def derive_by_array(expr, dx):
r"""
Derivative by arrays. Supports both arrays and scalars.
Given the array `A_{i_1, \ldots, i_N}` and the array `X_{j_1, \ldots, j_M}`
this function will return a new array `B` defined by
`B_{j_1,\ldots,j_M,i_1,\ldots,i_N} := \frac{\partial A_{i_1,\ldots,i_N}}{\partial X_{j_1,\ldots,j_M}}`
Examples
========
>>> from sympy import derive_by_array
>>> from sympy.abc import x, y, z, t
>>> from sympy import cos
>>> derive_by_array(cos(x*t), x)
-t*sin(t*x)
>>> derive_by_array(cos(x*t), [x, y, z, t])
[-t*sin(t*x), 0, 0, -x*sin(t*x)]
>>> derive_by_array([x, y**2*z], [[x, y], [z, t]])
[[[1, 0], [0, 2*y*z]], [[0, y**2], [0, 0]]]
"""
from sympy.matrices import MatrixBase
from sympy.tensor.array import SparseNDimArray
array_types = (Iterable, MatrixBase, NDimArray)
if isinstance(dx, array_types):
dx = ImmutableDenseNDimArray(dx)
for i in dx:
if not i._diff_wrt:
raise ValueError("cannot derive by this array")
if isinstance(expr, array_types):
if isinstance(expr, NDimArray):
expr = expr.as_immutable()
else:
expr = ImmutableDenseNDimArray(expr)
if isinstance(dx, array_types):
if isinstance(expr, SparseNDimArray):
lp = len(expr)
new_array = {
k + i * lp: v
for i, x in enumerate(Flatten(dx))
for k, v in expr.diff(x)._sparse_array.items()
}
else:
new_array = [[y.diff(x) for y in Flatten(expr)]
for x in Flatten(dx)]
return type(expr)(new_array, dx.shape + expr.shape)
else:
return expr.diff(dx)
else:
if isinstance(dx, array_types):
return ImmutableDenseNDimArray([expr.diff(i) for i in Flatten(dx)],
dx.shape)
else:
return diff(expr, dx)
def derive_by_array(expr, dx):
"""
Derivative by arrays. Supports both arrays and scalars.
Given the array `A_{i_1, \ldots, i_N}` and the array `X_{j_1, \ldots, j_M}`
this function will return a new array `B` defined by
`B_{j_1,\ldots,j_M,i_1,\ldots,i_N} := \frac{\partial A_{i_1,\ldots,i_N}}{\partial X_{j_1,\ldots,j_M}`
Examples
========
>>> from sympy.tensor.array import derive_by_array
>>> from sympy.abc import x, y, z, t
>>> from sympy import cos
>>> derive_by_array(cos(x*t), x)
-t*sin(t*x)
>>> derive_by_array(cos(x*t), [x, y, z, t])
[-t*sin(t*x), 0, 0, -x*sin(t*x)]
>>> derive_by_array([x, y**2*z], [[x, y], [z, t]])
[[[1, 0], [0, 2*y*z]], [[0, y**2], [0, 0]]]
"""
array_types = (collections.Iterable, MatrixBase, NDimArray)
if isinstance(dx, array_types):
dx = ImmutableDenseNDimArray(dx)
for i in dx:
if not i._diff_wrt:
raise ValueError("cannot derive by this array")
if isinstance(expr, array_types):
expr = ImmutableDenseNDimArray(expr)
if isinstance(dx, array_types):
new_array = [[y.diff(x) for y in expr] for x in dx]
return type(expr)(new_array, dx.shape + expr.shape)
else:
return expr.diff(dx)
else:
if isinstance(dx, array_types):
return ImmutableDenseNDimArray([expr.diff(i) for i in dx],
dx.shape)
else:
return diff(expr, dx)
def derive_by_array(expr, dx):
r"""
Derivative by arrays. Supports both arrays and scalars.
Given the array `A_{i_1, \ldots, i_N}` and the array `X_{j_1, \ldots, j_M}`
this function will return a new array `B` defined by
`B_{j_1,\ldots,j_M,i_1,\ldots,i_N} := \frac{\partial A_{i_1,\ldots,i_N}}{\partial X_{j_1,\ldots,j_M}}`
Examples
========
>>> from sympy import derive_by_array
>>> from sympy.abc import x, y, z, t
>>> from sympy import cos
>>> derive_by_array(cos(x*t), x)
-t*sin(t*x)
>>> derive_by_array(cos(x*t), [x, y, z, t])
[-t*sin(t*x), 0, 0, -x*sin(t*x)]
>>> derive_by_array([x, y**2*z], [[x, y], [z, t]])
[[[1, 0], [0, 2*y*z]], [[0, y**2], [0, 0]]]
"""
from sympy.matrices import MatrixBase
array_types = (collections.Iterable, MatrixBase, NDimArray)
if isinstance(dx, array_types):
dx = ImmutableDenseNDimArray(dx)
for i in dx:
if not i._diff_wrt:
raise ValueError("cannot derive by this array")
if isinstance(expr, array_types):
expr = ImmutableDenseNDimArray(expr)
if isinstance(dx, array_types):
new_array = [[y.diff(x) for y in expr] for x in dx]
return type(expr)(new_array, dx.shape + expr.shape)
else:
return expr.diff(dx)
else:
if isinstance(dx, array_types):
return ImmutableDenseNDimArray([expr.diff(i) for i in dx], dx.shape)
else:
return diff(expr, dx)