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"""Compressed Sparse Column matrix format"""
__docformat__ = "restructuredtext en"
__all__ = ['csc_matrix', 'isspmatrix_csc']
from warnings import warn
import numpy as np
from sparsetools import csc_tocsr
from sputils import upcast, isintlike
from compressed import _cs_matrix
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, ij), [shape=(M, N)])
where ``data`` and ``ij`` satisfy ``a[ij[0, k], ij[1, 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]:indices[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.
Notes
-----
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
========
>>> from scipy.sparse import *
>>> from scipy import *
>>> csc_matrix( (3,4), dtype=int8 ).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>> row = array([0,2,2,0,1,2])
>>> col = array([0,0,1,2,2,2])
>>> data = array([1,2,3,4,5,6])
>>> csc_matrix( (data,(row,col)), shape=(3,3) ).todense()
matrix([[1, 0, 4],
[0, 0, 5],
[2, 3, 6]])
>>> indptr = array([0,2,3,6])
>>> indices = array([0,2,2,0,1,2])
>>> data = array([1,2,3,4,5,6])
>>> csc_matrix( (data,indices,indptr), shape=(3,3) ).todense()
matrix([[1, 0, 4],
[0, 0, 5],
[2, 3, 6]])
"""
def __getattr__(self, attr):
if attr == 'rowind':
warn("rowind attribute no longer in use. Use .indices instead",
DeprecationWarning)
return self.indices
else:
return _cs_matrix.__getattr__(self, attr)
def transpose(self, copy=False):
from csr import csr_matrix
M,N = self.shape
return csr_matrix((self.data,self.indices,self.indptr),(N,M),copy=copy)
def __iter__(self):
csr = self.tocsr()
for r in xrange(self.shape[0]):
yield csr[r,:]
@np.deprecate
def rowcol(self, ind):
#TODO remove after 0.7
row = self.indices[ind]
col = np.searchsorted(self.indptr, ind+1) - 1
return (row, col)
def tocsc(self, copy=False):
if copy:
return self.copy()
else:
return self
def tocsr(self):
M,N = self.shape
indptr = np.empty(M + 1, dtype=np.intc)
indices = np.empty(self.nnz, dtype=np.intc)
data = np.empty(self.nnz, dtype=upcast(self.dtype))
csc_tocsr(M, N, \
self.indptr, self.indices, self.data, \
indptr, indices, data)
from csr import csr_matrix
A = csr_matrix((data, indices, indptr), shape=self.shape)
A.has_sorted_indices = True
return A
def __getitem__(self, key):
# use CSR to implement fancy indexing
if isinstance(key, tuple):
row = key[0]
col = key[1]
if isintlike(row) or isinstance(row, slice):
return self.T[col,row].T
else:
#[[1,2],??] or [[[1],[2]],??]
if isintlike(col) or isinstance(col,slice):
return self.T[col,row].T
else:
row = np.asarray(row, dtype=np.intc)
col = np.asarray(col, dtype=np.intc)
if len(row.shape) == 1:
return self.T[col,row]
elif len(row.shape) == 2:
row = row.reshape(-1)
col = col.reshape(-1,1)
return self.T[col,row].T
else:
raise NotImplementedError('unsupported indexing')
return self.T[col,row].T
elif isintlike(key) or isinstance(key,slice):
return self.T[:,key].T #[i] or [1:2]
else:
return self.T[:,key].T #[[1,2]]
# 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])
from sputils import _isinstance
def isspmatrix_csc(x):
return _isinstance(x, csc_matrix)
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