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"""Compressed Sparse Row matrix format"""
__docformat__ = "restructuredtext en"
__all__ = ['csr_matrix', 'isspmatrix_csr']
from warnings import warn
import numpy as np
from sparsetools import csr_tocsc, csr_tobsr, csr_count_blocks, \
get_csr_submatrix, csr_sample_values
from sputils import upcast, isintlike
from compressed import _cs_matrix
class csr_matrix(_cs_matrix):
Compressed Sparse Row matrix
This can be instantiated in several ways:
with a dense matrix or rank-2 ndarray D
with another sparse matrix S (equivalent to S.tocsr())
csr_matrix((M, N), [dtype])
to construct an empty matrix with shape (M, N)
dtype is optional, defaulting to dtype='d'.
csr_matrix((data, ij), [shape=(M, N)])
where ``data`` and ``ij`` satisfy the relationship
``a[ij[0, k], ij[1, k]] = data[k]``
csr_matrix((data, indices, indptr), [shape=(M, N)])
is the standard CSR representation where the column indices for
row 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.
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
CSR format data array of the matrix
CSR format index array of the matrix
CSR format index pointer array of the matrix
Whether indices are sorted
Sparse matrices can be used in arithmetic operations: they support
addition, subtraction, multiplication, division, and matrix power.
Advantages of the CSR format
- efficient arithmetic operations CSR + CSR, CSR * CSR, etc.
- efficient row slicing
- fast matrix vector products
Disadvantages of the CSR format
- slow column slicing operations (consider CSC)
- changes to the sparsity structure are expensive (consider LIL or DOK)
>>> from scipy.sparse import *
>>> from scipy import *
>>> csr_matrix( (3,4), dtype=int8 ).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>> 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 transpose(self, copy=False):
from csc import csc_matrix
M,N = self.shape
return csc_matrix((,self.indices,self.indptr), shape=(N,M), copy=copy)
def tolil(self):
from lil import lil_matrix
lil = lil_matrix(self.shape,dtype=self.dtype)
self.sort_indices() #lil_matrix needs sorted column indices
ptr,ind,dat = self.indptr,self.indices,
rows, data = lil.rows,
for n in xrange(self.shape[0]):
start = ptr[n]
end = ptr[n+1]
rows[n] = ind[start:end].tolist()
data[n] = dat[start:end].tolist()
return lil
def tocsr(self, copy=False):
if copy:
return self.copy()
return self
def tocsc(self):
indptr = np.empty(self.shape[1] + 1, dtype=np.intc)
indices = np.empty(self.nnz, dtype=np.intc)
data = np.empty(self.nnz, dtype=upcast(self.dtype))
csr_tocsc(self.shape[0], self.shape[1], \
self.indptr, self.indices,, \
indptr, indices, data)
from csc import csc_matrix
A = csc_matrix((data, indices, indptr), shape=self.shape)
A.has_sorted_indices = True
return A
def tobsr(self, blocksize=None, copy=True):
from bsr import bsr_matrix
if blocksize is None:
from spfuncs import estimate_blocksize
return self.tobsr(blocksize=estimate_blocksize(self))
elif blocksize == (1,1):
arg1 = (,1,1),self.indices,self.indptr)
return bsr_matrix(arg1, shape=self.shape, copy=copy )
R,C = blocksize
M,N = self.shape
if R < 1 or C < 1 or M % R != 0 or N % C != 0:
raise ValueError('invalid blocksize %s' % blocksize)
blks = csr_count_blocks(M,N,R,C,self.indptr,self.indices)
indptr = np.empty(M//R + 1, dtype=np.intc)
indices = np.empty(blks, dtype=np.intc)
data = np.zeros((blks,R,C), dtype=self.dtype)
csr_tobsr(M, N, R, C, self.indptr, self.indices,, \
indptr, indices, data.ravel() )
return bsr_matrix((data,indices,indptr), shape=self.shape)
# 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[0],x[1])
def __getitem__(self, key):
def asindices(x):
x = np.asarray(x, dtype=np.intc)
raise IndexError('invalid index')
return x
def check_bounds(indices,N):
max_indx = indices.max()
if max_indx >= N:
raise IndexError('index (%d) out of range' % max_indx)
min_indx = indices.min()
if min_indx < -N:
raise IndexError('index (%d) out of range' % (N + min_indx))
return (min_indx,max_indx)
def extractor(indices,N):
"""Return a sparse matrix P so that P*self implements
slicing of the form self[[1,2,3],:]
indices = asindices(indices)
(min_indx,max_indx) = check_bounds(indices,N)
if min_indx < 0:
indices = indices.copy()
indices[indices < 0] += N
indptr = np.arange(len(indices) + 1, dtype=np.intc)
data = np.ones(len(indices), dtype=self.dtype)
shape = (len(indices),N)
return csr_matrix((data,indices,indptr), shape=shape)
if isinstance(key, tuple):
row = key[0]
col = key[1]
if isintlike(row):
if isintlike(col):
return self._get_single_element(row, col) #[i,j]
elif isinstance(col, slice):
return self._get_row_slice(row, col) #[i,1:2]
P = extractor(col,self.shape[1]).T #[i,[1,2]]
return self[row,:]*P
elif isinstance(row, slice):
if isintlike(col) or isinstance(col, slice):
return self._get_submatrix(row, col) #[1:2,j]
P = extractor(col,self.shape[1]).T #[1:2,[1,2]]
return self[row,:]*P
#[[1,2],??] or [[[1],[2]],??]
