[Scipy-svn] r3676 - trunk/scipy/sparse
scipy-svn at scipy.org
scipy-svn at scipy.org
Sun Dec 16 17:09:30 EST 2007
Author: wnbell
Date: 2007-12-16 16:08:50 -0600 (Sun, 16 Dec 2007)
New Revision: 3676
Modified:
trunk/scipy/sparse/coo.py
trunk/scipy/sparse/csc.py
trunk/scipy/sparse/csr.py
Log:
updated some sparse docstrings
Modified: trunk/scipy/sparse/coo.py
===================================================================
--- trunk/scipy/sparse/coo.py 2007-12-16 04:35:42 UTC (rev 3675)
+++ trunk/scipy/sparse/coo.py 2007-12-16 22:08:50 UTC (rev 3676)
@@ -12,34 +12,68 @@
from sputils import upcast, to_native, isshape, getdtype
class coo_matrix(spmatrix):
- """ A sparse matrix in coordinate list format.
+ """A sparse matrix in COOrdinate format.
+ Also known as the 'ijv' or 'triplet' format.
- COO matrices are created either as:
- A = coo_matrix( (m, n), [dtype])
- for a zero matrix, or as:
- A = coo_matrix(S)
- where S is a sparse matrix, or as:
- A = coo_matrix(M)
- where M is a dense matrix or rank 2 ndarray, or as:
- A = coo_matrix((obj, ij), [shape])
- where the dimensions are optional. If supplied, we set (M, N) = shape.
- If not supplied, we infer these from the index arrays:
- ij[0][:] and ij[1][:]
+ This can be instantiated in several ways:
+ - coo_matrix(D)
+ with a dense matrix D
- The arguments 'obj' and 'ij' represent three arrays:
- 1. obj[:] the entries of the matrix, in any order
- 2. ij[0][:] the row indices of the matrix entries
- 3. ij[1][:] the column indices of the matrix entries
+ - coo_matrix(S)
+ with another sparse matrix S (equivalent to S.tocoo())
- So the following holds:
- A[ij[0][k], ij[1][k] = obj[k]
+ - coo_matrix((M, N), [dtype])
+ to construct an empty matrix with shape (M, N)
+ dtype is optional, defaulting to dtype='d'.
+ - coo_matrix((data, ij), [shape=(M, N)])
+ When shape is not specified, it is inferred from the index arrays:
+ ij[0][:] and ij[1][:]
+
+ The arguments 'data' and 'ij' represent three arrays:
+ 1. data[:] the entries of the matrix, in any order
+ 2. ij[0][:] the row indices of the matrix entries
+ 3. ij[1][:] the column indices of the matrix entries
+
+ So the following holds:
+ A[ij[0][k], ij[1][k] = data[k]
+
Note:
When converting to CSR or CSC format, duplicate (i,j) entries
will be summed together. This facilitates efficient construction
of finite element matrices and the like.
+ *Examples*
+ ----------
+
+ >>> from scipy.sparse import *
+ >>> from scipy import *
+ >>> coo_matrix( (3,4), dtype='i' ).todense()
+ matrix([[0, 0, 0, 0],
+ [0, 0, 0, 0],
+ [0, 0, 0, 0]])
+
+ >>> row = array([0,3,1,0])
+ >>> col = array([0,3,1,2])
+ >>> data = array([4,5,7,9])
+ >>> coo_matrix( (data,(row,col)), shape=(4,4) ).todense()
+ matrix([[4, 0, 9, 0],
+ [0, 7, 0, 0],
+ [0, 0, 0, 0],
+ [0, 0, 0, 5]])
+
+ >>> print "example with duplicates"
+ >>> row = array([0,0,1,3,1,0,0])
+ >>> col = array([0,2,1,3,1,0,0])
+ >>> data = array([1,1,1,1,1,1,1])
+ >>> coo_matrix( (data,(row,col)), shape=(4,4)).todense()
+ matrix([[3, 0, 1, 0],
+ [0, 2, 0, 0],
+ [0, 0, 0, 0],
+ [0, 0, 0, 1]])
+
"""
+
def __init__(self, arg1, shape=None, dtype=None, copy=False, dims=None):
spmatrix.__init__(self)
Modified: trunk/scipy/sparse/csc.py
===================================================================
--- trunk/scipy/sparse/csc.py 2007-12-16 04:35:42 UTC (rev 3675)
+++ trunk/scipy/sparse/csc.py 2007-12-16 22:08:50 UTC (rev 3676)
@@ -18,26 +18,61 @@
class csc_matrix(_cs_matrix):
- """ Compressed sparse column matrix
- This can be instantiated in several ways:
- - csc_matrix(d)
- with a dense matrix d
+ """Compressed Sparse Column matrix
- - csc_matrix(s)
- with another sparse matrix s (sugar for .tocsc())
+ This can be instantiated in several ways:
+ - csc_matrix(D)
+ with a dense matrix or rank-2 ndarray D
- - csc_matrix((M, N), [dtype])
- to construct a container, where (M, N) are dimensions and
- dtype is optional, defaulting to dtype='d'.
