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"""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:
dia_matrix(D)
with a dense matrix
dia_matrix(S)
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)
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
data
DIA format data array of the matrix
offsets
DIA format offset array of the matrix
Notes
-----
Sparse matrices can be used in arithmetic operations: they support
addition, subtraction, multiplication, division, and matrix power.
Examples
--------
>>> 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):
_data_matrix.__init__(self)
if isspmatrix_dia(arg1):
if copy:
arg1 = arg1.copy()
self.data = arg1.data
self.offsets = arg1.offsets
self.shape = arg1.shape
elif isspmatrix(arg1):
if isspmatrix_dia(arg1) and copy:
A = arg1.copy()
else:
A = arg1.todia()
self.data = A.data
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
self.data = np.zeros( (0,0), getdtype(dtype, default=float))
self.offsets = np.zeros( (0), dtype=np.intc)
else:
try:
# Try interpreting it as (data, offsets)
data, offsets = arg1
except:
raise ValueError('unrecognized form for dia_matrix constructor')
else:
if shape is None:
raise ValueError('expected a shape argument')
self.data = 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
else:
#must be dense, convert to COO first, then to DIA
try:
arg1 = np.asarray(arg1)
except:
raise ValueError("unrecognized form for" \
" %s_matrix constructor" % self.format)
from .coo import coo_matrix
A = coo_matrix(arg1, dtype=dtype).todia()
self.data = A.data
self.offsets = A.offsets
self.shape = A.shape
if dtype is not None:
self.data = self.data.astype(dtype)
#check format
if self.offsets.ndim != 1:
raise ValueError('offsets array must have rank 1')
if self.data.ndim != 2:
raise ValueError('data array must have rank 2')
if self.data.shape[0] != len(self.offsets):
raise ValueError('number of diagonals (%d) ' \
'does not match the number of offsets (%d)' \
% (self.data.shape[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, self.data.shape[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)
else:
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,
x.dtype.char))
L = self.data.shape[1]
M,N = self.shape
dia_matvec(M,N, len(self.offsets), L, self.offsets, self.data, 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()
else:
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(self.data)
len_data = self.data.shape[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(),self.data.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)
else:
return dia_matrix( (data,self.offsets), shape=self.shape)
def isspmatrix_dia(x):
return isinstance(x, dia_matrix)
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