When using scipy.sparse.spdiags or scipy.sparse.diags I have noticed want I consider to be a bug in the routines eg
scipy.sparse.spdiags([1.1,1.2,1.3],1,4,4).toarray()
returns
array([[ 0. , 1.2, 0. , 0. ],
[ 0. , 0. , 1.3, 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ]])
That is for positive diagonals it drops the first k data. One might argue that there is some grand programming reason for this and that I just need to pad with zeros. OK annoying as that may be, one can use scipy.sparse.diags which gives the correct result. However this routine has a bug that can't be worked around
scipy.sparse.diags([1.1,1.2],0,(4,2)).toarray()
gives
array([[ 1.1, 0. ],
[ 0. , 1.2],
[ 0. , 0. ],
[ 0. , 0. ]])
nice, and
scipy.sparse.diags([1.1,1.2],-2,(4,2)).toarray()
gives
array([[ 0. , 0. ],
[ 0. , 0. ],
[ 1.1, 0. ],
[ 0. , 1.2]])
but
scipy.sparse.diags([1.1,1.2],-1,(4,2)).toarray()
gives an error saying ValueError: Diagonal length (index 0: 2 at offset -1) does not agree with matrix size (4, 2). Obviously the answer is
array([[ 0. , 0. ],
[ 1.1, 0. ],
[ 0. , 1.2],
[ 0. , 0. ]])
and for extra random behaviour we have
scipy.sparse.diags([1.1],-1,(4,2)).toarray()
giving
array([[ 0. , 0. ],
[ 1.1, 0. ],
[ 0. , 1.1],
[ 0. , 0. ]])
Anyone know if there is a function for constructing diagonal sparse matrices that actually works?
Executive summary: spdiags works correctly, even if the matrix input isn't the most intuitive. diags has a bug that affects some offsets in rectangular matrices. There is a bug fix on scipy github.
The example for spdiags is:
>>> data = array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])
>>> diags = array([0,-1,2])
>>> spdiags(data, diags, 4, 4).todense()
matrix([[1, 0, 3, 0],
[1, 2, 0, 4],
[0, 2, 3, 0],
[0, 0, 3, 4]])
Note that the 3rd column of data always appears in the 3rd column of the sparse. The other columns also line up. But they are omitted where they 'fall off the edge'.
The input to this function is a matrix, while the input to diags is a ragged list. The diagonals of the sparse matrix all have different numbers of values. So the specification has to accomodate this in one or other. spdiags does this by ignoring some values, diags by taking a list input.
The sparse.diags([1.1,1.2],-1,(4,2)) error is puzzling.
the spdiags equivalent does work:
In [421]: sparse.spdiags([[1.1,1.2]],-1,4,2).A
Out[421]:
array([[ 0. , 0. ],
[ 1.1, 0. ],
[ 0. , 1.2],
[ 0. , 0. ]])
The error is raised in this block of code:
for j, diagonal in enumerate(diagonals):
offset = offsets[j]
k = max(0, offset)
length = min(m + offset, n - offset)
if length <= 0:
raise ValueError("Offset %d (index %d) out of bounds" % (offset, j))
try:
data_arr[j, k:k+length] = diagonal
except ValueError:
if len(diagonal) != length and len(diagonal) != 1:
raise ValueError(
"Diagonal length (index %d: %d at offset %d) does not "
"agree with matrix size (%d, %d)." % (
j, len(diagonal), offset, m, n))
raise
The actual matrix constructor in the diags is:
dia_matrix((data_arr, offsets), shape=(m, n))
This is the same constructor that spdiags uses, but without any manipulation.
In [434]: sparse.dia_matrix(([[1.1,1.2]],-1),shape=(4,2)).A
Out[434]:
array([[ 0. , 0. ],
[ 1.1, 0. ],
[ 0. , 1.2],
[ 0. , 0. ]])
In dia format, the inputs are stored exactly as given by spdiags (complete with that matrix with extra values):
In [436]: M.data
Out[436]: array([[ 1.1, 1.2]])
In [437]: M.offsets
Out[437]: array([-1], dtype=int32)
As @user2357112 points out, length = min(m + offset, n - offset is wrong, producing 3 in the test case. Changing it to length = min(m + k, n - k) makes all cases for this (4,2) matrix work. But it fails with the transpose: diags([1.1,1.2], 1, (2, 4))
The correction, as of Oct 5, for this issue is:
https://github.com/pv/scipy-work/commit/529cbde47121c8ed87f74fa6445c05d71353eb6c
length = min(m + offset, n - offset, min(m,n))
With this fix, diags([1.1,1.2], 1, (2, 4)) works.