bump version and lint lblack

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "tensortrax"
-version = "0.0.16"
+version = "0.1.0"
 description = "Math on (Hyper-Dual) Tensors with Trailing Axes"
 readme = "README.md"
 requires-python = ">=3.7"

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = tensortrax
-version = 0.0.16
+version = 0.1.0
 author = Andreas Dutzler
 author_email = a.dutzler@gmail.com
 description = Math on (Hyper-Dual) Tensors with Trailing Axes

tensortrax/_evaluate.py

@@ -40,20 +40,20 @@ def kernel(a, x, δx, Δx, args, kwargs):
             func = fun(t, *args, **kwargs)
             fx[:] = f(func)
             dfdx[a] = δ(func)
-        
+
         if not parallel:
             for a in indices:
                 kernel(a, x, δx, Δx, args, kwargs)
-                
+
         else:
             threads = [
                 Thread(target=kernel, args=(a, x, δx, Δx, args, kwargs))
                 for a in indices
             ]
-            
+
             for th in threads:
                 th.start()
-    
+
             for th in threads:
                 th.join()
 
@@ -81,20 +81,20 @@ def kernel(a, b, x, δx, Δx, args, kwargs):
             fx[:] = f(func)
             dfdx[a] = δ(func)
             d2fdx2[a, b] = d2fdx2[b, a] = Δδ(func)
-        
+
         if not parallel:
             for a, b in indices:
                 kernel(a, b, x, δx, Δx, args, kwargs)
-                
+
         else:
             threads = [
                 Thread(target=kernel, args=(a, b, x, δx, Δx, args, kwargs))
                 for a, b in indices
             ]
-            
+
             for th in threads:
                 th.start()
-    
+
             for th in threads:
                 th.join()
 

tensortrax/_tensor.py

@@ -107,7 +107,7 @@ def __sub__(self, B):
             Δx = Δ(A)
             Δδx = Δδ(A)
         return Tensor(x=x, δx=δx, Δx=Δx, Δδx=Δδx, ntrax=A.ntrax)
-    
+
     def __rsub__(self, B):
         return -self.__sub__(B)
 
@@ -147,7 +147,7 @@ def T(self):
 
     def __matmul__(self, B):
         return matmul(self, B)
-    
+
     def __rmatmul__(self, B):
         return matmul(B, self)
 
@@ -160,16 +160,16 @@ def einsum2(subscripts, *operands):
     "Einsum with two operands."
     A, B = operands
     _einsum = lambda *operands: np.einsum(subscripts, *operands)
-    
+
     if isinstance(A, Tensor) and isinstance(B, Tensor):
         x = _einsum(f(A), f(B))
         δx = _einsum(δ(A), f(B)) + _einsum(f(A), δ(B))
         Δx = _einsum(Δ(A), f(B)) + _einsum(f(A), Δ(B))
         Δδx = (
-                _einsum(Δδ(A), f(B))
-                + _einsum(f(A), Δδ(B))
-                + _einsum(δ(A), Δ(B))
-                + _einsum(Δ(A), δ(B))
+            _einsum(Δδ(A), f(B))
+            + _einsum(f(A), Δδ(B))
+            + _einsum(δ(A), Δ(B))
+            + _einsum(Δ(A), δ(B))
         )
         ntrax = A.ntrax
     elif isinstance(A, Tensor) and not isinstance(B, Tensor):

tensortrax/math/__init__.py

@@ -23,4 +23,4 @@
     einsum,
     matmul,
 )
-from . import _math_array as array
+from . import _math_array as array

tensortrax/math/_linalg/__init__.py

@@ -9,4 +9,4 @@
 """
 
 from ._linalg_array import det as _det, inv as _inv
-from ._linalg_tensor import det, eigvalsh
+from ._linalg_tensor import det, eigvalsh

tensortrax/math/_math_array.py

@@ -22,4 +22,4 @@ def cross(a, b):
     "Cross product of two vectors a and b."
     return np.einsum(
         "...i->i...", np.cross(np.einsum("i...->...i", a), np.einsum("i...->...i", b))
-    )
+    )

