From 3f9167ba04afd1e6eb9241c5599afacbd1e770d8 Mon Sep 17 00:00:00 2001
From: Wei Ji <23487320+weiji14@users.noreply.github.com>
Date: Tue, 4 Jul 2023 14:03:50 +1200
Subject: [PATCH] Use np.prod instead of np.product

Remove usage of `np.product` that was deprecated in numpy 1.25.0, see https://github.com/numpy/numpy/pull/23314.
---
 xbatcher/generators.py |  2 +-
 xbatcher/testing.py    | 14 +++++++-------
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/xbatcher/generators.py b/xbatcher/generators.py
index 58799ba..f1f4a78 100644
--- a/xbatcher/generators.py
+++ b/xbatcher/generators.py
@@ -192,7 +192,7 @@ def _gen_empty_batch_selectors(self) -> BatchSelectorSet:
         Create an empty batch selector set that can be populated by appending
         patches to each batch.
         """
-        n_batches = np.product(list(self._n_batches_per_dim.values()))
+        n_batches = np.prod(list(self._n_batches_per_dim.values()))
         return {k: [] for k in range(n_batches)}
 
     def _gen_patch_numbers(self, ds: Union[xr.DataArray, xr.Dataset]):
diff --git a/xbatcher/testing.py b/xbatcher/testing.py
index 66546ef..36de58f 100644
--- a/xbatcher/testing.py
+++ b/xbatcher/testing.py
@@ -109,9 +109,9 @@ def _get_sample_length(
     else:
         batch_concat_dims = []
     return int(
-        np.product(list(non_specified_ds_dims.values()))
-        * np.product(list(non_input_batch_dims.values()))
-        * np.product(batch_concat_dims)
+        np.prod(list(non_specified_ds_dims.values()))
+        * np.prod(list(non_input_batch_dims.values()))
+        * np.prod(batch_concat_dims)
     )
 
 
@@ -209,7 +209,7 @@ def _get_nbatches_from_input_dims(generator: BatchGenerator) -> int:
     s : int
         Number of batches expected given ``input_dims`` and ``input_overlap``.
     """
-    nbatches_from_input_dims = np.product(
+    nbatches_from_input_dims = np.prod(
         [
             generator.ds.sizes[dim] // length
             for dim, length in generator.input_dims.items()
@@ -218,7 +218,7 @@ def _get_nbatches_from_input_dims(generator: BatchGenerator) -> int:
         ]
     )
     if generator.input_overlap:
-        nbatches_from_input_overlap = np.product(
+        nbatches_from_input_overlap = np.prod(
             [
                 (generator.ds.sizes[dim] - overlap)
                 // (generator.input_dims[dim] - overlap)
@@ -242,13 +242,13 @@ def validate_generator_length(generator: BatchGenerator) -> None:
     """
     non_input_batch_dims = _get_non_input_batch_dims(generator)
     duplicate_batch_dims = _get_duplicate_batch_dims(generator)
-    nbatches_from_unique_batch_dims = np.product(
+    nbatches_from_unique_batch_dims = np.prod(
         [
             generator.ds.sizes[dim] // length
             for dim, length in non_input_batch_dims.items()
         ]
     )
-    nbatches_from_duplicate_batch_dims = np.product(
+    nbatches_from_duplicate_batch_dims = np.prod(
         [
             generator.ds.sizes[dim] // length
             for dim, length in duplicate_batch_dims.items()