format and clip

.pre-commit-config.yaml

@@ -1,13 +1,13 @@
 repos:
 -   repo: https://github.com/psf/black
-    rev: 22.10.0
+    rev: 24.8.0
     hooks:
     - id: black
 -   repo: https://github.com/PyCQA/flake8
-    rev: 6.0.0
+    rev: 7.0.0
     hooks:
     - id: flake8
--   repo: https://github.com/pre-commit/mirrors-isort
-    rev: v5.10.1
+-   repo: https://github.com/pycqa/isort
+    rev: 5.13.2
     hooks:
     -   id: isort

setup.cfg

@@ -52,7 +52,7 @@ extras = rnn
 
 
 [testenv:min_numpy]
-deps = numpy==1.20
+deps = numpy==1.24
        {[testenv]deps}
 basepython = python3.9
 commands = pytest -n auto --hypothesis-profile ci \

src/mygrad/_utils/graph_tracking.py

@@ -1,6 +1,7 @@
 """
 Provides user interface for suspending computational graph tracking and back-propagation
 """
+
 from functools import wraps
 from typing import Any, Callable, TypeVar, cast
 

src/mygrad/_utils/lock_management.py

@@ -1,6 +1,7 @@
 """
 Provides utilities responsible for locking/releasing array writeability.
 """
+
 import os
 from collections import Counter, defaultdict
 from typing import (

src/mygrad/math/misc/funcs.py

@@ -4,7 +4,6 @@
 from numpy import ndarray
 
 import mygrad as mg
-from mygrad._numpy_version import NP_IS_V2
 from mygrad.math.misc.ops import MatMul
 from mygrad.tensor_base import Tensor, implements_numpy_override
 from mygrad.typing import ArrayLike, DTypeLikeReals, Mask
@@ -502,7 +501,7 @@ def clip(
     Tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
     >>> mg.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
     Tensor([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])"""
-    if not NP_IS_V2 and a_min is None and a_max is None:
+    if np.__version__ < "2.1.0" and a_min is None and a_max is None:  # pragma: no cover
         raise ValueError("`a_min` and `a_max` cannot both be set to `None`")
 
     if a_min is not None:

src/mygrad/operation_base.py

@@ -1,6 +1,7 @@
 """
 Defines the base class for mathematical operations capable of back-propagating
 gradients to their input tensors."""
+
 from abc import ABC, abstractmethod
 from numbers import Real
 from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union

src/mygrad/tensor_manip/tensor_joining/ops.py

@@ -52,9 +52,11 @@ def backward_var(self, grad, index, **kwargs) -> np.ndarray:
 
         return grad[
             tuple(
-                slice(None, None, None)
-                if dim != self.axis
-                else slice(self.indices[index], self.indices[index + 1])
+                (
+                    slice(None, None, None)
+                    if dim != self.axis
+                    else slice(self.indices[index], self.indices[index + 1])
+                )
                 for dim in range(var.data.ndim)
             )
         ]

tests/custom_strategies/__init__.py

@@ -1,4 +1,5 @@
 """ Custom hypothesis search strategies """
+
 import math
 from collections import defaultdict
 from functools import lru_cache, partial, reduce

tests/math/binary/test_binary_funcs.py

@@ -1,4 +1,5 @@
 """ Test all binary arithmetic operations, checks for appropriate broadcast behavior"""
+
 from functools import partial
 from numbers import Number
 from typing import Any, Dict

tests/nnet/layers/test_conv.py

@@ -1,4 +1,5 @@
 """ Test conv fwd-prop and back-prop for ND convs"""
+
 from typing import Tuple
 
 import hypothesis.extra.numpy as hnp

tests/nnet/layers/test_gru.py

@@ -39,9 +39,11 @@ def test_nonconstant_s0_raises(s0, dropout: float, out_constant: bool):
     Uz, Ur, Uh = Tensor(np.random.rand(3, C, D))
     bz, br, bh = Tensor(np.random.rand(3, D))
 
-    with does_not_raise() if (
-        out_constant or s0 is None or isinstance(s0, np.ndarray) or s0.constant
-    ) else pytest.raises(ValueError):
+    with (
+        does_not_raise()
+        if (out_constant or s0 is None or isinstance(s0, np.ndarray) or s0.constant)
+        else pytest.raises(ValueError)
+    ):
         gru(
             X,
             Uz,

tests/tensor_base/test_chainrule.py

@@ -209,9 +209,11 @@ def test_linear_graph(
     # dL/d2 = dL/d4 * d4/d3 * d3/d2 * d2/d1
     _check_grad(
         v1,
-        None
-        if (v4.constant or v3.constant or v2.constant)
-        else grad * (2 * np.exp(v2.data)) * (2 * v1.data),
+        (
+            None
+            if (v4.constant or v3.constant or v2.constant)
+            else grad * (2 * np.exp(v2.data)) * (2 * v1.data)
+        ),
     )
 
     # check the backprop clears graph & clear graph always propagates through the graph
@@ -501,9 +503,7 @@ def test_dynamic_interesting_graph(
     )
     _check_grad(v3, v3_grad)
 
-    v2_grad = (
-        None if (v5.constant or v4.constant) else grad * v3.data**2 * v1.data**2
-    )
+    v2_grad = None if (v5.constant or v4.constant) else grad * v3.data**2 * v1.data**2
     _check_grad(v2, v2_grad)
 
     v1_grad = None if v5.constant else grad * v4.data * v3.data

tests/tensor_ops/test_setitem.py

@@ -415,13 +415,15 @@ def test_setitem_bool_basic_index():
     index_strat=lambda x: hnp.arrays(shape=(2, 3), dtype=bool).map(
         lambda _x: (_x[0], _x[1])
     ),
-    value_strat=lambda o: hnp.arrays(
-        shape=broadcastable_shapes(o.shape, max_dims=o.ndim, max_side=max(o.shape)),
-        dtype=float,
-        elements=st.floats(-10.0, 10.0),
-    )
-    if o.shape and o.size
-    else st.floats(-10.0, 10.0).map(np.asarray),
+    value_strat=lambda o: (
+        hnp.arrays(
+            shape=broadcastable_shapes(o.shape, max_dims=o.ndim, max_side=max(o.shape)),
+            dtype=float,
+            elements=st.floats(-10.0, 10.0),
+        )
+        if o.shape and o.size
+        else st.floats(-10.0, 10.0).map(np.asarray)
+    ),
 )
 def test_setitem_bool_axes_index():
     """index consists of boolean arrays specified for each axis"""

tests/utils/test_utils.py

@@ -1,6 +1,5 @@
 """ Test `numerical_gradient`, `numerical_derivative`, and `broadcast_check`"""
 
-
 import hypothesis.extra.numpy as hnp
 import hypothesis.strategies as st
 import numpy as np

tests/wrappers/uber.py

@@ -302,10 +302,12 @@ def wrapper(shapes: hnp.BroadcastableShapes, constant, data: st.DataObject):
             if self.permit_0d_array_as_float:
                 # potentially cast a 0D array as a float
                 arrs = tuple(
-                    arr.item()
-                    if arr.ndim == 0
-                    and data.draw(st.booleans(), label=f"arr-{n} to float")
-                    else arr
+                    (
+                        arr.item()
+                        if arr.ndim == 0
+                        and data.draw(st.booleans(), label=f"arr-{n} to float")
+                        else arr
+                    )
                     for n, arr in enumerate(arrs)
                 )