Skip to content


Subversion checkout URL

You can clone with HTTPS or Subversion.

Download ZIP
Fetching contributors…

Cannot retrieve contributors at this time

232 lines (183 sloc) 7.233 kb
"""Sparse DIAgonal format"""
from __future__ import division, print_function, absolute_import
__docformat__ = "restructuredtext en"
__all__ = ['dia_matrix', 'isspmatrix_dia']
import numpy as np
from .base import isspmatrix, _formats
from .data import _data_matrix
from .sputils import isshape, upcast, upcast_char, getdtype
from .sparsetools import dia_matvec
class dia_matrix(_data_matrix):
"""Sparse matrix with DIAgonal storage
This can be instantiated in several ways:
with a dense matrix
with another sparse matrix S (equivalent to S.todia())
dia_matrix((M, N), [dtype])
to construct an empty matrix with shape (M, N),
dtype is optional, defaulting to dtype='d'.
dia_matrix((data, offsets), shape=(M, N))
where the ``data[k,:]`` stores the diagonal entries for
diagonal ``offsets[k]`` (See example below)
dtype : dtype
Data type of the matrix
shape : 2-tuple
Shape of the matrix
ndim : int
Number of dimensions (this is always 2)
Number of nonzero elements
DIA format data array of the matrix
DIA format offset array of the matrix
Sparse matrices can be used in arithmetic operations: they support
addition, subtraction, multiplication, division, and matrix power.
>>> from scipy.sparse import *
>>> from scipy import *
>>> dia_matrix( (3,4), dtype=int8).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>> data = array([[1,2,3,4]]).repeat(3,axis=0)
>>> offsets = array([0,-1,2])
>>> dia_matrix( (data,offsets), shape=(4,4)).todense()
matrix([[1, 0, 3, 0],
[1, 2, 0, 4],
[0, 2, 3, 0],
[0, 0, 3, 4]])
def __init__(self, arg1, shape=None, dtype=None, copy=False):
if isspmatrix_dia(arg1):
if copy:
arg1 = arg1.copy() =
self.offsets = arg1.offsets
self.shape = arg1.shape
elif isspmatrix(arg1):
if isspmatrix_dia(arg1) and copy:
A = arg1.copy()
A = arg1.todia() =
self.offsets = A.offsets
self.shape = A.shape
elif isinstance(arg1, tuple):
if isshape(arg1):
# It's a tuple of matrix dimensions (M, N)
# create empty matrix
self.shape = arg1 #spmatrix checks for errors here = np.zeros( (0,0), getdtype(dtype, default=float))
self.offsets = np.zeros( (0), dtype=np.intc)
# Try interpreting it as (data, offsets)
data, offsets = arg1
raise ValueError('unrecognized form for dia_matrix constructor')
if shape is None:
raise ValueError('expected a shape argument') = np.atleast_2d(np.array(arg1[0], dtype=dtype, copy=copy))
self.offsets = np.atleast_1d(np.array(arg1[1], dtype=np.intc, copy=copy))
self.shape = shape
#must be dense, convert to COO first, then to DIA
arg1 = np.asarray(arg1)
raise ValueError("unrecognized form for" \
" %s_matrix constructor" % self.format)
from .coo import coo_matrix
A = coo_matrix(arg1, dtype=dtype).todia() =
self.offsets = A.offsets
self.shape = A.shape
if dtype is not None: =
#check format
if self.offsets.ndim != 1:
raise ValueError('offsets array must have rank 1')
if != 2:
raise ValueError('data array must have rank 2')
if[0] != len(self.offsets):
raise ValueError('number of diagonals (%d) ' \
'does not match the number of offsets (%d)' \
% ([0], len(self.offsets)))
if len(np.unique(self.offsets)) != len(self.offsets):
raise ValueError('offset array contains duplicate values')
def __repr__(self):
nnz = self.getnnz()
format = self.getformat()
return "<%dx%d sparse matrix of type '%s'\n" \
"\twith %d stored elements (%d diagonals) in %s format>" % \
( self.shape + (self.dtype.type, nnz,[0], \
_formats[format][1],) )
def getnnz(self):
"""number of nonzero values
explicit zero values are included in this number
M,N = self.shape
nnz = 0
for k in self.offsets:
if k > 0:
nnz += min(M,N-k)
nnz += min(M+k,N)
return nnz
nnz = property(fget=getnnz)
def _mul_vector(self, other):
x = other
y = np.zeros( self.shape[0], dtype=upcast_char(self.dtype.char,
L =[1]
M,N = self.shape
dia_matvec(M,N, len(self.offsets), L, self.offsets,, x.ravel(), y.ravel())
return y
def _mul_multimatrix(self, other):
return np.hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] )
def todia(self,copy=False):
if copy:
return self.copy()
return self
def tocsr(self):
#this could be faster
return self.tocoo().tocsr()
def tocsc(self):
#this could be faster
return self.tocoo().tocsc()
def tocoo(self):
num_data = len(
len_data =[1]
row = np.arange(len_data).reshape(1,-1).repeat(num_data,axis=0)
col = row.copy()
for i,k in enumerate(self.offsets):
row[i,:] -= k
row,col,data = row.ravel(),col.ravel(),
mask = (row >= 0)
mask &= (row < self.shape[0])
mask &= (col < self.shape[1])
mask &= data != 0
row,col,data = row[mask],col[mask],data[mask]
from .coo import coo_matrix
return coo_matrix((data,(row,col)), shape=self.shape)
# needed by _data_matrix
def _with_data(self, data, copy=True):
"""Returns a matrix with the same sparsity structure as self,
but with different data. By default the structure arrays are copied.
if copy:
return dia_matrix( (data, self.offsets.copy()), shape=self.shape)
return dia_matrix( (data,self.offsets), shape=self.shape)
def isspmatrix_dia(x):
return isinstance(x, dia_matrix)
Jump to Line
Something went wrong with that request. Please try again.