BUG: fixes for Dask

[skip cirrus] [skip circle]

Author: lucascolley

scipy/cluster/hierarchy.py

@@ -4154,7 +4154,7 @@ def leaders(Z, T):
     if T.shape[0] != Z.shape[0] + 1:
         raise ValueError('Mismatch: len(T)!=Z.shape[0] + 1.')
 
-    n_clusters = int(xp.unique_values(T).shape[0])
+    n_clusters = int(np.asarray(xp.unique_values(T)).shape[0])
     n_obs = int(Z.shape[0] + 1)
     L = np.zeros(n_clusters, dtype=np.int32)
     M = np.zeros(n_clusters, dtype=np.int32)

scipy/cluster/vq.py

@@ -317,8 +317,9 @@ def _kmeans(obs, guess, thresh=1e-5, xp=None):
         # recalc code_book as centroids of associated obs
         obs = np.asarray(obs)
         obs_code = np.asarray(obs_code)
+        code_book_len = np.asarray(code_book).shape[0]
         code_book, has_members = _vq.update_cluster_means(obs, obs_code,
-                                                          code_book.shape[0])
+                                                          code_book_len)
         obs = xp.asarray(obs)
         obs_code = xp.asarray(obs_code)
         code_book = xp.asarray(code_book)

scipy/differentiate/tests/test_differentiate.py

@@ -20,6 +20,7 @@
 
 @pytest.mark.skip_xp_backends('array_api_strict', reason=array_api_strict_skip_reason)
 @pytest.mark.skip_xp_backends('jax.numpy',reason=jax_skip_reason)
+@pytest.mark.skip_xp_backends('dask.array', reason='boolean indexing assignment')
 class TestDerivative:
 
     def f(self, x):

scipy/integrate/tests/test_tanhsinh.py

@@ -48,6 +48,9 @@ def wrapped(*arg_arrays):
 @pytest.mark.skip_xp_backends(
     'array_api_strict', reason='Currently uses fancy indexing assignment.'
 )
+@pytest.mark.skip_xp_backends(
+    'dask.array', reason='boolean indexing assignment'
+)
 @pytest.mark.skip_xp_backends(
     'jax.numpy', reason='JAX arrays do not support item assignment.'
 )

scipy/ndimage/_morphology.py

@@ -1726,6 +1726,7 @@ def morphological_laplace(input, size=None, footprint=None,
         tmp2 = grey_erosion(input, size, footprint, structure, None, mode,
                             cval, origin)
         np.add(tmp1, tmp2, tmp2)
+        input = np.asarray(input)
         np.subtract(tmp2, input, tmp2)
         np.subtract(tmp2, input, tmp2)
         return tmp2

scipy/ndimage/_support_alternative_backends.py

@@ -50,12 +50,13 @@ def wrapper(*args, **kwds):
                 elif result is None:
                     # inplace operations
                     return result
-                else:
-                    # lists/tuples
+                elif isinstance(result, (list, tuple)):
                     return type(result)(
                         xp.asarray(x) if isinstance(x, np.ndarray) else x
                         for x in result
                     )
+                else:
+                    return result
         return wrapper
     return inner
 

scipy/optimize/_chandrupatla.py

@@ -134,7 +134,7 @@ def _chandrupatla(func, a, b, *, args=(), xatol=None, xrtol=None,
     func, xs, fs, args, shape, dtype, xp = temp
     x1, x2 = xs
     f1, f2 = fs
-    status = xp.full_like(x1, xp.asarray(eim._EINPROGRESS),
+    status = xp.full_like(x1, eim._EINPROGRESS,
                           dtype=xp.int32)  # in progress
     nit, nfev = 0, 2  # two function evaluations performed above
     finfo = xp.finfo(dtype)
@@ -218,7 +218,7 @@ def post_termination_check(work):
         j = ((1 - xp.sqrt(1 - xi1)) < phi1) & (phi1 < xp.sqrt(xi1))
 
         f1j, f2j, f3j, alphaj = work.f1[j], work.f2[j], work.f3[j], alpha[j]
-        t = xp.full_like(alpha, xp.asarray(0.5))
+        t = xp.full_like(alpha, 0.5)
         t[j] = (f1j / (f1j - f2j) * f3j / (f3j - f2j)
                 - alphaj * f1j / (f3j - f1j) * f2j / (f2j - f3j))
 
@@ -400,8 +400,7 @@ def _chandrupatla_minimize(func, x1, x2, x3, *, args=(), xatol=None,
     x1, x2, x3 = xs
     f1, f2, f3 = fs
     phi = xp.asarray(0.5 + 0.5*5**0.5, dtype=dtype)[()]  # golden ratio
-    status = xp.full_like(x1, xp.asarray(eim._EINPROGRESS),
-                          dtype=xp.int32)  # in progress
+    status = xp.full_like(x1, eim._EINPROGRESS, dtype=xp.int32)  # in progress
     nit, nfev = 0, 3  # three function evaluations performed above
     fatol = xp.finfo(dtype).smallest_normal if fatol is None else fatol
     frtol = xp.finfo(dtype).smallest_normal if frtol is None else frtol