def test_ndim_array_converting(): dense_array = ImmutableDenseNDimArray([1, 2, 3, 4], (2, 2)) alist = dense_array.tolist() alist == [[1, 2], [3, 4]] matrix = dense_array.tomatrix() assert (isinstance(matrix, Matrix)) for i in range(len(dense_array)): assert dense_array[i] == matrix[i] assert matrix.shape == dense_array.shape assert ImmutableDenseNDimArray(matrix) == dense_array assert ImmutableDenseNDimArray(matrix.as_immutable()) == dense_array assert ImmutableDenseNDimArray(matrix.as_mutable()) == dense_array sparse_array = ImmutableSparseNDimArray([1, 2, 3, 4], (2, 2)) alist = sparse_array.tolist() assert alist == [[1, 2], [3, 4]] matrix = sparse_array.tomatrix() assert(isinstance(matrix, SparseMatrix)) for i in range(len(sparse_array)): assert sparse_array[i] == matrix[i] assert matrix.shape == sparse_array.shape assert ImmutableSparseNDimArray(matrix) == sparse_array assert ImmutableSparseNDimArray(matrix.as_immutable()) == sparse_array assert ImmutableSparseNDimArray(matrix.as_mutable()) == sparse_array
def test_reshape(): array = ImmutableDenseNDimArray(range(50), 50) assert array.shape == (50,) assert array.rank() == 1 array = array.reshape(5, 5, 2) assert array.shape == (5, 5, 2) assert array.rank() == 3 assert len(array) == 50
def test_iterator(): array = ImmutableDenseNDimArray(range(4), (2, 2)) j = 0 for i in array: assert i == j j += 1 array = array.reshape(4) j = 0 for i in array: assert i == j j += 1
def test_converting_functions(): arr_list = [1, 2, 3, 4] arr_matrix = Matrix(((1, 2), (3, 4))) # list arr_ndim_array = ImmutableDenseNDimArray(arr_list, (2, 2)) assert (isinstance(arr_ndim_array, ImmutableDenseNDimArray)) assert arr_matrix.tolist() == arr_ndim_array.tolist() # Matrix arr_ndim_array = ImmutableDenseNDimArray(arr_matrix) assert (isinstance(arr_ndim_array, ImmutableDenseNDimArray)) assert arr_matrix.tolist() == arr_ndim_array.tolist() assert arr_matrix.shape == arr_ndim_array.shape
def test_diff_and_applyfunc(): from sympy.abc import x, y, z md = ImmutableDenseNDimArray([[x, y], [x*z, x*y*z]]) assert md.diff(x) == ImmutableDenseNDimArray([[1, 0], [z, y*z]]) sd = ImmutableSparseNDimArray(md) assert sd == ImmutableSparseNDimArray([x, y, x*z, x*y*z], (2, 2)) assert sd.diff(x) == ImmutableSparseNDimArray([[1, 0], [z, y*z]]) mdn = md.applyfunc(lambda x: x*3) assert mdn == ImmutableDenseNDimArray([[3*x, 3*y], [3*x*z, 3*x*y*z]]) assert md != mdn sdn = sd.applyfunc(lambda x: x/2) assert sdn == ImmutableSparseNDimArray([[x/2, y/2], [x*z/2, x*y*z/2]]) assert sd != sdn
def test_ndim_array_converting(): dense_array = ImmutableDenseNDimArray([1, 2, 3, 4], (2, 2)) alist = dense_array.tolist() alist == [[1, 2], [3, 4]] matrix = dense_array.tomatrix() assert (isinstance(matrix, Matrix)) for i in range(len(dense_array)): assert dense_array[i] == matrix[i] assert matrix.shape == dense_array.shape assert ImmutableDenseNDimArray(matrix) == dense_array assert ImmutableDenseNDimArray(matrix.as_immutable()) == dense_array assert ImmutableDenseNDimArray(matrix.as_mutable()) == dense_array sparse_array = ImmutableSparseNDimArray([1, 2, 3, 4], (2, 2)) alist = sparse_array.tolist() assert alist == [[1, 2], [3, 4]] matrix = sparse_array.tomatrix() assert (isinstance(matrix, SparseMatrix)) for i in range(len(sparse_array)): assert sparse_array[i] == matrix[i] assert matrix.shape == sparse_array.shape assert ImmutableSparseNDimArray(matrix) == sparse_array assert ImmutableSparseNDimArray(matrix.as_immutable()) == sparse_array assert ImmutableSparseNDimArray(matrix.as_mutable()) == sparse_array
