pre-commit autoupdate

.devcontainer/Dockerfile

@@ -18,4 +18,4 @@ RUN if [ "${NODE_VERSION}" != "none" ]; then su vscode -c "umask 0002 && . /usr/
 #     && apt-get -y install --no-install-recommends <your-package-list-here>
 
 # [Optional] Uncomment this line to install global node packages.
-# RUN su vscode -c "source /usr/local/share/nvm/nvm.sh && npm install -g <your-package-here>" 2>&1
+# RUN su vscode -c "source /usr/local/share/nvm/nvm.sh && npm install -g <your-package-here>" 2>&1

.pre-commit-config.yaml

@@ -2,29 +2,33 @@
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
       - id: check-added-large-files
       - id: check-merge-conflict
+      - id: check-executables-have-shebangs
+      - id: check-shebang-scripts-are-executable
+      - id: end-of-file-fixer
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.3.1
+    rev: v3.15.0
     hooks:
       - id: pyupgrade
         args:
           - "--py38-plus"
-  - repo: https://github.com/psf/black
-    rev: 23.1.0
+  - repo: https://github.com/psf/black-pre-commit-mirror
+    rev: 23.12.1
     hooks:
       - id: black
   - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
+    rev: 5.13.2
     hooks:
       - id: isort
   - repo: https://github.com/PyCQA/flake8
-    rev: 6.0.0
+    rev: 6.1.0
     hooks:
       - id: flake8
         additional_dependencies:
+          - flake8-absolute-import
           # - flake8-annotations
           # - flake8-builtins
           - flake8-comprehensions
@@ -37,7 +41,7 @@ repos:
           - flake8-use-fstring
           - flake8-use-pathlib
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.0.0-alpha.4
+    rev: v3.1.0
     hooks:
       - id: prettier
         args:

donk/costs/cost_function_symbolic.py

@@ -30,8 +30,8 @@ def __init__(self, cost_fun: Callable[[np.ndarray, np.ndarray], np.ndarray], T:
         self.T, self.dX, self.dU = T, dX, dU
 
         # Create symbols
-        X_sym = np.array(symbols(f"x:{(T+1)*dX}")).reshape(T + 1, dX)
-        U_sym = np.array(symbols(f"u:{T*dU}")).reshape(T, dU)
+        X_sym = np.array(symbols(f"x:{(T + 1) * dX}")).reshape(T + 1, dX)
+        U_sym = np.array(symbols(f"u:{T * dU}")).reshape(T, dU)
 
         # Eval costs
         costs = cost_fun(X_sym, U_sym)
@@ -123,8 +123,8 @@ def cost_fun(X: np.ndarray, U: np.ndarray) -> np.ndarray:
         super().__init__(cost_fun, T, dX, dU)
 
         # Create symbols
-        X_sym = np.array(symbols(f"x:{(T+1)*dX}")).reshape(T + 1, dX)
-        U_sym = np.array(symbols(f"u:{T*dU}")).reshape(T, dU)
+        X_sym = np.array(symbols(f"x:{(T + 1) * dX}")).reshape(T + 1, dX)
+        U_sym = np.array(symbols(f"u:{T * dU}")).reshape(T, dU)
 
         # Lambdify and vectorize cost functions
         self.cost_funs = [

donk/models/tvlg.py

@@ -28,12 +28,12 @@ def __init__(
         self.T, dY, dX = coefficients.shape
 
         # Check shapes
-        assert coefficients.shape == (self.T, dY, dX), f"{coefficients.shape} != {(self.T, dY, dX )}"
+        assert coefficients.shape == (self.T, dY, dX), f"{coefficients.shape} != {(self.T, dY, dX)}"
         assert intercept.shape == (self.T, dY), f"{intercept.shape} != {(self.T, dY)}"
         if covar is not None:
             assert covar.shape == (self.T, dY, dY), f"{covar.shape} != {(self.T, dY, dY)}"
         if inv_covar is not None:
-            assert inv_covar.shape == (self.T, dY, dY), f"{inv_covar.shape} != {(self.T,dY, dY)}"
+            assert inv_covar.shape == (self.T, dY, dY), f"{inv_covar.shape} != {(self.T, dY, dY)}"
 
         self._coefficients = coefficients
         self._intercept = intercept

donk/samples/pool.py

@@ -24,7 +24,7 @@ def add(self, X: np.ndarray, U: np.ndarray) -> None:
         """
         # Check shapes
         assert X.shape[0] == U.shape[0], f"{X.shape[0]} != {U.shape[0]}"
-        assert X.shape[1] == U.shape[1] + 1, f"{X.shape[1]} != {U.shape[1]+1}"
+        assert X.shape[1] == U.shape[1] + 1, f"{X.shape[1]} != {U.shape[1] + 1}"
         if self._XUX is None:
             # Pool was empty, assume dimensions
             self.dX, self.dU = X.shape[-1], U.shape[-1]

donk/samples/sampler_gym.py

@@ -70,7 +70,7 @@ def take_sample(
             U[t] = pol.act(X[t], t, noise[t] if rng is not None else None)
             obs, _, done, _ = self.env.step(self.convert_action(U[t]))
             if done and t < T - 1:
-                raise Exception(f"Iteration ended prematurely {t+1}/{T}")
+                raise Exception(f"Iteration ended prematurely {t + 1}/{T}")
             X[t + 1] = self.convert_observation(obs)
 
         return X, U

donk/samples/state_dist.py

@@ -17,7 +17,7 @@ def __init__(self, mean: np.ndarray, covar: np.ndarray) -> None:
         dX = mean.shape[-1]
 
         # Check shapes
-        assert covar.shape[-2:] == (dX, dX), f"{covar.shape[-2:]} != {(dX, dX )}"
+        assert covar.shape[-2:] == (dX, dX), f"{covar.shape[-2:]} != {(dX, dX)}"
         assert mean.shape[:-1] == covar.shape[:-2], f"{mean.shape[:-1]} != {covar.shape[:-2]}"
 
         self.mean = mean

setup.cfg

@@ -1,7 +1,7 @@
 [flake8]
-ignore = E203,E741,W503,D100,D104,D105,PL123,ANN101
+ignore = E203,E741,W503,D100,D104,D105,PL123,ANN101,ANN102
 per-file-ignores =
-    tests/*:D101,D102,ANN001,ANN201,ANN202
+    tests/*:D101,D102,D103,ANN201,ABS101
 max-line-length = 120
 docstring-convention = google
 known-modules = donk.ai:[donk,tests]