def _get_submatrix(self, slice0, slice1):
"""Return a submatrix of this matrix (new matrix is created)."""
slice0, slice1 = self._swap((slice0, slice1))
shape0, shape1 = self._swap(self.shape)
def _process_slice(sl, num):
if isinstance(sl, slice):
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 np.isscalar(sl):
if sl < 0:
sl += num
return sl, sl + 1
else:
return sl[0], sl[1]
def _in_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(slice0, shape0)
j0, j1 = _process_slice(slice1, shape1)
_in_bounds(i0, i1, shape0)
_in_bounds(j0, j1, shape1)
aux = sparsetools.get_csr_submatrix(shape0, shape1, self.indptr,
self.indices, self.data, i0, i1,
j0, j1)
data, indices, indptr = aux[2], aux[1], aux[0]
shape = self._swap((i1 - i0, j1 - j0))
return self.__class__((data, indices, indptr), shape=shape)
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
else:
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, self.data, \
int(i0), int(i1), int(j0), int(j1) )
shape = (i1 - i0, j1 - j0)
return self.__class__( (data,indices,indptr), shape=shape )
def _get_submatrix( self, slice0, slice1 ):
"""Return a submatrix of this matrix (new matrix is created)."""
slice0, slice1 = self._swap((slice0,slice1))
shape0, shape1 = self._swap(self.shape)
def _process_slice( sl, num ):
if isinstance( sl, slice ):
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 np.isscalar( sl ):
if sl < 0:
sl += num
return sl, sl + 1
else:
return sl[0], sl[1]
def _in_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( slice0, shape0 )
j0, j1 = _process_slice( slice1, shape1 )
_in_bounds( i0, i1, shape0 )
_in_bounds( j0, j1, shape1 )
aux = sparsetools.get_csr_submatrix( shape0, shape1,
self.indptr, self.indices,
self.data,
i0, i1, j0, j1 )
data, indices, indptr = aux[2], aux[1], aux[0]
shape = self._swap( (i1 - i0, j1 - j0) )
return self.__class__( (data,indices,indptr), shape=shape )
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
else:
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, self.data, \
int(i0), int(i1), int(j0), int(j1) )
shape = (i1 - i0, j1 - j0)
return self.__class__((data, indices, indptr), shape=shape)