def test_diff_and_applyfunc(): from sympy.abc import x, y, z md = ImmutableDenseNDimArray([[x, y], [x * z, x * y * z]]) assert md.diff(x) == ImmutableDenseNDimArray([[1, 0], [z, y * z]]) assert diff(md, x) == ImmutableDenseNDimArray([[1, 0], [z, y * z]]) sd = ImmutableSparseNDimArray(md) assert sd == ImmutableSparseNDimArray([x, y, x * z, x * y * z], (2, 2)) assert sd.diff(x) == ImmutableSparseNDimArray([[1, 0], [z, y * z]]) assert diff(sd, x) == ImmutableSparseNDimArray([[1, 0], [z, y * z]]) mdn = md.applyfunc(lambda x: x * 3) assert mdn == ImmutableDenseNDimArray([[3 * x, 3 * y], [3 * x * z, 3 * x * y * z]]) assert md != mdn sdn = sd.applyfunc(lambda x: x / 2) assert sdn == ImmutableSparseNDimArray([[x / 2, y / 2], [x * z / 2, x * y * z / 2]]) assert sd != sdn sdp = sd.applyfunc(lambda x: x + 1) assert sdp == ImmutableSparseNDimArray([[x + 1, y + 1], [x * z + 1, x * y * z + 1]]) assert sd != sdp
def test_calculation(): a = ImmutableDenseNDimArray([1] * 9, (3, 3)) b = ImmutableDenseNDimArray([9] * 9, (3, 3)) c = a + b for i in c: assert i == 10 assert c == ImmutableDenseNDimArray([10] * 9, (3, 3)) assert c == ImmutableSparseNDimArray([10] * 9, (3, 3)) c = b - a for i in c: assert i == 8 assert c == ImmutableDenseNDimArray([8] * 9, (3, 3)) assert c == ImmutableSparseNDimArray([8] * 9, (3, 3))
def test_slices(): md = ImmutableDenseNDimArray(range(10, 34), (2, 3, 4)) assert md[:] == md._array assert md[:, :, 0].tomatrix() == Matrix([[10, 14, 18], [22, 26, 30]]) assert md[0, 1:2, :].tomatrix() == Matrix([[14, 15, 16, 17]]) assert md[0, 1:3, :].tomatrix() == Matrix([[14, 15, 16, 17], [18, 19, 20, 21]]) assert md[:, :, :] == md
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 test_equality(): first_list = [1, 2, 3, 4] second_list = [1, 2, 3, 4] third_list = [4, 3, 2, 1] assert first_list == second_list assert first_list != third_list first_ndim_array = ImmutableDenseNDimArray(first_list, (2, 2)) second_ndim_array = ImmutableDenseNDimArray(second_list, (2, 2)) fourth_ndim_array = ImmutableDenseNDimArray(first_list, (2, 2)) assert first_ndim_array == second_ndim_array def assignment_attempt(a): a[0, 0] = 0 raises(TypeError, lambda: assignment_attempt(second_ndim_array)) assert first_ndim_array == second_ndim_array assert first_ndim_array == fourth_ndim_array
def test_array_symbol_and_element(): A = ArraySymbol("A", (2,)) A0 = ArrayElement(A, (0,)) A1 = ArrayElement(A, (1,)) assert A[0] == A0 assert A[1] != A0 assert A.as_explicit() == ImmutableDenseNDimArray([A0, A1]) A2 = tensorproduct(A, A) assert A2.shape == (2, 2) # TODO: not yet supported: # assert A2.as_explicit() == Array([[A[0]*A[0], A[1]*A[0]], [A[0]*A[1], A[1]*A[1]]]) A3 = tensorcontraction(A2, (0, 1)) assert A3.shape == () # TODO: not yet supported: # assert A3.as_explicit() == Array([]) A = ArraySymbol("A", (2, 3, 4)) Ae = A.as_explicit() assert Ae == ImmutableDenseNDimArray( [[[ArrayElement(A, (i, j, k)) for k in range(4)] for j in range(3)] for i in range(2)]) p = _permute_dims(A, Permutation(0, 2, 1)) assert isinstance(p, PermuteDims) A = ArraySymbol("A", (2,)) raises(IndexError, lambda: A[()]) raises(IndexError, lambda: A[0, 1]) raises(ValueError, lambda: A[-1]) raises(ValueError, lambda: A[2]) O = OneArray(3, 4) Z = ZeroArray(m, n) raises(IndexError, lambda: O[()]) raises(IndexError, lambda: O[1, 2, 3]) raises(ValueError, lambda: O[3, 0]) raises(ValueError, lambda: O[0, 4]) assert O[1, 2] == 1 assert Z[1, 2] == 0
