[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -1,22 +1,22 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v5.0.0
     hooks:
       - id: check-yaml
       - id: requirements-txt-fixer
       - id: trailing-whitespace
   - repo: https://github.com/psf/black
-    rev: 22.10.0
+    rev: 24.10.0
     hooks:
       - id: black
   - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.5.2
+    rev: 1.9.1
     hooks:
       - id: nbqa-black
       - id: nbqa-isort
       # - id: nbqa-flake8
   - repo: https://github.com/PyCQA/isort
-    rev: 5.10.1
+    rev: 5.13.2
     hooks:
       - id: isort
   # - repo: https://github.com/pycqa/flake8

experiments/plot/plot_gifs.py

@@ -121,7 +121,7 @@ def plot_gif(
         fig=fig,
         func=update_custom,
         frames=num_episodes,
-        interval=250
+        interval=250,
         # interval=150
     )
     pathlib.Path(save_name).parents[0].mkdir(parents=True, exist_ok=True)

moderl/controllers/base.py

@@ -34,9 +34,7 @@ def control_dim(self) -> int:
 class TrajectoryOptimisationController(ControllerInterface):
     trajectory_optimiser: TrajectoryOptimiser
 
-    def __call__(
-        self, timestep: Optional[int] = None, dist: bool = False
-    ) -> Union[
+    def __call__(self, timestep: Optional[int] = None, dist: bool = False) -> Union[
         tfd.Distribution,
         Union[ttf.Tensor1[ControlDim], ttf.Tensor2[Horizon, ControlDim]],
     ]:

moderl/objectives.py

@@ -117,7 +117,7 @@ def bernoulli_entropy(
     mode_probs = dynamics.mosvgpe.gating_network.gp.likelihood.predict_mean_and_var(
         input_dists.mean(),
         Fmu=h_means,
-        Fvar=h_covs
+        Fvar=h_covs,
         # input_means, Fmu=h_means, Fvar=h_vars
     )[0]
     print("mode_probs.shape")
@@ -224,7 +224,7 @@ def bald_objective_sampling(input_dists):
         h_samples = dynamics.mosvgpe.gating_network.gp.predict_f_samples(
             input_dists.mean(),
             num_samples=10,
-            full_cov=False
+            full_cov=False,
             # input_dists.mean(), num_samples=10, full_cov=True
         )
         print("h_samples")
@@ -339,7 +339,7 @@ def entropy_approx(h_means, h_vars, mode_probs):
     def bald_objective_closed_form():
         h_means, h_vars = dynamics.mosvgpe.gating_network.gp.predict_f(
             input_dists.mean(),
-            full_cov=True
+            full_cov=True,
             # full_cov=False
             # input_dists.mean(), full_cov=True
         )

subtrees/mosvgpe/mosvgpe/mixture_of_experts.py

@@ -22,6 +22,7 @@
 
 logger = logging.getLogger(__name__)
 
+
 # class MixtureOfExpertsBase(gpf.models.BayesianModel, abc.ABC):
 # class MixtureOfExpertsBase(tf.keras.Model, abc.ABC):
 class MixtureOfExpertsBase(tf.keras.Model, BayesianModel, abc.ABC):

subtrees/simenvs/simenvs/point_mass/generate_dataset.py

@@ -49,7 +49,10 @@ def generate_point_mass_dataset(
         toml_env_config_file, gating_bitmap_filename=gating_bitmap_file
     )
 
-    (state_action_inputs, delta_state_outputs,) = generate_transitions_dataset(
+    (
+        state_action_inputs,
+        delta_state_outputs,
+    ) = generate_transitions_dataset(
         num_states=num_states,
         num_actions=num_actions,
         env=env,

subtrees/simenvs/simenvs/quadcopter/scenario-1/configs/env_config.toml

@@ -3,7 +3,7 @@ gating_bitmap = "./bitmaps/gating_mask.bmp"  # filename for gating bitmap
 # gating_bitmap = "../scenario-1/gating_mask.bmp"  # filename for gating bitmap
 
 # lists are converted to 1D np.arrays and floats/ints are broadcast
-# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state) 
+# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state)
 # state constraints
 min_state = [-3.0, -2.0]
 max_state = [3.0, 3.0]

subtrees/simenvs/simenvs/quadcopter/scenario-1/generate_const_action_dataset.py

@@ -21,7 +21,10 @@
 # configure environment from toml config file
 env = parse_toml_config_to_VelocityControlledQuadcopter2DEnv(toml_env_config_file)
 
-(state_action_inputs, delta_state_outputs,) = generate_transitions_dataset_const_action(
+(
+    state_action_inputs,
+    delta_state_outputs,
+) = generate_transitions_dataset_const_action(
     action, num_states=num_states, env=env, omit_data_mask=omit_data_mask
 )
 

subtrees/simenvs/simenvs/quadcopter/scenario-1/generate_dataset.py

@@ -16,7 +16,10 @@
 # configure environment from toml config file
 env = parse_toml_config_to_VelocityControlledQuadcopter2DEnv(toml_env_config_file)
 
-(state_action_inputs, delta_state_outputs,) = generate_transitions_dataset(
+(
+    state_action_inputs,
+    delta_state_outputs,
+) = generate_transitions_dataset(
     num_states=num_states,
     num_actions=num_actions,
     env=env,

subtrees/simenvs/simenvs/velocity_controlled_point_mass/generate_dataset.py

@@ -50,7 +50,10 @@ def generate_point_mass_dataset(
         toml_env_config_file, gating_bitmap_filename=gating_bitmap_file
     )
 
