def __add__(self, other):
from sympy.tensor.array.arrayop import Flatten
if not isinstance(other, NDimArray):
raise TypeError(str(other))
if self.shape != other.shape:
raise ValueError("array shape mismatch")
result_list = [i+j for i,j in zip(Flatten(self), Flatten(other))]
return type(self)(result_list, self.shape)
def __sub__(self, other):
if not isinstance(other, NewArray):
result_list = [i - other for i in Flatten(self)]
return type(self)(result_list, self.shape)
if self.shape != other.shape:
raise ValueError("array shape mismatch")
result_list = [i - j for i, j in zip(Flatten(self), Flatten(other))]
return type(self)(result_list, self.shape)
def __rdiv__(self, other):
if isinstance(other, (np.ndarray, NewArray)):
if self.shape != other.shape:
raise ValueError("array shape mismatch")
else:
result_list = [
i / j for i, j in zip(Flatten(self), Flatten(other))
]
return type(self)(result_list, self.shape)
else:
result_list = [other / i for i in Flatten(self)]
return type(self)(result_list, self.shape)
def test_flatten():
from sympy import Matrix
for ArrayType in [
ImmutableDenseNDimArray, ImmutableSparseNDimArray, Matrix
]:
A = ArrayType(range(24)).reshape(4, 6)
assert [i for i in Flatten(A)] == [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23
]
for i, v in enumerate(Flatten(A)):
i == v
def __rsub__(self, other):
if not isinstance(other, NewArray):
result_list = [other - i for i in Flatten(self)]
return type(self)(result_list, self.shape)
else:
raise NotImplementedError("array shape mismatch")
def my_cos(x):
if isinstance(x, (np.ndarray, NewArray)):
result_list = [sympy.cos(i) for i in Flatten(x)]
return NewArray(result_list, x.shape)
else:
return sympy.cos(x)
def __neg__(self):
from sympy.tensor.array import SparseNDimArray
from sympy.tensor.array.arrayop import Flatten
if isinstance(self, SparseNDimArray):
return type(self)({k: -v for (k, v) in self._sparse_array.items()}, self.shape)
result_list = [-i for i in Flatten(self)]
return type(self)(result_list, self.shape)
def __div__(self, other):
from sympy.matrices.matrices import MatrixBase
from sympy.tensor.array import SparseNDimArray
from sympy.tensor.array.arrayop import Flatten
if isinstance(other, (Iterable, NDimArray, MatrixBase)):
raise ValueError("scalar expected")
other = sympify(other)
if isinstance(self, SparseNDimArray) and other != S.Zero:
return type(self)({k: v/other for (k, v) in self._sparse_array.items()}, self.shape)
result_list = [i/other for i in Flatten(self)]
return type(self)(result_list, self.shape)
def my_der(self, other):
if isinstance(self, (np.ndarray, NewArray)):
if isinstance(other, (np.ndarray, NewArray)):
if self.shape != other.shape:
raise ValueError("array shape mismatch")
else:
result_list = [
sympy.diff(i, j, evaluate=False)
for i, j in zip(Flatten(self), Flatten(other))
]
return type(self)(result_list, self.shape)
else:
result_list = [
sympy.diff(i, other, evaluate=False) for i in Flatten(self)
]
return type(self)(result_list, self.shape)
else:
if isinstance(other, (np.ndarray, NewArray)):
result_list = [
sympy.diff(self, i, evaluate=False) for i in Flatten(other)
]
return type(self)(result_list, other.shape)
else:
return sympy.diff(self, other, evaluate=False)
def __rmul__(self, other):
from sympy.matrices.matrices import MatrixBase
from sympy.tensor.array import SparseNDimArray
from sympy.tensor.array.arrayop import Flatten
if isinstance(other, (Iterable, NDimArray, MatrixBase)):
raise ValueError("scalar expected, use tensorproduct(...) for tensorial product")
other = sympify(other)
if isinstance(self, SparseNDimArray):
if other.is_zero:
return type(self)({}, self.shape)
return type(self)({k: other*v for (k, v) in self._sparse_array.items()}, self.shape)
result_list = [other*i for i in Flatten(self)]
return type(self)(result_list, self.shape)
def applyfunc(self, f):
"""Apply a function to each element of the N-dim array.
Examples
========
>>> from sympy import ImmutableDenseNDimArray
>>> m = ImmutableDenseNDimArray([i*2+j for i in range(2) for j in range(2)], (2, 2))
>>> m
[[0, 1], [2, 3]]
>>> m.applyfunc(lambda i: 2*i)
[[0, 2], [4, 6]]
"""
from sympy.tensor.array import SparseNDimArray
from sympy.tensor.array.arrayop import Flatten
if isinstance(self, SparseNDimArray) and f(S.Zero) == 0:
return type(self)({k: f(v) for k, v in self._sparse_array.items() if f(v) != 0}, self.shape)
return type(self)(map(f, Flatten(self)), self.shape)
def _eval_conjugate(self):
from sympy.tensor.array.arrayop import Flatten
return self.func([i.conjugate() for i in Flatten(self)], self.shape)
def __abs__(self):
result_list = [sympy.Abs(i) for i in Flatten(self)]
return type(self)(result_list, self.shape)
def _eval_power(self, other):
if isinstance(other, (numbers.Real, sympy.Rational, sympy.Float)):
result_list = [i**other for i in Flatten(self)]
return type(self)(result_list, self.shape)
else:
raise ValueError("array shape mismatch")