Fix Numpy 2 Compatibility

Replace usage of np.product by np.prod.

stencil_benchmarks/benchmarks_collection/stencils/base.py

@@ -120,7 +120,7 @@ def strides(self):
 
     @property
     def data_size(self):
-        return len(self.args) * np.product(self.domain) * self.dtype_size
+        return len(self.args) * np.prod(self.domain) * self.dtype_size
 
     def inner_slice(self, shift=None, expand=None):
         if shift is None:
@@ -255,8 +255,8 @@ def args(self):
 
     @property
     def data_size(self):
-        return (2 * np.product(self.domain) +
-                np.product(np.array(self.domain) + 4)) * self.dtype_size
+        return (2 * np.prod(self.domain) +
+                np.prod(np.array(self.domain) + 4)) * self.dtype_size
 
     def verify_stencil(self, data_before, data_after):
         validation.check_equality('inp', data_before.inp, data_after.inp)
@@ -323,7 +323,7 @@ def data_size(self):
         else:
             reads = 15  # including ccol + dcol, but not datacol
             writes = 5  # including ccol + dcol, but not datacol
-        return (reads + writes) * np.product(self.domain) * self.dtype_size
+        return (reads + writes) * np.prod(self.domain) * self.dtype_size
 
     def verify_stencil(self, data_before, data_after):
         # pylint: disable=unsubscriptable-object

stencil_benchmarks/benchmarks_collection/stencils/openmp_blocked/mixin.py

@@ -107,7 +107,7 @@ def blocked(self, data):
             data.strides[2],
             data.strides[0],
         )
-        assert data.size == np.product(self.blocked_domain_with_halo)
+        assert data.size == np.prod(self.blocked_domain_with_halo)
         blocked_view = np.lib.stride_tricks.as_strided(
             data, self.blocked_domain_with_halo, blocked_strides)
 

stencil_benchmarks/tools/validation.py

@@ -103,7 +103,7 @@ def check_equality(name, result, expected):
     failure = ~np.isclose(result, expected, **_tolerances(result.dtype))
     if not np.any(failure):
         return
-    if result.ndim != 3 or np.product(result.shape) > 1000:
+    if result.ndim != 3 or np.prod(result.shape) > 1000:
         report = _report_failures_large(result, expected, failure)
     else:
         report = _report_failures_small(result, expected, failure)