chore: Update dependencies and pyright fixes

- Update networkx
- Update pytest
- Update setuptools
- Update pyright
- Update plotly

chore: Remove networkx from createstubs.sh

chore: Update progressbar2 dependency to version 4.4.2

chore: Update wheel dependency to version 0.44.0

chore: Update pip dependency to version 24.2

chore: Update prompt-toolkit dependency to version 3.0.47

Refactor CyberBattleEnv class to use np_random consistently

- Remove trailing whitespace in notebook scripts
- Update dependencies: prompt-toolkit, pip, wheel, progressbar2
- Refactor CyberBattleEnv class to use np_random consistently
- Remove networkx from createstubs.sh
- Update dependencies: networkx, pytest, setuptools, pyright, plotly

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cyberbattle/_env/cyberbattle_env.py

@@ -1317,7 +1317,7 @@ def get_explored_network_node_properties_bitmap_as_numpy(
             ]
         )
 
-    def step(self, action: Action) -> Tuple[Observation, float, bool, bool, StepInfo]:
+    def step(self, action: Action) -> Tuple[Observation, float, bool, bool, StepInfo]:  # type: ignore
         if self.__done:
             raise RuntimeError("new episode must be started with env.reset()")
 

cyberbattle/_env/discriminatedunion.py

@@ -14,7 +14,7 @@
 T_cov = TypeVar("T_cov", covariant=True)
 
 
-class DiscriminatedUnion(spaces.Dict, Generic[T_cov]):  # type: ignore
+class DiscriminatedUnion(spaces.Dict, Generic[T_cov]):
     """
     A discriminated union of simpler spaces.
 
@@ -23,6 +23,9 @@ class DiscriminatedUnion(spaces.Dict, Generic[T_cov]):  # type: ignore
     self.observation_space = discriminatedunion.DiscriminatedUnion(
         {"foo": spaces.Discrete(2), "Bar": spaces.Discrete(3)})
 
+    Generic type T_cov is the type of the contained discriminated values.
+    It should be defined as  a typed dictionary, e.g.:  TypedDict('Choices', {'foo': int, 'Bar': int})
+
     """
 
     def __init__(
@@ -47,7 +50,7 @@ def __init__(
     def seed(self, seed: Union[dict, None, int] = None):
         return super().seed(seed)
 
-    def sample(self, mask=None) -> T_cov:  # dict[str, object]:
+    def sample(self, mask=None) -> T_cov:   # type: ignore
         space_count = len(self.spaces.items())
         index_k = self.union_np_random.integers(0, space_count)
         kth_key, kth_space = list(self.spaces.items())[index_k]

cyberbattle/agents/baseline/agent_wrapper.py

@@ -25,7 +25,7 @@ def on_step(
         self,
         action: cyberbattle_env.Action,
         reward: float,
-        truncated,
+        truncated: bool,
         done: bool,
         observation: cyberbattle_env.Observation,
     ):
@@ -332,14 +332,15 @@ def __init__(self, p: EnvironmentBounds, feature_selection: List[Feature]):
         assert np.shape(self.ravelled_size) == (), f"! {np.shape(self.ravelled_size)}"
         super().__init__(p, [self.ravelled_size])
 
-    def vector_to_index(self, feature_vector):
+    def vector_to_index(self, feature_vector) -> int:
         assert len(self.dim_sizes) == len(feature_vector), (
             f"feature vector of size {len(feature_vector)}, "
             f"expecting {len(self.dim_sizes)}: {feature_vector} -- {self.dim_sizes}"
         )
-        index: np.int32 = np.ravel_multi_index(
+        index_intp = np.ravel_multi_index(
             list(feature_vector), list(self.dim_sizes)
         )
+        index = index_intp.item()
         assert index < self.ravelled_size, (
             f"feature vector out of bound ({feature_vector}, dim={self.dim_sizes}) "
             f"-> index={index}, max_index={self.ravelled_size-1})"