Fix some warnings and errors from partial tests

Author: milan-hl

mpnum/factory.py

@@ -228,7 +228,7 @@ def random_mpa(sites, ldim, rank, randstate=None, normalized=False,
            [-0.17262967,  2.4505633 ]])
     >>> random_mpa(2, 2, 3, rng, dtype=np.complex_).to_array()
     array([[-0.53552415+1.39701566j, -2.12128866+0.57913253j],
-           [-0.32652114+0.51490923j, -0.32222320-0.32675463j]])
+           [-0.32652114+0.51490923j, -0.3222232 -0.32675463j]])
 
     """
     randfun = ft.partial(_randfuncs[dtype], randstate=randstate)

mpnum/utils/extmath.py

@@ -95,7 +95,7 @@ def block_diag(summands, axes=(0, 1)):
 
     for array, shape in zip(summands, shapes):
         endpos = startpos + shape
-        pos = [slice(start, end) for start, end in zip(startpos, endpos)]
+        pos = tuple(slice(start, end) for start, end in zip(startpos, endpos))
         res[pos] += array
         startpos = endpos
 
@@ -248,8 +248,8 @@ def randomized_svd(M, n_components, n_oversamples=10, n_iter='auto',
         ``n_components`` is small (``< .1 * min(X.shape)``). Then,
         ``n_iter`` is set to 7.  This improves precision with few
         components. (default ``'auto'``)
-    :param str piter_normalizer: ``'auto'`` (default), ``'QR'``\ , ``'LU'``\ ,
-        ``'none'``\ .  Whether the power iterations are normalized with
+    :param str piter_normalizer: ``'auto'`` (default), ``'QR'``, ``'LU'``,
+        ``'none'``.  Whether the power iterations are normalized with
         step-by-step QR factorization (the slowest but most accurate),
         ``'none'`` (the fastest but numerically unstable when `n_iter` is
         large, e.g.  typically 5 or larger), or ``'LU'`` factorization

tests/extmath_test.py

@@ -73,7 +73,7 @@ def test_block_diag(rgen):
 @pt.mark.parametrize('piter_normalizer', [None, 'qr', 'lu', 'auto'])
 def test_approximate_range_finder(rows, cols, rank, dtype, piter_normalizer, rgen):
     # only guaranteed to work for low-rank matrices
-    if rank is 'fullrank':
+    if rank == 'fullrank':
         return
 
     rf_size = rank + 10
@@ -84,9 +84,8 @@ def test_approximate_range_finder(rows, cols, rank, dtype, piter_normalizer, rge
     Q = em.approx_range_finder(A, rf_size, 7, randstate=rgen,
                                piter_normalizer=piter_normalizer)
 
-    Q = np.asmatrix(Q)
     assert Q.shape == (rows, rf_size)
-    normdist = np.linalg.norm(A - Q * (Q.H * A), ord='fro')
+    normdist = np.linalg.norm(A - Q @ (Q.T.conj() @ A), ord='fro')
     assert normdist < 1e-7
 
 
@@ -98,7 +97,7 @@ def test_approximate_range_finder(rows, cols, rank, dtype, piter_normalizer, rge
                                             (20, mptest.random_fullrank)])
 def test_randomized_svd(rows, cols, rank, dtype, transpose, n_iter, target_gen,
                         rgen):
-    rank = min(rows, cols) - 2 if rank is 'fullrank' else rank
+    rank = min(rows, cols) - 2 if rank == 'fullrank' else rank
     A = target_gen(rows, cols, rank=rank, randstate=rgen, dtype=dtype)
 
     U_ref, s_ref, V_ref = utils.truncated_svd(A, k=rank)