from __future__ import print_function, division
import functools
import itertools
from sympy.core.sympify import _sympify
from sympy import Matrix, flatten, Basic, Tuple
from sympy.tensor.array.mutable_ndim_array import MutableNDimArray
from sympy.tensor.array.ndim_array import NDimArray, ImmutableNDimArray
class DenseNDimArray(NDimArray):
def __new__(self, *args, **kwargs):
return ImmutableDenseNDimArray(*args, **kwargs)
def __getitem__(self, index):
"""
Allows to get items from N-dim array.
Examples
========
>>> from sympy.tensor.array import MutableDenseNDimArray
>>> a = MutableDenseNDimArray([0, 1, 2, 3], (2, 2))
>>> a
[[0, 1], [2, 3]]
>>> a[0, 0]
0
>>> a[1, 1]
3
"""
if isinstance(index, tuple) and any([isinstance(i, slice) for i in index]):
def slice_expand(s, dim):
if not isinstance(s, slice):
return (s,)
start, stop, step = s.indices(dim)
return [start + i*step for i in range((stop-start)//step)]
sl_factors = [slice_expand(i, dim) for (i, dim) in zip(index, self.shape)]
eindices = itertools.product(*sl_factors)
array = [self._array[self._parse_index(i)] for i in eindices]
nshape = [len(el) for i, el in enumerate(sl_factors) if isinstance(index[i], slice)]
return type(self)(array, nshape)
else:
if isinstance(index, slice):
return self._array[index]
else:
index = self._parse_index(index)
return self._array[index]
@classmethod
def zeros(cls, *shape):
list_length = functools.reduce(lambda x, y: x*y, shape)
return cls._new(([0]*list_length,), shape)
def tomatrix(self):
"""
Converts MutableDenseNDimArray to Matrix. Can convert only 2-dim array, else will raise error.
Examples
========
>>> from sympy.tensor.array import MutableDenseNDimArray
>>> a = MutableDenseNDimArray([1 for i in range(9)], (3, 3))
>>> b = a.tomatrix()
>>> b
Matrix([
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
"""
if self.rank() != 2:
raise ValueError('Dimensions must be of size of 2')
return Matrix(self.shape[0], self.shape[1], self._array)
def __iter__(self):
return self._array.__iter__()
def reshape(self, *newshape):
"""
Returns MutableDenseNDimArray instance with new shape. Elements number
must be suitable to new shape. The only argument of method sets
new shape.
Examples
========
>>> from sympy.tensor.array import MutableDenseNDimArray
>>> a = MutableDenseNDimArray([1, 2, 3, 4, 5, 6], (2, 3))
>>> a.shape
(2, 3)
>>> a
[[1, 2, 3], [4, 5, 6]]
>>> b = a.reshape(3, 2)
>>> b.shape
(3, 2)
>>> b
[[1, 2], [3, 4], [5, 6]]
"""
new_total_size = functools.reduce(lambda x,y: x*y, newshape)
if new_total_size != self._loop_size:
raise ValueError("Invalid reshape parameters " + newshape)
# there is no `.func` as this class does not subtype `Basic`:
return type(self)(self._array, newshape)
[docs]class ImmutableDenseNDimArray(DenseNDimArray, ImmutableNDimArray):
"""
"""
def __new__(cls, *args, **kwargs):
return cls._new(*args, **kwargs)
@classmethod
def _new(cls, *args, **kwargs):
shape, flat_list = cls._handle_ndarray_creation_inputs(*args, **kwargs)
shape = Tuple(*map(_sympify, shape))
flat_list = flatten(flat_list)
flat_list = Tuple(*flat_list)
self = Basic.__new__(cls, flat_list, shape, **kwargs)
self._shape = shape
self._array = list(flat_list)
self._rank = len(shape)
self._loop_size = functools.reduce(lambda x,y: x*y, shape) if shape else 0
return self
def __setitem__(self, index, value):
raise TypeError('immutable N-dim array')
[docs]class MutableDenseNDimArray(DenseNDimArray, MutableNDimArray):
def __new__(cls, *args, **kwargs):
return cls._new(*args, **kwargs)
@classmethod
def _new(cls, *args, **kwargs):
shape, flat_list = cls._handle_ndarray_creation_inputs(*args, **kwargs)
flat_list = flatten(flat_list)
self = object.__new__(cls)
self._shape = shape
self._array = list(flat_list)
self._rank = len(shape)
self._loop_size = functools.reduce(lambda x,y: x*y, shape) if shape else 0
return self
def __setitem__(self, index, value):
"""Allows to set items to MutableDenseNDimArray.
Examples
========
>>> from sympy.tensor.array import MutableDenseNDimArray
>>> a = MutableDenseNDimArray.zeros(2, 2)
>>> a[0,0] = 1
>>> a[1,1] = 1
>>> a
[[1, 0], [0, 1]]
"""
index = self._parse_index(index)
self._setter_iterable_check(value)
value = _sympify(value)
self._array[index] = value