+ - csc_matrix(S)
+ with another sparse matrix S (equivalent to S.tocsc())
- - csc_matrix((data, ij), [(M, N)])
- where data, ij satisfy:
- a[ij[0, k], ij[1, k]] = data[k]
+ - 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, ptr), [(M, N)])
- standard CSC representation
+ - csc_matrix((data, ij), [shape=(M, N)])
+ where data, ij satisfy:
+ a[ij[0, k], ij[1, k]] = data[k]
+
+ - csc_matrix((data, indices, indptr), [shape=(M, N)])
+ is the native 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.
+
+
+ *Examples*
+ ----------
+
+ >>> from scipy.sparse import *
+ >>> from scipy import *
+ >>> csc_matrix( (3,4), dtype='i' ).todense()
+ matrix([[0, 0, 0, 0],
+ [0, 0, 0, 0],
+ [0, 0, 0, 0]])
+
+ >>> row = array([0,0,1,2,2,2])
+ >>> col = array([0,2,2,0,1,2])
+ >>> data = array([1,2,3,4,5,6])
+ >>> csc_matrix( (data,(row,col)), shape=(3,3) ).todense()
+ matrix([[1, 0, 2],
+ [0, 0, 3],
+ [4, 5, 6]])
+
+ >>> indptr = array([0,2,3,6])
+ >>> indices = array([0,2,2,0,1,2])
+ >>> data = array([1,4,6,2,3,5])
+ >>> csc_matrix( (data,indices,indptr), shape=(3,3) ).todense()
+ matrix([[1, 0, 2],
+ [0, 0, 3],
+ [4, 5, 6]])
+
"""
-
+
def __getattr__(self, attr):
if attr == 'rowind':
warn("rowind attribute no longer in use. Use .indices instead",
Modified: trunk/scipy/sparse/csr.py
===================================================================
--- trunk/scipy/sparse/csr.py 2007-12-16 04:35:42 UTC (rev 3675)
+++ trunk/scipy/sparse/csr.py 2007-12-16 22:08:50 UTC (rev 3676)
@@ -18,26 +18,65 @@
from compressed import _cs_matrix,resize1d
class csr_matrix(_cs_matrix):
- """ Compressed sparse row matrix
- This can be instantiated in several ways:
- - csr_matrix(d)
- with a dense matrix d
+ """Compressed Sparse Row matrix
- - csr_matrix(s)
- with another sparse matrix s (sugar for .tocsr())
+ This can be instantiated in several ways:
+ - csr_matrix(D)
+ with a dense matrix or rank-2 ndarray D
- - csr_matrix((M, N), [dtype])
- to construct a container, where (M, N) are dimensions and
- dtype is optional, defaulting to dtype='d'.
+ - csr_matrix(S)
+ with another sparse matrix S (equivalent to S.tocsr())
- - csr_matrix((data, ij), [shape=(M, N)])
- where data, ij satisfy:
- a[ij[0, k], ij[1, k]] = data[k]
+ - csr_matrix((M, N), [dtype])
+ to construct an empty matrix with shape (M, N)
+ dtype is optional, defaulting to dtype='d'.
- - csr_matrix((data, col, ptr), [shape=(M, N)])
- standard CSR representation
+ - csr_matrix((data, ij), [shape=(M, N)])
+ where data, ij satisfy:
+ a[ij[0, k], ij[1, k]] = data[k]
+
+ - csr_matrix((data, indices, indptr), [shape=(M, N)])
+ is the native CSR representation where:
+ the column indices for row 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.
+
+
+ *Examples*
+ ----------
+
+ >>> from scipy.sparse import *
+ >>> from scipy import *
+ >>> csr_matrix( (3,4), dtype='i' ).todense()
+ matrix([[0, 0, 0, 0],
+ [0, 0, 0, 0],
+ [0, 0, 0, 0]])
+
+ >>> row = array([0,0,1,2,2,2])
+ >>> col = array([0,2,2,0,1,2])
+ >>> data = array([1,2,3,4,5,6])
+ >>> csr_matrix( (data,(row,col)), shape=(3,3) ).todense()
+ matrix([[1, 0, 2],
+ [0, 0, 3],
+ [4, 5, 6]])
+
+ >>> indptr = array([0,2,3,6])
+ >>> indices = array([0,2,2,0,1,2])
+ >>> data = array([1,2,3,4,5,6])
+ >>> csr_matrix( (data,indices,indptr), shape=(3,3) ).todense()
+ matrix([[1, 0, 2],
+ [0, 0, 3],
+ [4, 5, 6]])
+
"""
+
+
+
+
def __getattr__(self, attr):
if attr == 'colind':
warn("colind attribute no longer in use. Use .indices instead",
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