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

Here's my nomination for a faster, and possibly better, way to make a symmetric matrix from flat values:

def make_sym(val, n):
    # uses boolean mask
    # uses the same lower tri as np.triu
    mask = ~np.tri(5,k=-1,dtype=bool)
    out = np.zeros((n,n),dtype=val.dtype)
    out[mask] = val
    out.T[mask] = val
    return out

testing:

In [939]: val=np.arange(1,16)
In [940]: make_sym(val, 5)
Out[940]: 
array([[ 1,  2,  3,  4,  5],
       [ 2,  6,  7,  8,  9],
       [ 3,  7, 10, 11, 12],
       [ 4,  8, 11, 13, 14],
       [ 5,  9, 12, 14, 15]])

Like the other answers it uses out.T[] to assign the lower triangle.

Warren's answer uses np.triu_indices, which are the where values. This type of indexing is a bit slower than boolean masking.

But as I noted the np.triu that Divakar uses does not return a boolean mask in earlier numpy versions (e.g. 1.9). This is what prompted me to dig into the issue.

In 1.10 this function was rewritten as:

mask = np.tri(*m.shape[-2:], k=k-1, dtype=bool)
return np.where(mask, np.zeros(1, m.dtype), m)

I gain a bit of speed by replacing the where with ~mask. Same result, but just cutting out an intermediate step.