def test_symbolic_indexing(): x, y, z, w = symbols("x y z w") M = ImmutableDenseNDimArray([[x, y], [z, w]]) i, j = symbols("i, j") Mij = M[i, j] assert isinstance(Mij, Indexed) Ms = ImmutableSparseNDimArray([[2, 3 * x], [4, 5]]) msij = Ms[i, j] assert isinstance(msij, Indexed) for oi, oj in [(0, 0), (0, 1), (1, 0), (1, 1)]: assert Mij.subs({i: oi, j: oj}) == M[oi, oj] assert msij.subs({i: oi, j: oj}) == Ms[oi, oj] A = IndexedBase("A", (0, 2)) assert A[0, 0].subs(A, M) == x assert A[i, j].subs(A, M) == M[i, j] assert M[i, j].subs(M, A) == A[i, j] assert isinstance(M[3 * i - 2, j], Indexed) assert M[3 * i - 2, j].subs({i: 1, j: 0}) == M[1, 0] assert isinstance(M[i, 0], Indexed) assert M[i, 0].subs(i, 0) == M[0, 0] assert M[0, i].subs(i, 1) == M[0, 1] assert M[i, j].diff(x) == ImmutableDenseNDimArray([[1, 0], [0, 0]])[i, j] assert Ms[i, j].diff(x) == ImmutableSparseNDimArray([[0, 3], [0, 0]])[i, j] Mo = ImmutableDenseNDimArray([1, 2, 3]) assert Mo[i].subs(i, 1) == 2 Mos = ImmutableSparseNDimArray([1, 2, 3]) assert Mos[i].subs(i, 1) == 2 raises(ValueError, lambda: M[i, 2]) raises(ValueError, lambda: M[i, -1]) raises(ValueError, lambda: M[2, i]) raises(ValueError, lambda: M[-1, i]) raises(ValueError, lambda: Ms[i, 2]) raises(ValueError, lambda: Ms[i, -1]) raises(ValueError, lambda: Ms[2, i]) raises(ValueError, lambda: Ms[-1, i])
def tensorproduct(*args): """ Tensor product among scalars or array-like objects. Examples ======== >>> from sympy.tensor.array import tensorproduct, Array >>> from sympy.abc import x, y, z, t >>> 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. >>> from sympy import Matrix, eye >>> 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]]]] """ from sympy.tensor.array import SparseNDimArray, ImmutableSparseNDimArray if len(args) == 0: return S.One 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 if isinstance(a, SparseNDimArray) and isinstance(b, SparseNDimArray): lp = len(b) new_array = { k1 * lp + k2: v1 * v2 for k1, v1 in a._sparse_array.items() for k2, v2 in b._sparse_array.items() } return ImmutableSparseNDimArray(new_array, a.shape + b.shape) product_list = [i * j for i in Flatten(a) for j in Flatten(b)] return ImmutableDenseNDimArray(product_list, a.shape + b.shape)
def test_array_symbol_and_element(): A = ArraySymbol("A", (2,)) A0 = ArrayElement(A, (0,)) A1 = ArrayElement(A, (1,)) assert A.as_explicit() == ImmutableDenseNDimArray([A0, A1]) A2 = tensorproduct(A, A) assert A2.shape == (2, 2) # TODO: not yet supported: # assert A2.as_explicit() == Array([[A[0]*A[0], A[1]*A[0]], [A[0]*A[1], A[1]*A[1]]]) A3 = tensorcontraction(A2, (0, 1)) assert A3.shape == () # TODO: not yet supported: # assert A3.as_explicit() == Array([]) A = ArraySymbol("A", (2, 3, 4)) Ae = A.as_explicit() assert Ae == ImmutableDenseNDimArray( [[[ArrayElement(A, (i, j, k)) for k in range(4)] for j in range(3)] for i in range(2)]) p = _permute_dims(A, Permutation(0, 2, 1)) assert isinstance(p, PermuteDims)