tensortrax/math/_math_tensor.py

@@ -101,4 +101,3 @@ def tanh(A):
         )
     else:
         return np.tanh(A)
-

tests/test_hessian.py

@@ -32,11 +32,11 @@ def test_function_gradient_hessian():
             ww = tr.function(fun, ntrax=2, parallel=parallel)(F)
             dwdf, w = tr.gradient(fun, ntrax=2, parallel=parallel)(F)
             d2WdF2, dWdF, W = tr.hessian(fun, ntrax=2, parallel=parallel)(F)
-    
+
             assert W.shape == (1, 1)
             assert dWdF.shape == (3, 3, 1, 1)
             assert d2WdF2.shape == (3, 3, 3, 3, 1, 1)
-    
+
             assert np.allclose(w, ww)
             assert np.allclose(w, W)
             assert np.allclose(dwdf, dWdF)

tests/test_hessian_vector_product.py

@@ -20,16 +20,18 @@ def ogden(F, mu=1, alpha=2):
 def test_hvp():
 
     F = δF = ΔF = (np.eye(3).ravel() + np.arange(9) / 10).reshape(3, 3, 1, 1)
-    
+
     for parallel in [False, True]:
 
         for fun in [neo_hooke, ogden]:
             δfun = tr.gradient_vector_product(fun, ntrax=2, parallel=parallel)(F, δF)
-            Δδfun = tr.hessian_vector_product(fun, ntrax=2, parallel=parallel)(F, δF, ΔF)
-
+            Δδfun = tr.hessian_vector_product(fun, ntrax=2, parallel=parallel)(
+                F, δF, ΔF
+            )
+
             assert δfun.shape == (1, 1)
             assert Δδfun.shape == (1, 1)
-    
+
             assert not np.any(np.isnan(δfun))
             assert not np.any(np.isnan(Δδfun))
 

tests/test_math.py

@@ -5,52 +5,54 @@
 
 
 def test_math():
-    
+
     F = np.eye(3) + np.arange(9).reshape(3, 3) / 10
     T = tr.Tensor(F)
-    
+
     assert isinstance(T @ F, tr.Tensor)
     assert isinstance(F @ T, tr.Tensor)
     assert isinstance(T @ T, tr.Tensor)
-    
+
     assert isinstance(T * F, tr.Tensor)
     assert isinstance(F * T, tr.Tensor)
     assert isinstance(T * T, tr.Tensor)
-    
+
     assert isinstance(T / F, tr.Tensor)
-    with pytest.raises(TypeError): F / T
-    with pytest.raises(NotImplementedError): T / T
-
+    with pytest.raises(TypeError):
+        F / T
+    with pytest.raises(NotImplementedError):
+        T / T
+
     assert isinstance(T + F, tr.Tensor)
     assert isinstance(F + T, tr.Tensor)
     assert isinstance(T + T, tr.Tensor)
-    
+
     assert isinstance(T - F, tr.Tensor)
     assert isinstance(F - T, tr.Tensor)
     assert isinstance(T - T, tr.Tensor)
-    
+
     assert np.allclose((-T).x, -F)
-    
+
     F = np.eye(3) + np.arange(9).reshape(3, 3) / 10
     T = tr.Tensor(F)
-    
+
     assert np.allclose(tm.linalg.det(F), tm.linalg.det(T).x)
-    
+
     tm.linalg._det(F[:2, :2])
     tm.linalg._det(F[:1, :1])
     tm.linalg._inv(F[:2, :2])
-    
+
     for fun in [tm.sin, tm.cos, tm.tan, tm.tanh, tm.sqrt]:
         assert np.allclose(fun(F), fun(T).x)
-        
+
     for fun in [tm.linalg.det]:
         assert np.allclose(fun(F), fun(T).x)
-    
+
     assert tm.linalg.eigvalsh(T).shape == (3,)
-    
+
     assert tm.array.cross(F, F).shape == F.shape
     assert tm.array.eye(F).shape == F.shape
-    
+
     with pytest.raises(NotImplementedError):
         tm.einsum("ij...,kl...,mn...->ijklmn...", T, T, T)