Add support for NumPy 2.0. (#681)

* Add support for NumPy 2.0.

* Avoid using private NumPy API.

* Allow for `OverflowError` in `can_store`.

* Replace `np.array(..., copy=False)` with `np.asarray(...)`.

.github/workflows/ci.yml

@@ -7,6 +7,7 @@ jobs:
       matrix:
         os: [ubuntu-latest]
         python: ['3.10', '3.11', '3.12']
+        pip_opts: ['']
         numba_boundscheck: [0]
         include:
           - os: macos-latest
@@ -16,6 +17,8 @@ jobs:
           - os: ubuntu-latest
             python: '3.10'
             numba_boundscheck: 1
+          - os: ubuntu-latest
+            pip_opts: "--pre -U"
       fail-fast: false
     runs-on: ${{ matrix.os }}
     env:
@@ -47,6 +50,7 @@ jobs:
       - name: Install package
         run: |
           pip install -e .[tests]
+          pip install ${{ matrix.pip_opts }} numpy numba
       - name: Run tests
         run: |
           pytest --pyargs sparse

sparse/numba_backend/_common.py

@@ -1876,7 +1876,7 @@ def asnumpy(a, dtype=None, order=None):
 
     if isinstance(a, SparseArray):
         a = a.todense()
-    return np.array(a, dtype=dtype, copy=False, order=order)
+    return np.asarray(a, dtype=dtype, order=order)
 
 
 # this code was taken from numpy.moveaxis

sparse/numba_backend/_coo/common.py

@@ -744,8 +744,6 @@ def roll(a, shift, axis=None):
     res : ndarray
         Output array, with the same shape as a.
     """
-    from numpy.core._exceptions import UFuncTypeError
-
     from .core import COO, as_coo
 
     a = as_coo(a)
@@ -786,7 +784,7 @@ def roll(a, shift, axis=None):
         for sh, ax in zip(shift, axis, strict=True):
             coords[ax] += sh
             coords[ax] %= a.shape[ax]
-    except UFuncTypeError as e:
+    except TypeError as e:
         if is_unsigned_dtype(coords.dtype):
             raise ValueError(
                 f"rolling with coords.dtype as {coords.dtype} is not safe. Try using a signed dtype."

sparse/numba_backend/_utils.py

@@ -622,7 +622,7 @@ def can_store(dtype, scalar):
             warnings.simplefilter("ignore")
             warnings.filterwarnings("error", "out-of-bound", DeprecationWarning)
             return np.array(scalar, dtype=dtype) == np.array(scalar)
-    except ValueError:
+    except (ValueError, OverflowError):
         return False