NumPy 2.0 support. (#625)

Author: hameerabbasi

sparse/_utils.py

@@ -358,7 +358,7 @@ def random_value_array(value, fraction):
     def replace_values(n):
         i = int(n * fraction)
 
-        ar = np.empty((n,), dtype=np.float_)
+        ar = np.empty((n,), dtype=np.float64)
         ar[:i] = value
         ar[i:] = default_rng.random(n - i)
         return ar
@@ -465,7 +465,7 @@ def html_table(arr):
     table = ["<table><tbody>"]
     headings = ["Format", "Data Type", "Shape", "nnz", "Density", "Read-only"]
 
-    density = np.float_(arr.nnz) / np.float_(arr.size)
+    density = np.float64(arr.nnz) / np.float64(arr.size)
 
     info = [
         type(arr).__name__.lower(),
@@ -482,7 +482,9 @@ def html_table(arr):
         headings.append("Size")
         info.append(human_readable_size(arr.nbytes))
         headings.append("Storage ratio")
-        info.append(f"{np.float_(arr.nbytes) / np.float_(reduce(operator.mul, arr.shape, 1) * arr.dtype.itemsize):.2f}")
+        info.append(
+            f"{np.float64(arr.nbytes) / np.float64(reduce(operator.mul, arr.shape, 1) * arr.dtype.itemsize):.2f}"
+        )
 
     # compressed_axes
     if type(arr).__name__ == "GCXS":

sparse/tests/test_coo.py

@@ -550,7 +550,7 @@ def test_advanced_indexing(index):
 
 
 def test_custom_dtype_slicing():
-    dt = np.dtype([("part1", np.float_), ("part2", np.int64, (2,)), ("part3", np.int64, (2, 2))])
+    dt = np.dtype([("part1", np.float64), ("part2", np.int64, (2,)), ("part3", np.int64, (2, 2))])
 
     x = np.zeros((2, 3, 4), dtype=dt)
     x[1, 1, 1] = (0.64, [4, 2], [[1, 2], [3, 0]])
@@ -800,7 +800,7 @@ def test_cache_csr():
 def test_empty_shape():
     x = COO(np.empty((0, 1), dtype=np.int8), [1.0])
     assert x.shape == ()
-    assert_eq(2 * x, np.float_(2.0))
+    assert_eq(2 * x, np.float64(2.0))
 
 
 def test_single_dimension():
@@ -876,7 +876,7 @@ def test_triul(shape, k):
 
 
 def test_empty_reduction():
-    x = np.zeros((2, 3, 4), dtype=np.float_)
+    x = np.zeros((2, 3, 4), dtype=np.float64)
     xs = COO.from_numpy(x)
 
     assert_eq(x.sum(axis=(0, 2)), xs.sum(axis=(0, 2)))

sparse/tests/test_elemwise.py

@@ -378,7 +378,7 @@ def test_ndarray_densification_fails(rng):
 
 def test_elemwise_noargs():
     def func():
-        return np.float_(5.0)
+        return np.float64(5.0)
 
     with pytest.raises(ValueError, match=r"None of the args is sparse:"):
         sparse.elemwise(func)