Refactored numpy 2.4 try/except removed

Author: seanlaw

stumpy/core.py

@@ -4001,10 +4001,7 @@ def _subspace(D, k, include=None, discords=False):
     # `include` processing occur here since we are dealing with indices, not distances
     if include is not None:
         include = _preprocess_include(include)
-        try:
-            mask = np.in1d(sorted_idx, include)
-        except AttributeError:  # pragma: no cover
-            mask = np.isin(sorted_idx, include)
+        mask = np.isin(sorted_idx, include)
         include_idx = mask.nonzero()[0]
         exclude_idx = (~mask).nonzero()[0]
         sorted_idx[: include_idx.shape[0]], sorted_idx[include_idx.shape[0] :] = (

stumpy/floss.py

@@ -661,10 +661,7 @@ def update(self, t):
             self._Σ_T[:-1] = self._Σ_T[1:]
 
             M_T, Σ_T = core.compute_mean_std(self._T[-self._m :], self._m)
-            try:
-                self._M_T[-1], self._Σ_T[-1] = M_T, Σ_T
-            except ValueError:  # pragma: no cover
-                self._M_T[-1], self._Σ_T[-1] = M_T.item(), Σ_T.item()
+            self._M_T[-1], self._Σ_T[-1] = M_T.item(), Σ_T.item()
 
             D = core.mass(
                 self._finite_Q,

tests/test_core.py

@@ -1209,10 +1209,7 @@ def test_merge_topk_PI_without_overlap():
         col_idx = np.random.randint(0, k, size=n)
         for i in range(n):  # creating ties between values of PA and PB
             val = np.random.choice(PA[i], size=1, replace=False)
-            try:
-                PB[i, col_idx[i]] = val
-            except ValueError:  # pragma: no cover
-                PB[i, col_idx[i]] = val.item()
+            PB[i, col_idx[i]] = val.item()
         PB[:, :] = np.sort(PB, axis=1)  # sorting each row separately
 
         IA = np.arange(n * k).reshape(n, k)
@@ -1337,10 +1334,7 @@ def test_merge_topk_ρI_without_overlap():
         col_idx = np.random.randint(0, k, size=n)
         for i in range(n):  # creating ties between values of PA and PB
             val = np.random.choice(ρA[i], size=1, replace=False)
-            try:
-                ρB[i, col_idx[i]] = val
-            except ValueError:  # pragma: no cover
-                ρB[i, col_idx[i]] = val.item()
+            ρB[i, col_idx[i]] = val.item()
         ρB[:, :] = np.sort(ρB, axis=1)  # sorting each row separately
 
         IA = np.arange(n * k).reshape(n, k)