from __future__ import absolute_import, division, print_function
from abc import ABCMeta, abstractmethod
from collections import Iterable
from numbers import Integral
from functools import reduce
import operator
from .utils import _zero_of_dtype
[docs]class SparseArray(object):
"""
An abstract base class for all the sparse array classes.
Attributes
----------
dtype : numpy.dtype
The data type of this array.
fill_value : scalar
The fill value of this array.
"""
__metaclass__ = ABCMeta
def __init__(self, shape, fill_value=None):
if not isinstance(shape, Iterable):
shape = (shape,)
if not all(isinstance(l, Integral) and int(l) >= 0 for l in shape):
raise ValueError('shape must be an non-negative integer or a tuple '
'of non-negative integers.')
self.shape = tuple(int(l) for l in shape)
if fill_value is not None:
if not hasattr(fill_value, 'dtype') or fill_value.dtype != self.dtype:
self.fill_value = self.dtype.type(fill_value)
else:
self.fill_value = fill_value
else:
self.fill_value = _zero_of_dtype(self.dtype)
dtype = None
@property
@abstractmethod
def nnz(self):
"""
The number of nonzero elements in this array. Note that any duplicates in
:code:`coords` are counted multiple times. To avoid this, call :obj:`COO.sum_duplicates`.
Returns
-------
int
The number of nonzero elements in this array.
See Also
--------
DOK.nnz : Equivalent :obj:`DOK` array property.
numpy.count_nonzero : A similar Numpy function.
scipy.sparse.coo_matrix.nnz : The Scipy equivalent property.
Examples
--------
>>> import numpy as np
>>> from sparse import COO
>>> x = np.array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3, 0, 0])
>>> np.count_nonzero(x)
6
>>> s = COO.from_numpy(x)
>>> s.nnz
6
>>> np.count_nonzero(x) == s.nnz
True
"""
@property
def ndim(self):
"""
The number of dimensions of this array.
Returns
-------
int
The number of dimensions of this array.
See Also
--------
DOK.ndim : Equivalent property for :obj:`DOK` arrays.
numpy.ndarray.ndim : Numpy equivalent property.
Examples
--------
>>> from sparse import COO
>>> import numpy as np
>>> x = np.random.rand(1, 2, 3, 1, 2)
>>> s = COO.from_numpy(x)
>>> s.ndim
5
>>> s.ndim == x.ndim
True
"""
return len(self.shape)
@property
def size(self):
"""
The number of all elements (including zeros) in this array.
Returns
-------
int
The number of elements.
See Also
--------
numpy.ndarray.size : Numpy equivalent property.
Examples
--------
>>> from sparse import COO
>>> import numpy as np
>>> x = np.zeros((10, 10))
>>> s = COO.from_numpy(x)
>>> s.size
100
"""
# We use this instead of np.prod because np.prod
# returns a float64 for an empty shape.
return reduce(operator.mul, self.shape, 1)
@property
def density(self):
"""
The ratio of nonzero to all elements in this array.
Returns
-------
float
The ratio of nonzero to all elements.
See Also
--------
COO.size : Number of elements.
COO.nnz : Number of nonzero elements.
Examples
--------
>>> import numpy as np
>>> from sparse import COO
>>> x = np.zeros((8, 8))
>>> x[0, :] = 1
>>> s = COO.from_numpy(x)
>>> s.density
0.125
"""
return self.nnz / self.size