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

pre-commit-ci[bot] · weiji14 · web-flow · commit ab5fa39a95cf · 2023-07-04T20:00:46.000+12:00
* [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>
diff --git a/.pre-commit-config.yaml b/.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: [
diff --git 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
@@ -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()

Original file line number	Diff line number	Diff line change
`@@ -109,9 +109,9 @@ def _get_sample_length(`
`109`	`109`	`else:`
`110`	`110`	`batch_concat_dims = []`
`111`	`111`	`return int(`
`112`		`- np.product(list(non_specified_ds_dims.values()))`
`113`		`- * np.product(list(non_input_batch_dims.values()))`
`114`		`- * np.product(batch_concat_dims)`
	`112`	`+ np.prod(list(non_specified_ds_dims.values()))`
	`113`	`+ * np.prod(list(non_input_batch_dims.values()))`
	`114`	`+ * np.prod(batch_concat_dims)`
`115`	`115`	`)`
`116`	`116`
`117`	`117`
`@@ -209,7 +209,7 @@ def _get_nbatches_from_input_dims(generator: BatchGenerator) -> int:`
`209`	`209`	`s : int`
`210`	`210`	Number of batches expected given ``input_dims`` and ``input_overlap``.
`211`	`211`	`"""`
`212`		`- nbatches_from_input_dims = np.product(`
	`212`	`+ nbatches_from_input_dims = np.prod(`
`213`	`213`	`[`
`214`	`214`	`generator.ds.sizes[dim] // length`
`215`	`215`	`for dim, length in generator.input_dims.items()`
`@@ -218,7 +218,7 @@ def _get_nbatches_from_input_dims(generator: BatchGenerator) -> int:`
`218`	`218`	`]`
`219`	`219`	`)`
`220`	`220`	`if generator.input_overlap:`
`221`		`- nbatches_from_input_overlap = np.product(`
	`221`	`+ nbatches_from_input_overlap = np.prod(`
`222`	`222`	`[`
`223`	`223`	`(generator.ds.sizes[dim] - overlap)`
`224`	`224`	`// (generator.input_dims[dim] - overlap)`
`@@ -242,13 +242,13 @@ def validate_generator_length(generator: BatchGenerator) -> None:`
`242`	`242`	`"""`
`243`	`243`	`non_input_batch_dims = _get_non_input_batch_dims(generator)`
`244`	`244`	`duplicate_batch_dims = _get_duplicate_batch_dims(generator)`
`245`		`- nbatches_from_unique_batch_dims = np.product(`
	`245`	`+ nbatches_from_unique_batch_dims = np.prod(`
`246`	`246`	`[`
`247`	`247`	`generator.ds.sizes[dim] // length`
`248`	`248`	`for dim, length in non_input_batch_dims.items()`
`249`	`249`	`]`
`250`	`250`	`)`
`251`		`- nbatches_from_duplicate_batch_dims = np.product(`
	`251`	`+ nbatches_from_duplicate_batch_dims = np.prod(`
`252`	`252`	`[`
`253`	`253`	`generator.ds.sizes[dim] // length`
`254`	`254`	`for dim, length in duplicate_batch_dims.items()`