Copy flat list of upper triangle entries to full matrix?

Question

I have the upper triangle entries (including diagonal) of a symmetric matrix in a flat list (concatenated rows), and I want to use them to fill in the full matrix, including the

Answer 1

Inspired by this solution, you can use boolean indexing to set elements and as such might be pretty efficient. Here's one way to implement it -

def mask_based_utri2mat(utri,ntotal):
    # Setup output array
    out = np.empty((ntotal,ntotal))

    # Create upper triang. mask
    mask = np.triu(np.ones((ntotal,ntotal),dtype=bool))

    # Set upper triang. elements with mask
    out[mask] = utri

    # Set lower triang. elements with transposed mask
    out.T[mask] = utri
    return out    

Runtime tests -

In [52]: # Inputs
    ...: ntotal = 100
    ...: utri = np.random.rand(np.triu_indices(ntotal)[0].size)
    ...: 

In [53]: np.allclose(mask_based_utri2mat(utri,ntotal),utri2mat(utri,ntotal))
Out[53]: True

In [54]: %timeit utri2mat(utri,ntotal)
1000 loops, best of 3: 270 µs per loop

In [55]: %timeit mask_based_utri2mat(utri,ntotal)
10000 loops, best of 3: 127 µs per loop

In [56]: # Inputs
    ...: ntotal = 1000
    ...: utri = np.random.rand(np.triu_indices(ntotal)[0].size)
    ...: 

In [57]: np.allclose(mask_based_utri2mat(utri,ntotal),utri2mat(utri,ntotal))
Out[57]: True

In [58]: %timeit utri2mat(utri,ntotal)
10 loops, best of 3: 53.9 ms per loop

In [59]: %timeit mask_based_utri2mat(utri,ntotal)
100 loops, best of 3: 15.1 ms per loop