reformatting (apply black and isort)

Pyrado/pyrado/algorithms/episodic/predefined_lqr.py

@@ -102,7 +102,6 @@ def sampler(self, sampler: ParallelRolloutSampler):
         self._sampler = sampler
 
     def step(self, snapshot_mode: str, meta_info: dict = None):
-
         if isinstance(inner_env(self._env), BallOnPlate5DSim):
             ctrl_gains = to.tensor(
                 [

Pyrado/pyrado/algorithms/meta/adr.py

@@ -431,7 +431,6 @@ def __init__(
         logger: StepLogger = None,
         device: str = "cuda" if to.cuda.is_available() else "cpu",
     ):
-
         """
         Constructor
 
@@ -472,7 +471,6 @@ def get_reward(self, traj: StepSequence) -> to.Tensor:
     def train(
         self, reference_trajectory: StepSequence, randomized_trajectory: StepSequence, num_epoch: int
     ) -> to.Tensor:
-
         reference_batch_generator = reference_trajectory.iterate_rollouts()
         random_batch_generator = randomized_trajectory.iterate_rollouts()
 

Pyrado/pyrado/algorithms/regression/timeseries_prediction.py

@@ -104,7 +104,6 @@ def __init__(
             self._lr_scheduler = lr_scheduler(self.optim, **lr_scheduler_hparam)
 
     def step(self, snapshot_mode: str, meta_info: dict = None):
-
         # Feed one epoch of the training set to the policy
         if self.windowed:
             # Predict

Pyrado/pyrado/algorithms/step_based/dql.py

@@ -192,7 +192,6 @@ def update(self):
             file=sys.stdout,
             leave=False,
         ):
-
             # Sample steps and the associated next step from the replay memory
             steps, next_steps = self._memory.sample(self.batch_size)
             steps.torch(data_type=to.get_default_dtype())

Pyrado/pyrado/algorithms/step_based/gae.py

@@ -239,7 +239,6 @@ def update(self, rollouts: Sequence[StepSequence], use_empirical_returns: bool =
 
         # Iterate over all gathered samples num_epoch times
         for e in range(self.num_epoch):
-
             for batch in tqdm(
                 concat_ros.split_shuffled_batches(
                     self.batch_size, complete_rollouts=isinstance(self.vfcn, RecurrentPolicy)

Pyrado/pyrado/algorithms/step_based/ppo.py

@@ -176,7 +176,6 @@ def update(self, rollouts: Sequence[StepSequence]):
 
         # Iterations over the whole data set
         for e in range(self.num_epoch):
-
             for batch in tqdm(
                 concat_ros.split_shuffled_batches(self.batch_size, complete_rollouts=self._policy.is_recurrent),
                 total=num_iter_from_rollouts(None, concat_ros, self.batch_size),
@@ -412,7 +411,6 @@ def update(self, rollouts: Sequence[StepSequence]):
 
         # Iterations over the whole data set
         for e in range(self.num_epoch):
-
             for batch in tqdm(
                 concat_ros.split_shuffled_batches(
                     self.batch_size,

Pyrado/pyrado/environments/rcspysim/planar_insert.py

@@ -75,7 +75,6 @@ class PlanarInsertSim(RcsSim, Serializable):
     """
 
     def __init__(self, task_args: dict, collision_config: dict = None, max_dist_force: float = None, **kwargs):
-
         """
         Constructor
 

Pyrado/pyrado/policies/feed_forward/poly_time.py

@@ -251,9 +251,7 @@ class TraceablePolySplineTimePolicy(nn.Module):
     t_init: float
     t_curr: float
     overtime_behavior: str
-    act_space_shape: Tuple[
-        int,
-    ]
+    act_space_shape: Tuple[int,]
     act_space_flat_dim: int
 
     def __init__(

Pyrado/pyrado/sampling/parallel_rollout_sampler.py

@@ -276,7 +276,6 @@ def sample(
             disable=(not self.show_progress_bar),
             unit="steps" if self.min_steps is not None else "rollouts",
         ) as pb:
-
             if self.min_steps is None:
                 if init_states is None and domain_params is None:
                     # Simply run min_rollouts times

Pyrado/pyrado/sampling/sequences.py

@@ -99,6 +99,7 @@ def sequence_add_init(x_init, iter, dtype=int):
     :param dtype: data type to cast to (either int of float)
     :return: element at the given iteration and array of the whole sequence
     """
+
     # non-exponential growth
     def iter_function(x_seq, i, x_init):
         return x_seq[0, :] * (i + 1)
@@ -115,6 +116,7 @@ def sequence_rec_double(x_init, iter, dtype=int):
     :param dtype: data type to cast to (either int of float)
     :return: element at the given iteration and array of the whole sequence
     """
+
     # exponential growth
     def iter_function(x_seq, i, x_init):
         return x_seq[i - 1, :] * 2.0
@@ -131,6 +133,7 @@ def sequence_sqrt(x_init, iter, dtype=int):
     :param dtype: data type to cast to (either int of float)
     :return: element at the given iteration and array of the whole sequence
     """
+
     # non-exponential growth
     def iter_function(x_seq, i, x_init):
         return x_seq[0, :] * np.sqrt(i + 1)  # i+1 because sqrt(1) = 1
@@ -147,6 +150,7 @@ def sequence_rec_sqrt(x_init, iter, dtype=int):
     :param dtype: data type to cast to (either int of float)
     :return: element at the given iteration and array of the whole sequence
     """
+
     # exponential growth
     def iter_function(x_seq, i, x_init):
         return x_seq[i - 1, :] * np.sqrt(i + 1)  # i+1 because sqrt(1) = 1
@@ -163,6 +167,7 @@ def sequence_nlog2(x_init, iter, dtype=int):
     :param dtype: data type to cast to (either int of float)
     :return: element at the given iteration and array of the whole sequence
     """
+
     # non-exponential growth
     def iter_function(x_seq, i, x_init):
         return x_seq[0, :] * i * np.log2(i + 2)  # i+2 because log2(1) = 0 and log2(2) = 1

Pyrado/scripts/evaluation/paper_specific/sob_illustrative_example.py

@@ -139,7 +139,6 @@ def check_E_n_Jhat(th_n_opt, n):
     b_Jhat_n_hist = np.empty((num_samples, num_iter))
 
     for s in range(num_samples):
-
         for n in range(1, num_iter + 1):
             n_V = np.random.binomial(n, psi)  # perform n Bernoulli trials
             n_M = n - n_V

Pyrado/scripts/hyperparam_optimization/hopt_qq-su_ppo2.py

@@ -138,7 +138,6 @@ def train_and_eval(trial: optuna.Trial, study_dir: str, seed: int):
 
 
 if __name__ == "__main__":
-
     # Parse command line arguments
     args = get_argparser().parse_args()
 

setup_deps.py

@@ -314,7 +314,6 @@ def members(ml):
             with tarfile.open(tf.name) as tar:
 
                 def is_within_directory(directory, target):
-
                     abs_directory = os.path.abspath(directory)
                     abs_target = os.path.abspath(target)
 
@@ -323,7 +322,6 @@ def is_within_directory(directory, target):
                     return prefix == abs_directory
 
                 def safe_extract(tar, path=".", members=None, *, numeric_owner=False):
-
                     for member in tar.getmembers():
                         member_path = os.path.join(path, member.name)
                         if not is_within_directory(path, member_path):