def test_higher_dimenions(): m3 = ImmutableDenseNDimArray(range(10, 34), (2, 3, 4)) assert m3.tolist() == [[[10, 11, 12, 13], [14, 15, 16, 17], [18, 19, 20, 21]], [[22, 23, 24, 25], [26, 27, 28, 29], [30, 31, 32, 33]]] assert m3._get_tuple_index(0) == (0, 0, 0) assert m3._get_tuple_index(1) == (0, 0, 1) assert m3._get_tuple_index(4) == (0, 1, 0) assert m3._get_tuple_index(12) == (1, 0, 0) assert str(m3) == '[[[10, 11, 12, 13], [14, 15, 16, 17], [18, 19, 20, 21]], [[22, 23, 24, 25], [26, 27, 28, 29], [30, 31, 32, 33]]]' m3_rebuilt = ImmutableDenseNDimArray([[[10, 11, 12, 13], [14, 15, 16, 17], [18, 19, 20, 21]], [[22, 23, 24, 25], [26, 27, 28, 29], [30, 31, 32, 33]]]) assert m3 == m3_rebuilt m3_other = ImmutableDenseNDimArray([[[10, 11, 12, 13], [14, 15, 16, 17], [18, 19, 20, 21]], [[22, 23, 24, 25], [26, 27, 28, 29], [30, 31, 32, 33]]], (2, 3, 4)) assert m3 == m3_other
def test_rebuild_immutable_arrays(): sparr = ImmutableSparseNDimArray(range(10, 34), (2, 3, 4)) densarr = ImmutableDenseNDimArray(range(10, 34), (2, 3, 4)) assert sparr == sparr.func(*sparr.args) assert densarr == densarr.func(*densarr.args)
def permutedims(expr, perm=None, index_order_old=None, index_order_new=None): """ Permutes the indices of an array. Parameter specifies the permutation of the indices. Examples ======== >>> from sympy.abc import x, y, z, t >>> from sympy import sin >>> from sympy import Array, permutedims >>> 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: >>> from sympy import transpose >>> 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]]] An alternative way to specify the same permutations as in the previous lines involves passing the *old* and *new* indices, either as a list or as a string: >>> permutedims(b, index_order_old="cba", index_order_new="abc") [[[1, 5], [3, 7]], [[2, 6], [4, 8]]] >>> permutedims(b, index_order_old="cab", index_order_new="abc") [[[1, 5], [2, 6]], [[3, 7], [4, 8]]] ``Permutation`` objects are also allowed: >>> from sympy.combinatorics import Permutation >>> permutedims(b, Permutation([1, 2, 0])) [[[1, 5], [2, 6]], [[3, 7], [4, 8]]] """ from sympy.tensor.array import SparseNDimArray from sympy.tensor.array.expressions.array_expressions import _ArrayExpr from sympy.tensor.array.expressions.array_expressions import _CodegenArrayAbstract from sympy.tensor.array.expressions.array_expressions import _permute_dims from sympy.matrices.expressions.matexpr import MatrixSymbol from sympy.tensor.array.expressions import PermuteDims from sympy.tensor.array.expressions.array_expressions import get_rank perm = PermuteDims._get_permutation_from_arguments(perm, index_order_old, index_order_new, get_rank(expr)) if isinstance(expr, (_ArrayExpr, _CodegenArrayAbstract, MatrixSymbol)): return _permute_dims(expr, perm) if not isinstance(expr, NDimArray): expr = ImmutableDenseNDimArray(expr) from sympy.combinatorics import Permutation 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 new_shape = perm(expr.shape) if isinstance(expr, SparseNDimArray): return type(expr)({ tuple(perm(expr._get_tuple_index(k))): v for k, v in expr._sparse_array.items() }, new_shape) 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] return type(expr)(new_array, new_shape)
def test_ndim_array_initiation(): arr_with_no_elements = ImmutableDenseNDimArray([], shape=(0, )) assert len(arr_with_no_elements) == 0 assert arr_with_no_elements.rank() == 1 raises(ValueError, lambda: ImmutableDenseNDimArray([0], shape=(0, ))) raises(ValueError, lambda: ImmutableDenseNDimArray([1, 2, 3], shape=(0, ))) raises(ValueError, lambda: ImmutableDenseNDimArray([], shape=())) raises(ValueError, lambda: ImmutableSparseNDimArray([0], shape=(0, ))) raises(ValueError, lambda: ImmutableSparseNDimArray([1, 2, 3], shape=(0, ))) raises(ValueError, lambda: ImmutableSparseNDimArray([], shape=())) arr_with_one_element = ImmutableDenseNDimArray([23]) assert len(arr_with_one_element) == 1 assert arr_with_one_element[0] == 23 assert arr_with_one_element[:] == ImmutableDenseNDimArray([23]) assert arr_with_one_element.rank() == 1 arr_with_symbol_element = ImmutableDenseNDimArray([Symbol('x')]) assert len(arr_with_symbol_element) == 1 assert arr_with_symbol_element[0] == Symbol('x') assert arr_with_symbol_element[:] == ImmutableDenseNDimArray([Symbol('x')]) assert arr_with_symbol_element.rank() == 1 number5 = 5 vector = ImmutableDenseNDimArray.zeros(number5) assert len(vector) == number5 assert vector.shape == (number5, ) assert vector.rank() == 1 vector = ImmutableSparseNDimArray.zeros(number5) assert len(vector) == number5 assert vector.shape == (number5, ) assert vector._sparse_array == Dict() assert vector.rank() == 1 n_dim_array = ImmutableDenseNDimArray(range(3**4), ( 3, 3, 3, 3, )) assert len(n_dim_array) == 3 * 3 * 3 * 3 assert n_dim_array.shape == (3, 3, 3, 3) assert n_dim_array.rank() == 4 array_shape = (3, 3, 3, 3) sparse_array = ImmutableSparseNDimArray.zeros(*array_shape) assert len(sparse_array._sparse_array) == 0 assert len(sparse_array) == 3 * 3 * 3 * 3 assert n_dim_array.shape == array_shape assert n_dim_array.rank() == 4 one_dim_array = ImmutableDenseNDimArray([2, 3, 1]) assert len(one_dim_array) == 3 assert one_dim_array.shape == (3, ) assert one_dim_array.rank() == 1 assert one_dim_array.tolist() == [2, 3, 1] shape = (3, 3) array_with_many_args = ImmutableSparseNDimArray.zeros(*shape) assert len(array_with_many_args) == 3 * 3 assert array_with_many_args.shape == shape assert array_with_many_args[0, 0] == 0 assert array_with_many_args.rank() == 2 shape = (long(3), long(3)) array_with_long_shape = ImmutableSparseNDimArray.zeros(*shape) assert len(array_with_long_shape) == 3 * 3 assert array_with_long_shape.shape == shape assert array_with_long_shape[long(0), long(0)] == 0 assert array_with_long_shape.rank() == 2 vector_with_long_shape = ImmutableDenseNDimArray(range(5), long(5)) assert len(vector_with_long_shape) == 5 assert vector_with_long_shape.shape == (long(5), ) assert vector_with_long_shape.rank() == 1 raises(ValueError, lambda: vector_with_long_shape[long(5)]) from sympy.abc import x for ArrayType in [ImmutableDenseNDimArray, ImmutableSparseNDimArray]: rank_zero_array = ArrayType(x) assert len(rank_zero_array) == 1 assert rank_zero_array.shape == () assert rank_zero_array.rank() == 0 assert rank_zero_array[()] == x raises(ValueError, lambda: rank_zero_array[0])
def test_ndim_array_initiation(): arr_with_one_element = ImmutableDenseNDimArray([23]) assert len(arr_with_one_element) == 1 assert arr_with_one_element[0] == 23 assert arr_with_one_element[:] == [23] assert arr_with_one_element.rank() == 1 arr_with_symbol_element = ImmutableDenseNDimArray([Symbol('x')]) assert len(arr_with_symbol_element) == 1 assert arr_with_symbol_element[0] == Symbol('x') assert arr_with_symbol_element[:] == [Symbol('x')] assert arr_with_symbol_element.rank() == 1 number5 = 5 vector = ImmutableDenseNDimArray.zeros(number5) assert len(vector) == number5 assert vector.shape == (number5, ) assert vector.rank() == 1 vector = ImmutableSparseNDimArray.zeros(number5) assert len(vector) == number5 assert vector.shape == (number5, ) assert vector._sparse_array == Dict() assert vector.rank() == 1 n_dim_array = ImmutableDenseNDimArray(range(3**4), ( 3, 3, 3, 3, )) assert