if isintlike(col) or isinstance(col,slice):
P = extractor(row, self.shape[0]) #[[1,2],j] or [[1,2],1:2]
return (P*self)[:,col]
row = asindices(row)
col = asindices(col)
if len(row.shape) == 1:
if len(row) != len(col): #[[1,2],[1,2]]
raise IndexError('number of row and column indices differ')
check_bounds(row, self.shape[0])
check_bounds(col, self.shape[1])
num_samples = len(row)
val = np.empty(num_samples, dtype=self.dtype)
csr_sample_values(self.shape[0], self.shape[1],
self.indptr, self.indices,,
num_samples, row, col, val)
#val = []
#for i,j in zip(row,col):
# val.append(self._get_single_element(i,j))
return np.asmatrix(val)
elif len(row.shape) == 2:
row = np.ravel(row) #[[[1],[2]],[1,2]]
P = extractor(row, self.shape[0])
return (P*self)[:,col]
raise NotImplementedError('unsupported indexing')
elif isintlike(key) or isinstance(key,slice):
return self[key,:] #[i] or [1:2]
return self[asindices(key),:] #[[1,2]]
def _get_single_element(self,row,col):
"""Returns the single element self[row, col]
M, N = self.shape
if (row < 0):
row += M
if (col < 0):
col += N
if not (0<=row<M) or not (0<=col<N):
raise IndexError("index out of bounds")
#TODO make use of sorted indices (if present)
start = self.indptr[row]
end = self.indptr[row+1]
indxs = np.where(col == self.indices[start:end])[0]
num_matches = len(indxs)
if num_matches == 0:
# entry does not appear in the matrix
return self.dtype.type(0)
elif num_matches == 1:
raise ValueError('nonzero entry (%d,%d) occurs more than once' % (row,col) )
def _get_row_slice(self, i, cslice):
"""Returns a copy of row self[i, cslice]
if i < 0:
i += self.shape[0]
if i < 0 or i >= self.shape[0]:
raise IndexError('index (%d) out of range' % i )
start, stop, stride = cslice.indices(self.shape[1])
if stride != 1:
raise ValueError("slicing with step != 1 not supported")
if stop <= start:
raise ValueError("slice width must be >= 1")
#TODO make [i,:] faster
#TODO implement [i,x:y:z]
indices = []
for ind in xrange(self.indptr[i], self.indptr[i+1]):
if self.indices[ind] >= start and self.indices[ind] < stop:
index = self.indices[indices] - start
data =[indices]
indptr = np.array([0, len(indices)])
return csr_matrix( (data, index, indptr), shape=(1, stop-start) )
def _get_submatrix( self, row_slice, col_slice ):
"""Return a submatrix of this matrix (new matrix is created)."""
M,N = self.shape
def process_slice( sl, num ):
if isinstance( sl, slice ):
if sl.step not in (1, None):
raise ValueError('slicing with step != 1 not supported')
i0, i1 = sl.start, sl.stop
if i0 is None:
i0 = 0
elif i0 < 0:
i0 = num + i0
if i1 is None:
i1 = num
elif i1 < 0:
i1 = num + i1
return i0, i1
elif isintlike( sl ):
if sl < 0:
sl += num
return sl, sl + 1
raise TypeError('expected slice or scalar')
def check_bounds( i0, i1, num ):
if not (0<=i0<num) or not (0<i1<=num) or not (i0<i1):
raise IndexError( \
"index out of bounds: 0<=%d<%d, 0<=%d<%d, %d<%d" %\
(i0, num, i1, num, i0, i1) )
i0, i1 = process_slice( row_slice, M )
j0, j1 = process_slice( col_slice, N )
check_bounds( i0, i1, M )
check_bounds( j0, j1, N )
indptr, indices, data = get_csr_submatrix( M, N, \
self.indptr, self.indices,, \
int(i0), int(i1), int(j0), int(j1) )
shape = (i1 - i0, j1 - j0)
return self.__class__( (data,indices,indptr), shape=shape )
def isspmatrix_csr(x):
return isinstance(x, csr_matrix)
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