/
arrayop.py
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/
arrayop.py
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import collections
import itertools
from ...combinatorics import Permutation
from ...core import Integer, Tuple, diff
from ...matrices import MatrixBase
from .dense_ndim_array import ImmutableDenseNDimArray
from .ndim_array import NDimArray
def _arrayfy(a):
if isinstance(a, NDimArray):
return a
if isinstance(a, (MatrixBase, list, tuple, Tuple)):
return ImmutableDenseNDimArray(a)
return a
def tensorproduct(*args):
"""
Tensor product among scalars or array-like objects.
Examples
========
>>> A = Array([[1, 2], [3, 4]])
>>> B = Array([x, y])
>>> tensorproduct(A, B)
[[[x, y], [2*x, 2*y]], [[3*x, 3*y], [4*x, 4*y]]]
>>> tensorproduct(A, x)
[[x, 2*x], [3*x, 4*x]]
>>> tensorproduct(A, B, B)
[[[[x**2, x*y], [x*y, y**2]], [[2*x**2, 2*x*y], [2*x*y, 2*y**2]]],
[[[3*x**2, 3*x*y], [3*x*y, 3*y**2]], [[4*x**2, 4*x*y], [4*x*y, 4*y**2]]]]
Applying this function on two matrices will result in a rank 4 array.
>>> m = Matrix([[x, y], [z, t]])
>>> p = tensorproduct(eye(3), m)
>>> p
[[[[x, y], [z, t]], [[0, 0], [0, 0]], [[0, 0], [0, 0]]],
[[[0, 0], [0, 0]], [[x, y], [z, t]], [[0, 0], [0, 0]]],
[[[0, 0], [0, 0]], [[0, 0], [0, 0]], [[x, y], [z, t]]]]
"""
if len(args) == 0:
return Integer(1)
if len(args) == 1:
return _arrayfy(args[0])
if len(args) > 2:
return tensorproduct(tensorproduct(args[0], args[1]), *args[2:])
# length of args is 2:
a, b = map(_arrayfy, args)
if not isinstance(a, NDimArray) or not isinstance(b, NDimArray):
return a*b
al = list(a)
bl = list(b)
product_list = [i*j for i in al for j in bl]
return ImmutableDenseNDimArray(product_list, a.shape + b.shape)
def tensorcontraction(array, *contraction_axes):
"""
Contraction of an array-like object on the specified axes.
Examples
========
>>> tensorcontraction(eye(3), (0, 1))
3
>>> A = Array(range(18), (3, 2, 3))
>>> A
[[[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]],
[[12, 13, 14], [15, 16, 17]]]
>>> tensorcontraction(A, (0, 2))
[21, 30]
Matrix multiplication may be emulated with a proper combination of
``tensorcontraction`` and ``tensorproduct``
>>> from diofant.abc import e, f, g, h
>>> m1 = Matrix([[a, b], [c, d]])
>>> m2 = Matrix([[e, f], [g, h]])
>>> p = tensorproduct(m1, m2)
>>> p
[[[[a*e, a*f], [a*g, a*h]], [[b*e, b*f], [b*g, b*h]]],
[[[c*e, c*f], [c*g, c*h]], [[d*e, d*f], [d*g, d*h]]]]
>>> tensorcontraction(p, (1, 2))
[[a*e + b*g, a*f + b*h], [c*e + d*g, c*f + d*h]]
>>> m1*m2
Matrix([
[a*e + b*g, a*f + b*h],
[c*e + d*g, c*f + d*h]])
"""
array = _arrayfy(array)
# Verify contraction_axes:
taken_dims = set()
for axes_group in contraction_axes:
if not isinstance(axes_group, collections.abc.Iterable):
raise ValueError('collections of contraction axes expected')
dim = array.shape[axes_group[0]]
for d in axes_group:
if d in taken_dims:
raise ValueError('dimension specified more than once')
if dim != array.shape[d]:
raise ValueError('cannot contract between axes of different dimension')
taken_dims.add(d)
rank = array.rank()
remaining_shape = [dim for i, dim in enumerate(array.shape) if i not in taken_dims]
cum_shape = [0]*rank
_cumul = 1
for i in range(rank):
cum_shape[rank - i - 1] = _cumul
_cumul *= int(array.shape[rank - i - 1])
# DEFINITION: by absolute position it is meant the position along the one
# dimensional array containing all the tensor components.
# Possible future work on this module: move computation of absolute
# positions to a class method.
# Determine absolute positions of the uncontracted indices:
remaining_indices = [[cum_shape[i]*j for j in range(array.shape[i])]
for i in range(rank) if i not in taken_dims]
# Determine absolute positions of the contracted indices:
summed_deltas = []
for axes_group in contraction_axes:
lidx = []
for js in range(array.shape[axes_group[0]]):
lidx.append(sum(cum_shape[ig] * js for ig in axes_group))
summed_deltas.append(lidx)
# Compute the contracted array:
#
# 1. external for loops on all uncontracted indices.
# Uncontracted indices are determined by the combinatorial product of
# the absolute positions of the remaining indices.
# 2. internal loop on all contracted indices.
# It sum the values of the absolute contracted index and the absolute
# uncontracted index for the external loop.
contracted_array = []
for icontrib in itertools.product(*remaining_indices):
index_base_position = sum(icontrib)
isum = Integer(0)
for sum_to_index in itertools.product(*summed_deltas):
isum += array[index_base_position + sum(sum_to_index)]
contracted_array.append(isum)
if len(remaining_indices) == 0:
assert len(contracted_array) == 1
return contracted_array[0]
return type(array)(contracted_array, remaining_shape)
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
========
>>> 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.abc.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)
new_array = [[y.diff(x) for y in expr] for x in dx]
return type(expr)(new_array, dx.shape + expr.shape)
if isinstance(dx, array_types):
return ImmutableDenseNDimArray([expr.diff(i) for i in dx], dx.shape)
return diff(expr, dx)
def permutedims(expr, perm):
"""
Permutes the indices of an array.
Parameter specifies the permutation of the indices.
Examples
========
>>> a = Array([[x, y, z], [t, sin(x), 0]])
>>> a
[[x, y, z], [t, sin(x), 0]]
>>> permutedims(a, (1, 0))
[[x, t], [y, sin(x)], [z, 0]]
If the array is of second order, ``transpose`` can be used:
>>> transpose(a)
[[x, t], [y, sin(x)], [z, 0]]
Examples on higher dimensions:
>>> b = Array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
>>> permutedims(b, (2, 1, 0))
[[[1, 5], [3, 7]], [[2, 6], [4, 8]]]
>>> permutedims(b, (1, 2, 0))
[[[1, 5], [2, 6]], [[3, 7], [4, 8]]]
``Permutation`` objects are also allowed:
>>> permutedims(b, Permutation([1, 2, 0]))
[[[1, 5], [2, 6]], [[3, 7], [4, 8]]]
"""
if not isinstance(expr, NDimArray):
raise TypeError('expression has to be an N-dim array')
if not isinstance(perm, Permutation):
perm = Permutation(list(perm))
if perm.size != expr.rank():
raise ValueError('wrong permutation size')
# Get the inverse permutation:
iperm = ~perm
indices_span = perm([range(i) for i in expr.shape])
new_array = [None]*len(expr)
for i, idx in enumerate(itertools.product(*indices_span)):
t = iperm(idx)
new_array[i] = expr[t]
new_shape = perm(expr.shape)
return expr.func(new_array, new_shape)