len(n_dim_array) == 3 * 3 * 3 * 3 assert n_dim_array.shape == (3, 3, 3, 3) assert n_dim_array.rank() == 4 array_shape = (3, 3, 3, 3) sparse_array = ImmutableSparseNDimArray.zeros(*array_shape) assert len(sparse_array._sparse_array) == 0 assert len(sparse_array) == 3 * 3 * 3 * 3 assert n_dim_array.shape == array_shape assert n_dim_array.rank() == 4 one_dim_array = ImmutableDenseNDimArray([2, 3, 1]) assert len(one_dim_array) == 3 assert one_dim_array.shape == (3, ) assert one_dim_array.rank() == 1 assert one_dim_array.tolist() == [2, 3, 1] shape = (3, 3) array_with_many_args = ImmutableSparseNDimArray.zeros(*shape) assert len(array_with_many_args) == 3 * 3 assert array_with_many_args.shape == shape assert array_with_many_args[0, 0] == 0 assert array_with_many_args.rank() == 2
def test_ndim_array_initiation(): arr_with_one_element = ImmutableDenseNDimArray([23]) assert len(arr_with_one_element) == 1 assert arr_with_one_element[0] == 23 assert arr_with_one_element[:] == [23] assert arr_with_one_element.rank() == 1 arr_with_symbol_element = ImmutableDenseNDimArray([Symbol('x')]) assert len(arr_with_symbol_element) == 1 assert arr_with_symbol_element[0] == Symbol('x') assert arr_with_symbol_element[:] == [Symbol('x')] assert arr_with_symbol_element.rank() == 1 number5 = 5 vector = ImmutableDenseNDimArray.zeros(number5) assert len(vector) == number5 assert vector.shape == (number5,) assert vector.rank() == 1 vector = ImmutableSparseNDimArray.zeros(number5) assert len(vector) == number5 assert vector.shape == (number5,) assert vector._sparse_array == Dict() assert vector.rank() == 1 n_dim_array = ImmutableDenseNDimArray(range(3**4), (3, 3, 3, 3,)) assert len(n_dim_array) == 3 * 3 * 3 * 3 assert n_dim_array.shape == (3, 3, 3, 3) assert n_dim_array.rank() == 4 array_shape = (3, 3, 3, 3) sparse_array = ImmutableSparseNDimArray.zeros(*array_shape) assert len(sparse_array._sparse_array) == 0 assert len(sparse_array) == 3 * 3 * 3 * 3 assert n_dim_array.shape == array_shape assert n_dim_array.rank() == 4 one_dim_array = ImmutableDenseNDimArray([2, 3, 1]) assert len(one_dim_array) == 3 assert one_dim_array.shape == (3,) assert one_dim_array.rank() == 1 assert one_dim_array.tolist() == [2, 3, 1] shape = (3, 3) array_with_many_args = ImmutableSparseNDimArray.zeros(*shape) assert len(array_with_many_args) == 3 * 3 assert array_with_many_args.shape == shape assert array_with_many_args[0, 0] == 0 assert array_with_many_args.rank() == 2
def permutedims(expr, perm): """ Permutes the indices of an array. Parameter specifies the permutation of the indices. Examples ======== >>> from sympy.abc import x, y, z, t >>> from sympy import sin >>> from sympy import Array, permutedims >>> 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: >>> from sympy import transpose >>> 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: >>> from sympy.combinatorics import Permutation >>> permutedims(b, Permutation([1, 2, 0])) [[[1, 5], [2, 6]], [[3, 7], [4, 8]]] """ from sympy.tensor.array import SparseNDimArray if not isinstance(expr, NDimArray): expr = ImmutableDenseNDimArray(expr) from sympy.combinatorics import Permutation 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 new_shape = perm(expr.shape) if isinstance(expr, SparseNDimArray): return type(expr)({ tuple(perm(expr._get_tuple_index(k))): v for k, v in expr._sparse_array.items() }, new_shape) 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] return type(expr)(new_array, new_shape)
