apply pre-commits

src/primaite/environment/primaite_env.py

@@ -14,8 +14,7 @@ from gym import Env, spaces
 from matplotlib import pyplot as plt
 
 from primaite.acl.access_control_list import AccessControlList
-from primaite.agents.utils import is_valid_acl_action_extra, \
+from primaite.agents.utils import is_valid_acl_action_extra, is_valid_node_action
-    is_valid_node_action
 from primaite.common.custom_typing import NodeUnion
 from primaite.common.enums import (
     ActionType,
@@ -24,8 +23,9 @@ from primaite.common.enums import (
     NodePOLInitiator,
     NodePOLType,
     NodeType,
+    ObservationType,
     Priority,
-    SoftwareState, ObservationType,
+    SoftwareState,
 )
 from primaite.common.service import Service
 from primaite.config import training_config
@@ -35,15 +35,13 @@ from primaite.environment.reward import calculate_reward_function
 from primaite.links.link import Link
 from primaite.nodes.active_node import ActiveNode
 from primaite.nodes.node import Node
-from primaite.nodes.node_state_instruction_green import \
+from primaite.nodes.node_state_instruction_green import NodeStateInstructionGreen
-    NodeStateInstructionGreen
 from primaite.nodes.node_state_instruction_red import NodeStateInstructionRed
 from primaite.nodes.passive_node import PassiveNode
 from primaite.nodes.service_node import ServiceNode
 from primaite.pol.green_pol import apply_iers, apply_node_pol
 from primaite.pol.ier import IER
-from primaite.pol.red_agent_pol import apply_red_agent_iers, \
+from primaite.pol.red_agent_pol import apply_red_agent_iers, apply_red_agent_node_pol
-    apply_red_agent_node_pol
 from primaite.transactions.transaction import Transaction
 
 _LOGGER = logging.getLogger(__name__)
@@ -178,7 +176,6 @@ class Primaite(Env):
         # It will be initialised later.
         self.obs_handler: ObservationsHandler
 
-
         # Open the config file and build the environment laydown
         with open(self._lay_down_config_path, "r") as file:
             # Open the config file and build the environment laydown
@@ -200,7 +197,7 @@ class Primaite(Env):
         try:
             plt.tight_layout()
             nx.draw_networkx(self.network, with_labels=True)
-            now = datetime.now()  # current date and time
+            # now = datetime.now()  # current date and time
 
             file_path = session_path / f"network_{timestamp_str}.png"
             plt.savefig(file_path, format="PNG")
@@ -239,7 +236,9 @@ class Primaite(Env):
             self.action_dict = self.create_node_and_acl_action_dict()
             self.action_space = spaces.Discrete(len(self.action_dict))
         else:
-            _LOGGER.info(f"Invalid action type selected: {self.training_config.action_type}")
+            _LOGGER.info(
+                f"Invalid action type selected: {self.training_config.action_type}"
+            )
         # Set up a csv to store the results of the training
         try:
             header = ["Episode", "Average Reward"]
@@ -384,7 +383,7 @@ class Primaite(Env):
             self.step_count,
             self.training_config,
         )
-        #print(f"    Step {self.step_count} Reward: {str(reward)}")
+        # print(f"    Step {self.step_count} Reward: {str(reward)}")
         self.total_reward += reward
         if self.step_count == self.episode_steps:
             self.average_reward = self.total_reward / self.step_count
@@ -1049,7 +1048,6 @@ class Primaite(Env):
         """
         self.observation_type = ObservationType[observation_info["type"]]
 
-
     def get_action_info(self, action_info):
         """
         Extracts action_info.

src/primaite/environment/reward.py

@@ -2,11 +2,11 @@
 """Implements reward function."""
 from typing import Dict
 
+from primaite import getLogger
 from primaite.common.enums import FileSystemState, HardwareState, SoftwareState
 from primaite.common.service import Service
 from primaite.nodes.active_node import ActiveNode
 from primaite.nodes.service_node import ServiceNode
-from primaite import getLogger
 
 _LOGGER = getLogger(__name__)
 
@@ -80,7 +80,13 @@ def calculate_reward_function(
         if step_count >= start_step and step_count <= stop_step:
             if not ier_value.get_is_running() and ref_ier.get_is_running():
                 # what should happen if reference IER is blocked but live IER is running?
-                _LOGGER.debug(f"Applying penalty of {config_values.green_ier_blocked * ier_value.get_mission_criticality()} due to IER {ier_key} being blocked")
+                _LOGGER.debug(
+                    (
+                        f"Applying penalty of "
+                        f"{config_values.green_ier_blocked * ier_value.get_mission_criticality()} "
+                        f"due to IER {ier_key} being blocked"
+                    )
+                )
                 reward_value += (
                     config_values.green_ier_blocked
                     * ier_value.get_mission_criticality()