, j]*B[j, k], (j, 0, 2)), [i, k])
ArrayContraction(ArrayTensorProduct(A, B), (1, 2))

The diagonal of a matrix in the array expression form:

>>> convert_indexed_to_array(A[i, i], [i])
ArrayDiagonal(A, (0, 1))

The trace of a matrix in the array expression form:

>>> convert_indexed_to_array(Sum(A[i, i], (i, 0, 2)), [i])
ArrayContraction(A, (0, 1))

Compatibility with matrices#

Array expressions can be mixed with objects from the matrix module:

>>> from sympy import MatrixSymbol
>>> from sympy.tensor.array.expressions import ArrayContraction
>>> M = MatrixSymbol("M", 3, 3)
>>> N = MatrixSymbol("N", 3, 3)

Express the matrix product in the array expression form:

>>> from sympy.tensor.array.expressions import convert_matrix_to_array
>>> expr = convert_matrix_to_array(M*N)
>>> expr
ArrayContraction(ArrayTensorProduct(M, N), (1, 2))

The expression can be converted back to matrix form:

>>> from sympy.tensor.array.expressions import convert_array_to_matrix
>>> convert_array_to_matrix(expr)
M*N

Add a second contraction on the remaining axes in order to get the trace of \(M \cdot N\):

>>> expr_tr = ArrayContraction(expr, (0, 1))
>>> expr_tr
ArrayContraction(ArrayContraction(ArrayTensorProduct(M, N), (1, 2)), (0, 1))

Flatten the expression by calling .doit() and remove the nested array contraction operations:

>>> expr_tr.doit()
ArrayContraction(ArrayTensorProduct(M, N), (0, 3), (1, 2))

Get the explicit form of the array expression:

>>> expr.as_explicit()
[[M[0, 0]*N[0, 0] + M[0, 1]*N[1, 0] + M[0, 2]*N[2, 0], M[0, 0]*N[0, 1] + M[0, 1]*N[1, 1] + M[0, 2]*N[2, 1], M[0, 0]*N[0, 2] + M[0, 1]*N[1, 2] + M[0, 2]*N[2, 2]],
 [M[1, 0]*N[0, 0] + M[1, 1]*N[1, 0] + M[1, 2]*N[2, 0], M[1, 0]*N[0, 1] + M[1, 1]*N[1, 1] + M[1, 2]*N[2, 1], M[1, 0]*N[0, 2] + M[1, 1]*N[1, 2] + M[1, 2]*N[2, 2]],
 [M[2, 0]*N[0, 0] + M[2, 1]*N[1, 0] + M[2, 2]*N[2, 0], M[2, 0]*N[0, 1] + M[2, 1]*N[1, 1] + M[2, 2]*N[2, 1], M[2, 0]*N[0, 2] + M[2, 1]*N[1, 2] + M[2, 2]*N[2, 2]]]

Express the trace of a matrix:

>>> from sympy import Trace
>>> convert_matrix_to_array(Trace(M))
ArrayContraction(M, (0, 1))
>>> convert_matrix_to_array(Trace(M*N))
ArrayContraction(ArrayTensorProduct(M, N), (0, 3), (1, 2))

Express the transposition of a matrix (will be expressed as a permutation of the axes:

>>> convert_matrix_to_array(M.T)
PermuteDims(M, (0 1))

Compute the derivative array expressions:

>>> from sympy.tensor.array.expressions import array_derive
>>> d = array_derive(M, M)
>>> d
PermuteDims(ArrayTensorProduct(I, I), (3)(1 2))

Verify that the derivative corresponds to the form computed with explicit matrices:

>>> d.as_explicit()
[[[[1, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 1, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 1], [0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [1, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 1, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 1], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0], [1, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 1, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 1]]]]
>>> Me = M.as_explicit()
>>> Me.diff(Me)
[[[[1, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 1, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 1], [0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [1, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 1, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 1], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0], [1, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 1, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 1]]]]

class sympy.tensor.array.expressions.ArrayTensorProduct(*args, **kwargs)[源代码]#: 类来表示类数组对象的张量积。

class sympy.tensor.array.expressions.ArrayContraction(expr, *contraction_indices, **kwargs)[源代码]#: 这个类的目的是以代码打印机易于处理的形式表示数组的收缩。

class sympy.tensor.array.expressions.ArrayDiagonal(expr, *diagonal_indices, **kwargs)[源代码]#

类来表示对角线运算符。

解释

在二维数组中，它返回对角线，如下所示：

\(A_{ij} \rightarrow A_{ii}\)

两个二维阵列张量积在轴1和轴2（第二和第三）上的对角线 \(A \otimes B\) 是

\(\Big[ A_{ab} B_{cd} \Big]_{abcd} \rightarrow \Big[ A_{ai} B_{id} \Big]_{adi}\)

在最后一个示例中，数组表达式已从四维缩减为三维。请注意，没有出现收缩，而是出现了一个新的指数 \(i\) 对于对角线，收缩会将数组减少到二维。

请注意，将对角化的out维度作为新维度添加到索引末尾。

class sympy.tensor.array.expressions.PermuteDims(expr, permutation=None, index_order_old=None, index_order_new=None, **kwargs)[源代码]#

类来表示数组轴的排列。

实例

>>> from sympy.tensor.array import permutedims
>>> from sympy import MatrixSymbol
>>> M = MatrixSymbol("M", 3, 3)
>>> cg = permutedims(M, [1, 0])

客体 cg 表示 M ，作为排列 [1, 0] 将通过切换指数来执行：

\(M_{ij} \Rightarrow M_{ji}\)

这在转换回矩阵形式时很明显：

>>> from sympy.tensor.array.expressions.from_array_to_matrix import convert_array_to_matrix
>>> convert_array_to_matrix(cg)
M.T

>>> N = MatrixSymbol("N", 3, 2)
>>> cg = permutedims(N, [1, 0])
>>> cg.shape
(2, 3)

There are optional parameters that can be used as alternative to the permutation:

>>> from sympy.tensor.array.expressions import ArraySymbol, PermuteDims
>>> M = ArraySymbol("M", (1, 2, 3, 4, 5))
>>> expr = PermuteDims(M, index_order_old="ijklm", index_order_new="kijml")
>>> expr
PermuteDims(M, (0 2 1)(3 4))
>>> expr.shape
(3, 1, 2, 5, 4)

Permutations of tensor products are simplified in order to achieve a standard form:

>>> from sympy.tensor.array import tensorproduct
>>> M = MatrixSymbol("M", 4, 5)
>>> tp = tensorproduct(M, N)
>>> tp.shape
(4, 5, 3, 2)
>>> perm1 = permutedims(tp, [2, 3, 1, 0])

The args (M, N) have been sorted and the permutation has been simplified, the expression is equivalent:

>>> perm1.expr.args
(N, M)
>>> perm1.shape
(3, 2, 5, 4)
>>> perm1.permutation
(2 3)

The permutation in its array form has been simplified from [2, 3, 1, 0] to [0, 1, 3, 2], as the arguments of the tensor product \(M\) and \(N\) have been switched:

>>> perm1.permutation.array_form
[0, 1, 3, 2]

We can nest a second permutation:

>>> perm2 = permutedims(perm1, [1, 0, 2, 3])
>>> perm2.shape
(2, 3, 5, 4)
>>> perm2.permutation.array_form
[1, 0, 3, 2]