Updated all np.product calls to np.prod (#5493)

* Updated all np.product calls to np.prod * added whats new * review responses * no typos here * added link to name * Update docs/src/whatsnew/latest.rst Co-authored-by: Elias <[email protected]> * Update docs/src/whatsnew/latest.rst Co-authored-by: Elias <[email protected]> --------- Co-authored-by: Elias <[email protected]>
SciTools · Sep 21, 2023 · b6e39d7 · b6e39d7
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diff --git a/docs/src/whatsnew/latest.rst b/docs/src/whatsnew/latest.rst
@@ -82,6 +82,9 @@ This document explains the changes made to Iris for this release
    working properly. (Main pull request: :pull:`5437`, more detail:
    :pull:`5430`, :pull:`5431`, :pull:`5432`, :pull:`5434`, :pull:`5436`)
 
+#. `@acchamber`_ removed several warnings from iris related to Numpy 1.25 deprecations.
+   (:pull:`5493`)
+
 #. `@trexfeathers`_ replaced all uses of the ``logging.WARNING`` level, in
    favour of using Python warnings, following team agreement. (:pull:`5488`)
 
@@ -90,11 +93,14 @@ This document explains the changes made to Iris for this release
 
 
 
+
 .. comment
     Whatsnew author names (@github name) in alphabetical order. Note that,
     core dev names are automatically included by the common_links.inc:
 
 .. _@scottrobinson02: https://github.com/scottrobinson02
+.. _@acchamber: https://github.com/acchamber
+
 
 .. comment
     Whatsnew resources in alphabetical order:
diff --git a/lib/iris/fileformats/_structured_array_identification.py b/lib/iris/fileformats/_structured_array_identification.py
@@ -417,7 +417,7 @@ def filter_strides_of_length(length):
                 # If we are to build another dimension on top of this possible
                 # structure, we need to compute the stride that would be
                 # needed for that dimension.
-                next_stride = np.product(
+                next_stride = np.prod(
                     [struct.size for (_, struct) in potential]
                 )
 

diff --git a/lib/iris/pandas.py b/lib/iris/pandas.py
@@ -398,7 +398,7 @@ def as_cubes(
 
     cube_shape = getattr(pandas_index, "levshape", (pandas_index.nunique(),))
     n_rows = len(pandas_structure)
-    if np.product(cube_shape) > n_rows:
+    if np.prod(cube_shape) > n_rows:
         message = (
             f"Not all index values have a corresponding row - {n_rows} rows "
             f"cannot be reshaped into {cube_shape}. Consider padding with NaN "

diff --git a/lib/iris/tests/integration/netcdf/test_thread_safety.py b/lib/iris/tests/integration/netcdf/test_thread_safety.py
@@ -38,7 +38,7 @@ def tiny_chunks():
     def _check_tiny_loaded_chunks(cube: Cube):
         assert cube.has_lazy_data()
         cube_lazy_data = cube.core_data()
-        assert np.product(cube_lazy_data.chunksize) < cube_lazy_data.size
+        assert np.prod(cube_lazy_data.chunksize) < cube_lazy_data.size
 
     with dask.config.set({"array.chunk-size": "1KiB"}):
         yield _check_tiny_loaded_chunks

diff --git a/lib/iris/tests/unit/pandas/test_pandas.py b/lib/iris/tests/unit/pandas/test_pandas.py
@@ -1075,7 +1075,7 @@ def test_ancillary_variable(self):
     def test_3d_with_2d_coord(self):
         df = self._create_pandas(index_levels=3)
         coord_shape = df.index.levshape[:2]
-        coord_values = np.arange(np.product(coord_shape))
+        coord_values = np.arange(np.prod(coord_shape))
         coord_name = "foo"
         df[coord_name] = coord_values.repeat(df.index.levshape[-1])
         result = iris.pandas.as_cubes(df, aux_coord_cols=[coord_name])
@@ -1089,7 +1089,7 @@ def test_3d_with_2d_coord(self):
     def test_coord_varies_all_indices(self):
         df = self._create_pandas(index_levels=3)
         coord_shape = df.index.levshape
-        coord_values = np.arange(np.product(coord_shape))
+        coord_values = np.arange(np.prod(coord_shape))
         coord_name = "foo"
         df[coord_name] = coord_values
         result = iris.pandas.as_cubes(df, aux_coord_cols=[coord_name])
@@ -1105,7 +1105,7 @@ def test_category_coord(self):
         #  increment.
         df = self._create_pandas(index_levels=2)
         coord_shape = df.index.levshape
-        coord_values = np.arange(np.product(coord_shape))
+        coord_values = np.arange(np.prod(coord_shape))
         coord_name = "foo"
 
         # Create a repeating value along a dimension.