def test_zeros_without_shape(): arr = ImmutableDenseNDimArray.zeros() assert arr == ImmutableDenseNDimArray(0)
def test_issue_21870(): a0 = ImmutableDenseNDimArray(0) assert a0.rank() == 0 a1 = ImmutableDenseNDimArray(a0) assert a1.rank() == 0
def test_issue_12665(): # Testing Python 3 hash of immutable arrays: arr = ImmutableDenseNDimArray([1, 2, 3]) # This should NOT raise an exception: hash(arr)
def test_ndim_array_initiation(): arr_with_no_elements = ImmutableDenseNDimArray([], shape=(0,)) assert len(arr_with_no_elements) == 0 assert arr_with_no_elements.rank() == 1 raises(ValueError, lambda: ImmutableDenseNDimArray([0], shape=(0,))) raises(ValueError, lambda: ImmutableDenseNDimArray([1, 2, 3], shape=(0,))) raises(ValueError, lambda: ImmutableDenseNDimArray([], shape=())) raises(ValueError, lambda: ImmutableSparseNDimArray([0], shape=(0,))) raises(ValueError, lambda: ImmutableSparseNDimArray([1, 2, 3], shape=(0,))) raises(ValueError, lambda: ImmutableSparseNDimArray([], shape=())) arr_with_one_element = ImmutableDenseNDimArray([23]) assert len(arr_with_one_element) == 1 assert arr_with_one_element[0] == 23 assert arr_with_one_element[:] == [23] assert arr_with_one_element.rank() == 1 arr_with_symbol_element = ImmutableDenseNDimArray([Symbol('x')]) assert len(arr_with_symbol_element) == 1 assert arr_with_symbol_element[0] == Symbol('x') assert arr_with_symbol_element[:] == [Symbol('x')] assert arr_with_symbol_element.rank() == 1 number5 = 5 vector = ImmutableDenseNDimArray.zeros(number5) assert len(vector) == number5 assert vector.shape == (number5,) assert vector.rank() == 1 vector = ImmutableSparseNDimArray.zeros(number5) assert len(vector) == number5 assert vector.shape == (number5,) assert vector._sparse_array == Dict() assert vector.rank() == 1 n_dim_array = ImmutableDenseNDimArray(range(3**4), (3, 3, 3, 3,)) assert len(n_dim_array) == 3 * 3 * 3 * 3 assert n_dim_array.shape == (3, 3, 3, 3) assert n_dim_array.rank() == 4 array_shape = (3, 3, 3, 3) sparse_array = ImmutableSparseNDimArray.zeros(*array_shape) assert len(sparse_array._sparse_array) == 0 assert len(sparse_array) == 3 * 3 * 3 * 3 assert n_dim_array.shape == array_shape assert n_dim_array.rank() == 4 one_dim_array = ImmutableDenseNDimArray([2, 3, 1]) assert len(one_dim_array) == 3 assert one_dim_array.shape == (3,) assert one_dim_array.rank() == 1 assert one_dim_array.tolist() == [2, 3, 1] shape = (3, 3) array_with_many_args = ImmutableSparseNDimArray.zeros(*shape) assert len(array_with_many_args) == 3 * 3 assert array_with_many_args.shape == shape assert array_with_many_args[0, 0] == 0 assert array_with_many_args.rank() == 2 shape = (long(3), long(3)) array_with_long_shape = ImmutableSparseNDimArray.zeros(*shape) assert len(array_with_long_shape) == 3 * 3 assert array_with_long_shape.shape == shape assert array_with_long_shape[long(0), long(0)] == 0 assert array_with_long_shape.rank() == 2 vector_with_long_shape = ImmutableDenseNDimArray(range(5), long(5)) assert len(vector_with_long_shape) == 5 assert vector_with_long_shape.shape == (long(5),) assert vector_with_long_shape.rank() == 1 raises(ValueError, lambda: vector_with_long_shape[long(5)]) from sympy.abc import x rank_zero_array = ImmutableDenseNDimArray(x) assert len(rank_zero_array) == 1 assert rank_zero_array.shape == () assert rank_zero_array.rank() == 0 assert rank_zero_array[()] == x assert rank_zero_array[0] == x