-    (state_action_inputs, delta_state_outputs,) = generate_transitions_dataset(
+    (
+        state_action_inputs,
+        delta_state_outputs,
+    ) = generate_transitions_dataset(
         num_states=num_states,
         num_actions=num_actions,
         env=env,

subtrees/simenvs/simenvs/velocity_controlled_point_mass/scenario-10/env_config.toml

@@ -3,7 +3,7 @@ gating_bitmap = "./bitmaps/gating_mask.bmp"  # filename for gating bitmap
 # gating_bitmap = "../scenario-1/gating_mask.bmp"  # filename for gating bitmap
 
 # lists are converted to 1D np.arrays and floats/ints are broadcast
-# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state) 
+# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state)
 # state constraints
 min_state = [-3.0, -3.0]
 # min_state = [-3.0, -3.0]

subtrees/simenvs/simenvs/velocity_controlled_point_mass/scenario-2/env_config.toml

@@ -3,7 +3,7 @@ gating_bitmap = "./bitmaps/gating_mask.bmp"  # filename for gating bitmap
 # gating_bitmap = "../scenario-1/gating_mask.bmp"  # filename for gating bitmap
 
 # lists are converted to 1D np.arrays and floats/ints are broadcast
-# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state) 
+# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state)
 # state constraints
 min_state = [-3.0, -2.0]
 max_state = [3.0, 3.0]

subtrees/simenvs/simenvs/velocity_controlled_point_mass/scenario-3/env_config.toml

@@ -3,7 +3,7 @@ gating_bitmap = "./bitmaps/gating_mask.bmp"  # filename for gating bitmap
 # gating_bitmap = "../scenario-1/gating_mask.bmp"  # filename for gating bitmap
 
 # lists are converted to 1D np.arrays and floats/ints are broadcast
-# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state) 
+# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state)
 # state constraints
 min_state = [-3.0, -2.0]
 max_state = [3.0, 3.0]

subtrees/simenvs/simenvs/velocity_controlled_point_mass/scenario-4/env_config.toml

@@ -3,7 +3,7 @@ gating_bitmap = "./bitmaps/gating_mask.bmp"  # filename for gating bitmap
 # gating_bitmap = "../scenario-1/gating_mask.bmp"  # filename for gating bitmap
 
 # lists are converted to 1D np.arrays and floats/ints are broadcast
-# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state) 
+# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state)
 # state constraints
 min_state = [-3.0, -2.0]
 max_state = [3.0, 3.0]

subtrees/simenvs/simenvs/velocity_controlled_point_mass/scenario-5/env_config.toml

@@ -3,7 +3,7 @@ gating_bitmap = "./bitmaps/gating_mask.bmp"  # filename for gating bitmap
 # gating_bitmap = "../scenario-1/gating_mask.bmp"  # filename for gating bitmap
 
 # lists are converted to 1D np.arrays and floats/ints are broadcast
-# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state) 
+# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state)
 # state constraints
 min_state = [-3.1, -3.0]
 # min_state = [-3.0, -3.0]

subtrees/simenvs/simenvs/velocity_controlled_point_mass/scenario-6/env_config.toml

@@ -3,7 +3,7 @@ gating_bitmap = "./bitmaps/gating_mask.bmp"  # filename for gating bitmap
 # gating_bitmap = "../scenario-1/gating_mask.bmp"  # filename for gating bitmap
 
 # lists are converted to 1D np.arrays and floats/ints are broadcast
-# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state) 
+# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state)
 # state constraints
 min_state = [-3.0, -2.0]
 max_state = [3.0, 3.0]

subtrees/simenvs/simenvs/velocity_controlled_point_mass/scenario-7/env_config.toml

@@ -3,7 +3,7 @@ gating_bitmap = "./bitmaps/gating_mask.bmp"  # filename for gating bitmap
 # gating_bitmap = "../scenario-1/gating_mask.bmp"  # filename for gating bitmap
 
 # lists are converted to 1D np.arrays and floats/ints are broadcast
-# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state) 
+# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state)
 # state constraints
 min_state = [-3.0, -3.0]
 max_state = [3.0, 3.0]

subtrees/simenvs/simenvs/velocity_controlled_point_mass/scenario-8/env_config.toml

@@ -3,7 +3,7 @@ gating_bitmap = "./bitmaps/gating_mask.bmp"  # filename for gating bitmap
 # gating_bitmap = "../scenario-1/gating_mask.bmp"  # filename for gating bitmap
 
 # lists are converted to 1D np.arrays and floats/ints are broadcast
-# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state) 
+# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state)
 # state constraints
 min_state = [-3.0, -3.0]
 max_state = [3.0, 3.0]

subtrees/simenvs/simenvs/velocity_controlled_point_mass/scenario-9/env_config.toml

@@ -3,7 +3,7 @@ gating_bitmap = "./bitmaps/gating_mask.bmp"  # filename for gating bitmap
 # gating_bitmap = "../scenario-1/gating_mask.bmp"  # filename for gating bitmap
 
 # lists are converted to 1D np.arrays and floats/ints are broadcast
-# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state) 
+# e.g min_state = [-3.0, -2.0] is converted with np.array(min_state)
 # state constraints
 min_state = [-3.0, -3.0]
 max_state = [3.0, 3.0]