Sparse matrix visualisation

Question

I'm working on FEM analysis. I just wanted to evaluate a simple matrix multiplication and see the numeric result. How can I see the elements of the sparse matrix?

the code that I have used for is:

U_h= 0.5 * np.dot(np.dot(U[np.newaxis], K), U[np.newaxis].T)

Since U is a 1x3 matrix, K is 3x3 matrix and U.T is 3x1 matrix, I expect a 1x1 matrix with a single number in it. However, the result is "[[<3x3 sparse matrix of type 'class 'numpy.float64' with 3 stored elements in Compressed Sparse Row format>]]"

score 0 · Accepted Answer · answered May 02 '19 at 18:55

In [260]: M = sparse.random(5,5,.2, format='csr')

What you got was the repr format of the matrix:

In [261]: M                                                                          
Out[261]: 
<5x5 sparse matrix of type '<class 'numpy.float64'>'
    with 5 stored elements in Compressed Sparse Row format>
In [262]: repr(M)                                                                    
Out[262]: "<5x5 sparse matrix of type '<class 'numpy.float64'>'\n\twith 5 stored elements in Compressed Sparse Row format>"

The str format used print is:

In [263]: print(M)                                                                   
  (1, 0)    0.7152749140462651
  (1, 1)    0.4298096228326874
  (1, 3)    0.8148327301300698
  (4, 0)    0.23366934073409018
  (4, 3)    0.6117499168861333
In [264]: str(M)                                                                     
Out[264]: '  (1, 0)\t0.7152749140462651\n  (1, 1)\t0.4298096228326874\n  (1, 3)\t0.8148327301300698\n  (4, 0)\t0.23366934073409018\n  (4, 3)\t0.6117499168861333'

If the matrix isn't big, displaying it as a dense array is nice. M.toarray() does that, or for short:

In [265]: M.A                                                                        
Out[265]: 
array([[0.        , 0.        , 0.        , 0.        , 0.        ],
       [0.71527491, 0.42980962, 0.        , 0.81483273, 0.        ],
       [0.        , 0.        , 0.        , 0.        , 0.        ],
       [0.        , 0.        , 0.        , 0.        , 0.        ],
       [0.23366934, 0.        , 0.        , 0.61174992, 0.        ]])

Thanks for the reply. it still does not work when I used M.A. the python response is: 'numpy.ndarray' object has no attribute 'A' or 'numpy.ndarray' object has no attribute 'toarray'. — Mert, May 03 '19 at 11:47
In that case the object is a regular array, not a sparse one, I missed tge [[]] in your display. That's a sparse matrix wrapped in a dense array. The use of np.dot is wrong. Ask a new question with more information - the variables and types, not just shapes. — hpaulj, May 03 '19 at 13:48
I used type(U_h) and got while when print(U_h) it gives [[<3x3 sparse matrix of type '' with 3 stored elements in Compressed Sparse Row format>]] — Mert, May 03 '19 at 13:57
you were right. I had to change K matrix to array and not U_h. So this is the solution: U_h= 0.5 * np.dot(np.dot(U.T, K.toarray()), U) — Mert, May 03 '19 at 14:47

score 0 · Answer 2 · answered May 02 '19 at 23:08

0

for a graphical inspection use plt.spy()
see an applied example here
see the reference manual here

answered May 02 '19 at 23:08

pyano

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Sparse matrix visualisation

2 Answers2