"""Compressed Sparse Column matrix format"""
__docformat__ = "restructuredtext en"
__all__ = ['csc_matrix', 'isspmatrix_csc']
import numpy as np
from ._base import spmatrix
from ._sparsetools import csc_tocsr, expandptr
from ._sputils import upcast, get_index_dtype
from ._compressed import _cs_matrix
[docs]class csc_matrix(_cs_matrix):
"""
Compressed Sparse Column matrix
This can be instantiated in several ways:
csc_matrix(D)
with a dense matrix or rank-2 ndarray D
csc_matrix(S)
with another sparse matrix S (equivalent to S.tocsc())
csc_matrix((M, N), [dtype])
to construct an empty matrix with shape (M, N)
dtype is optional, defaulting to dtype='d'.
csc_matrix((data, (row_ind, col_ind)), [shape=(M, N)])
where ``data``, ``row_ind`` and ``col_ind`` satisfy the
relationship ``a[row_ind[k], col_ind[k]] = data[k]``.
csc_matrix((data, indices, indptr), [shape=(M, N)])
is the standard CSC representation where the row indices for
column i are stored in ``indices[indptr[i]:indptr[i+1]]``
and their corresponding values are stored in
``data[indptr[i]:indptr[i+1]]``. If the shape parameter is
not supplied, the matrix dimensions are inferred from
the index arrays.
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 stored values, including explicit zeros
data
Data array of the matrix
indices
CSC format index array
indptr
CSC format index pointer array
has_sorted_indices
Whether indices are sorted
Notes
-----
Sparse matrices can be used in arithmetic operations: they support
addition, subtraction, multiplication, division, and matrix power.
Advantages of the CSC format
- efficient arithmetic operations CSC + CSC, CSC * CSC, etc.
- efficient column slicing
- fast matrix vector products (CSR, BSR may be faster)
Disadvantages of the CSC format
- slow row slicing operations (consider CSR)
- changes to the sparsity structure are expensive (consider LIL or DOK)
Examples
--------
>>> import numpy as np
>>> from scipy.sparse import csc_matrix
>>> csc_matrix((3, 4), dtype=np.int8).toarray()
array([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>> row = np.array([0, 2, 2, 0, 1, 2])
>>> col = np.array([0, 0, 1, 2, 2, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csc_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 4],
[0, 0, 5],
[2, 3, 6]])
>>> indptr = np.array([0, 2, 3, 6])
>>> indices = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csc_matrix((data, indices, indptr), shape=(3, 3)).toarray()
array([[1, 0, 4],
[0, 0, 5],
[2, 3, 6]])
"""
format = 'csc'
[docs] def transpose(self, axes=None, copy=False):
if axes is not None:
raise ValueError(("Sparse matrices do not support "
"an 'axes' parameter because swapping "
"dimensions is the only logical permutation."))
M, N = self.shape
return self._csr_container((self.data, self.indices,
self.indptr), (N, M), copy=copy)
transpose.__doc__ = spmatrix.transpose.__doc__
[docs] def __iter__(self):
yield from self.tocsr()
[docs] def tocsc(self, copy=False):
if copy:
return self.copy()
else:
return self
tocsc.__doc__ = spmatrix.tocsc.__doc__
[docs] def tocsr(self, copy=False):
M,N = self.shape
idx_dtype = get_index_dtype((self.indptr, self.indices),
maxval=max(self.nnz, N))
indptr = np.empty(M + 1, dtype=idx_dtype)
indices = np.empty(self.nnz, dtype=idx_dtype)
data = np.empty(self.nnz, dtype=upcast(self.dtype))
csc_tocsr(M, N,
self.indptr.astype(idx_dtype),
self.indices.astype(idx_dtype),
self.data,
indptr,
indices,
data)
A = self._csr_container(
(data, indices, indptr),
shape=self.shape, copy=False
)
A.has_sorted_indices = True
return A
tocsr.__doc__ = spmatrix.tocsr.__doc__
[docs] def nonzero(self):
# CSC can't use _cs_matrix's .nonzero method because it
# returns the indices sorted for self transposed.
# Get row and col indices, from _cs_matrix.tocoo
major_dim, minor_dim = self._swap(self.shape)
minor_indices = self.indices
major_indices = np.empty(len(minor_indices), dtype=self.indices.dtype)
expandptr(major_dim, self.indptr, major_indices)
row, col = self._swap((major_indices, minor_indices))
# Remove explicit zeros
nz_mask = self.data != 0
row = row[nz_mask]
col = col[nz_mask]
# Sort them to be in C-style order
ind = np.argsort(row, kind='mergesort')
row = row[ind]
col = col[ind]
return row, col
nonzero.__doc__ = _cs_matrix.nonzero.__doc__
[docs] def getrow(self, i):
"""Returns a copy of row i of the matrix, as a (1 x n)
CSR matrix (row vector).
"""
M, N = self.shape
i = int(i)
if i < 0:
i += M
if i < 0 or i >= M:
raise IndexError('index (%d) out of range' % i)
return self._get_submatrix(minor=i).tocsr()
[docs] def getcol(self, i):
"""Returns a copy of column i of the matrix, as a (m x 1)
CSC matrix (column vector).
"""
M, N = self.shape
i = int(i)
if i < 0:
i += N
if i < 0 or i >= N:
raise IndexError('index (%d) out of range' % i)
return self._get_submatrix(major=i, copy=True)
def _get_intXarray(self, row, col):
return self._major_index_fancy(col)._get_submatrix(minor=row)
def _get_intXslice(self, row, col):
if col.step in (1, None):
return self._get_submatrix(major=col, minor=row, copy=True)
return self._major_slice(col)._get_submatrix(minor=row)
def _get_sliceXint(self, row, col):
if row.step in (1, None):
return self._get_submatrix(major=col, minor=row, copy=True)
return self._get_submatrix(major=col)._minor_slice(row)
def _get_sliceXarray(self, row, col):
return self._major_index_fancy(col)._minor_slice(row)
def _get_arrayXint(self, row, col):
return self._get_submatrix(major=col)._minor_index_fancy(row)
def _get_arrayXslice(self, row, col):
return self._major_slice(col)._minor_index_fancy(row)
# these functions are used by the parent class (_cs_matrix)
# to remove redudancy between csc_matrix and csr_matrix
def _swap(self, x):
"""swap the members of x if this is a column-oriented matrix
"""
return x[1], x[0]
def isspmatrix_csc(x):
"""Is x of csc_matrix type?
Parameters
----------
x
object to check for being a csc matrix
Returns
-------
bool
True if x is a csc matrix, False otherwise
Examples
--------
>>> from scipy.sparse import csc_matrix, isspmatrix_csc
>>> isspmatrix_csc(csc_matrix([[5]]))
True
>>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc
>>> isspmatrix_csc(csr_matrix([[5]]))
False
"""
from ._arrays import csc_array
return isinstance(x, csc_matrix) or isinstance(x, csc_array)