# Tensor Operators#

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

Partial derivative for tensor expressions.

Examples

>>> from sympy.tensor.tensor import TensorIndexType, TensorHead
>>> from sympy.tensor.toperators import PartialDerivative
>>> from sympy import symbols
>>> L = TensorIndexType("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)]]