scipy.sparse 文档如何查找及使用

如果想真正的查看底层的代码，以及每一种存储方式的优缺点请点击：https://github.com/scipy/scipy/blob/v1.1.0/scipy/sparse/

下面以dok_matrix为例，源代码网址：https://github.com/scipy/scipy/blob/v1.1.0/scipy/sparse/dok.py

"""Dictionary Of Keys based matrix"""

from __future__ import division, print_function, absolute_import

__docformat__ = "restructuredtext en"

__all__ = ['dok_matrix', 'isspmatrix_dok']

import functools
import operator
import itertools

import numpy as np

from scipy._lib.six import zip as izip, xrange, iteritems, iterkeys, itervalues

from .base import spmatrix, isspmatrix
from .sputils import (isdense, getdtype, isshape, isintlike, isscalarlike,
                      upcast, upcast_scalar, IndexMixin, get_index_dtype,
                      check_shape)

try:
    from operator import isSequenceType as _is_sequence
except ImportError:
    def _is_sequence(x):
        return (hasattr(x, '__len__') or hasattr(x, '__next__')
                or hasattr(x, 'next'))


class dok_matrix(spmatrix, IndexMixin, dict):
    """
    Dictionary Of Keys based sparse matrix.
    This is an efficient structure for constructing sparse
    matrices incrementally.
    This can be instantiated in several ways:
        dok_matrix(D)
            with a dense matrix, D
        dok_matrix(S)
            with a sparse matrix, S
        dok_matrix((M,N), [dtype])
            create the matrix with initial shape (M,N)
            dtype is optional, defaulting to dtype='d'
    Attributes
    ----------
    dtype : dtype
        Data type of the matrix
    shape : 2-tuple
        Shape of the matrix
    ndim : int
        Number of dimensions (this is always 2)
    nnz
        Number of nonzero elements
    Notes
    -----
    Sparse matrices can be used in arithmetic operations: they support
    addition, subtraction, multiplication, division, and matrix power.
    Allows for efficient O(1) access of individual elements.
    Duplicates are not allowed.
    Can be efficiently converted to a coo_matrix once constructed.
    Examples
    --------
    >>> import numpy as np
    >>> from scipy.sparse import dok_matrix
    >>> S = dok_matrix((5, 5), dtype=np.float32)
    >>> for i in range(5):
    ...     for j in range(5):
    ...         S[i, j] = i + j    # Update element
    """
    format = 'dok'

    def __init__(self, arg1, shape=None, dtype=None, copy=False):
        dict.__init__(self)
        spmatrix.__init__(self)

        self.dtype = getdtype(dtype, default=float)
        if isinstance(arg1, tuple) and isshape(arg1):  # (M,N)
            M, N = arg1
            self._shape = check_shape((M, N))
        elif isspmatrix(arg1):  # Sparse ctor
            if isspmatrix_dok(arg1) and copy:
                arg1 = arg1.copy()
            else:
                arg1 = arg1.todok()

            if dtype is not None:
                arg1 = arg1.astype(dtype)

            dict.update(self, arg1)
            self._shape = check_shape(arg1.shape)
            self.dtype = arg1.dtype
        else:  # Dense ctor
            try:
                arg1 = np.asarray(arg1)
            except:
                raise TypeError('Invalid input format.')

            if len(arg1.shape) != 2:
                raise TypeError('Expected rank <=2 dense array or matrix.')

            from .coo import coo_matrix
            d = coo_matrix(arg1, dtype=dtype).todok()
            dict.update(self, d)
            self._shape = check_shape(arg1.shape)
            self.dtype = d.dtype

    def update(self, val):
        # Prevent direct usage of update
        raise NotImplementedError("Direct modification to dok_matrix element "