[pre-commit.ci] pre-commit autoupdate (#191)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/pre-commit/mirrors-prettier: v3.0.0-alpha.6 → v3.0.0-alpha.9-for-vscode](pre-commit/mirrors-prettier@v3.0.0-alpha.6...v3.0.0-alpha.9-for-vscode)
- [github.com/pre-commit/mirrors-mypy: v1.1.1 → v1.4.1](pre-commit/mirrors-mypy@v1.1.1...v1.4.1)

* Use np.prod instead of np.product

Remove usage of `np.product` that was deprecated in numpy 1.25.0, see numpy/numpy#23314.

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Wei Ji <23487320+weiji14@users.noreply.github.com>

.pre-commit-config.yaml

@@ -32,12 +32,12 @@ repos:
       - id: isort
 
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.0.0-alpha.6
+    rev: v3.0.0-alpha.9-for-vscode
     hooks:
       - id: prettier
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.1.1
+    rev: v1.4.1
     hooks:
       - id: mypy
         additional_dependencies: [

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]):

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()