Skip to content
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Go to file
Cannot retrieve contributors at this time
96 lines (75 sloc) 2.89 KB



This implements sparse arrays of arbitrary dimension on top of :obj:`numpy` and :obj:`scipy.sparse`. It generalizes the :obj:`scipy.sparse.coo_matrix` and :obj:`scipy.sparse.dok_matrix` layouts, but extends beyond just rows and columns to an arbitrary number of dimensions.

Additionally, this project maintains compatibility with the :obj:`numpy.ndarray` interface rather than the :obj:`numpy.matrix` interface used in :obj:`scipy.sparse`

These differences make this project useful in certain situations where scipy.sparse matrices are not well suited, but it should not be considered a full replacement. It lacks layouts that are not easily generalized like CSR/CSC and depends on scipy.sparse for some computations.


Sparse arrays, or arrays that are mostly empty or filled with zeros, are common in many scientific applications. To save space we often avoid storing these arrays in traditional dense formats, and instead choose different data structures. Our choice of data structure can significantly affect our storage and computational costs when working with these arrays.


The main data structure in this library follows the Coordinate List (COO) layout for sparse matrices, but extends it to multiple dimensions.

The COO layout, which stores the row index, column index, and value of every element:

row col data
0 0 10
0 2 13
1 3 9
3 8 21

It is straightforward to extend the COO layout to an arbitrary number of dimensions:

dim1 dim2 dim3 ... data
0 0 0 . 10
0 0 3 . 13
0 2 2 . 9
3 1 4 . 21

This makes it easy to store a multidimensional sparse array, but we still need to reimplement all of the array operations like transpose, reshape, slicing, tensordot, reductions, etc., which can be challenging in general.

Fortunately in many cases we can leverage the existing :obj:`scipy.sparse` algorithms if we can intelligently transpose and reshape our multi-dimensional array into an appropriate 2-d sparse matrix, perform a modified sparse matrix operation, and then reshape and transpose back. These reshape and transpose operations can all be done at numpy speeds by modifying the arrays of coordinates. After scipy.sparse runs its operations (often written in C) then we can convert back to using the same path of reshapings and transpositions in reverse.


This library is licensed under BSD-3

.. toctree::
   :maxdepth: 3