Tensor Operators

class sympy.tensor.toperators.PartialDerivative(expr, *variables)[source]

Partial derivative for tensor expressions.


>>> from sympy.tensor.tensor import TensorIndexType, TensorHead
>>> from sympy.tensor.toperators import PartialDerivative
>>> from sympy import symbols
>>> L = TensorIndexType("L")
>>> A = TensorHead("A", [L])
>>> B = TensorHead("B", [L])
>>> i, j, k = symbols("i j k")
>>> expr = PartialDerivative(A(i), A(j))
>>> expr
PartialDerivative(A(i), A(j))

The PartialDerivative object behaves like a tensorial expression:

>>> expr.get_indices()
[i, -j]

Notice that the deriving variables have opposite valence than the printed one: A(j) is printed as covariant, but the index of the derivative is actually contravariant, i.e. -j.

Indices can be contracted:

>>> expr = PartialDerivative(A(i), A(i))
>>> expr
PartialDerivative(A(L_0), A(L_0))
>>> expr.get_indices()
[L_0, -L_0]

The method .get_indices() always returns all indices (even the contracted ones). If only uncontracted indices are needed, call .get_free_indices():

>>> expr.get_free_indices()

Nested partial derivatives are flattened:

>>> expr = PartialDerivative(PartialDerivative(A(i), A(j)), A(k))
>>> expr
PartialDerivative(A(i), A(j), A(k))
>>> expr.get_indices()
[i, -j, -k]

Replace a derivative with array values:

>>> from sympy.abc import x, y
>>> from sympy import sin, log
>>> compA = [sin(x), log(x)*y**3]
>>> compB = [x, y]
>>> expr = PartialDerivative(A(i), B(j))
>>> expr.replace_with_arrays({A(i): compA, B(i): compB})
[[cos(x), 0], [y**3/x, 3*y**2*log(x)]]

The returned array is indexed by \((i, -j)\).

Be careful that other SymPy modules put the indices of the deriving variables before the indices of the derivand in the derivative result. For example:

>>> expr.get_free_indices()
[i, -j]
>>> from sympy import Matrix, Array
>>> Matrix(compA).diff(Matrix(compB)).reshape(2, 2)
[[cos(x), y**3/x], [0, 3*y**2*log(x)]]
>>> Array(compA).diff(Array(compB))
[[cos(x), y**3/x], [0, 3*y**2*log(x)]]

These are the transpose of the result of PartialDerivative, as the matrix and the array modules put the index \(-j\) before \(i\) in the derivative result. An array read with index order \((-j, i)\) is indeed the transpose of the same array read with index order \((i, -j)\). By specifying the index order to .replace_with_arrays one can get a compatible expression:

>>> expr.replace_with_arrays({A(i): compA, B(i): compB}, [-j, i])
[[cos(x), y**3/x], [0, 3*y**2*log(x)]]