Merge pull request #37 from APLA-Toolbox/improve-devops

revert changes

Author: guilyx

.github/workflows/format.yml

@@ -1,7 +1,7 @@
 name: format
 on:
-  push:
-    branches: [master]
+  pull_request:
+    branches: [main]
 jobs:
   format:
     runs-on: ubuntu-latest
@@ -24,5 +24,5 @@ jobs:
       - name: Commit changes
         uses: stefanzweifel/git-auto-commit-action@v4.1.2
         with:
-          commit_message: 🎉 Apply formatting changes
-          branch: master
+          commit_message: Apply formatting changes
+          branch: ${{ github.head_ref }}

src/automated_planner.py

@@ -30,25 +30,29 @@ def __init__(self, domain_path, problem_path):
     """
     Transition from one state to the next using an action
     """
+
     def transition(self, state, action):
         return self.pddl.transition(self.domain, state, action, check=False)
 
     """
     Returns all available actions from the given state 
     """
+
     def available_actions(self, state):
         return self.pddl.available(state, self.domain)
 
     """ 
     Check if a vector of terms is satisfied by the given state
     """
+
     def satisfies(self, asserted_state, state):
         return self.pddl.satisfy(asserted_state, state, self.domain)[0]
 
     """
     Check if the term is satisfied by the state
     To do: compare if it's faster to compute the check on a vector of terms in julia or python 
     """
+
     def state_has_term(self, state, term):
         if self.pddl.has_term_in_state(self.domain, state, term):
             return True
@@ -59,12 +63,14 @@ def state_has_term(self, state, term):
     Flatten the goal to a vector of terms
     To do: check if we can iterate over the jl vector 
     """
+
     def __flatten_goal(self):
         return self.pddl.flatten_goal(self.problem)
 
     """
     Retrieves the linked list path 
     """
+
     def __retrace_path(self, node):
         if not node:
             return []
@@ -78,6 +84,7 @@ def __retrace_path(self, node):
     """
     Returns all the actions operated to reach the goal 
     """
+
     def get_actions_from_path(self, path):
         if not path:
             logging.warning("Path is empty, can't operate...")
@@ -95,6 +102,7 @@ def get_actions_from_path(self, path):
     """
     Returns all the states that should be opened from start to goal 
     """
+
     def get_state_def_from_path(self, path):
         if not path:
             logging.warning("Path is empty, can't operate...")
@@ -107,6 +115,7 @@ def get_state_def_from_path(self, path):
     """
     Runs the BFS algorithm on the loaded domain/problem 
     """
+
     def breadth_first_search(self, time_it=False):
         if time_it:
             start_time = now()
@@ -123,6 +132,7 @@ def breadth_first_search(self, time_it=False):
     """
     Runs the DFS algorithm on the domain/problem 
     """
+
     def depth_first_search(self, time_it=False):
         if time_it:
             start_time = now()
@@ -139,6 +149,7 @@ def depth_first_search(self, time_it=False):
     """
     Runs the Dijkstra algorithm on the domain/problem 
     """
+
     def dijktra_best_first_search(self, time_it=False):
         if time_it:
             start_time = now()

src/heuristics.py

@@ -1,6 +1,7 @@
 def zero_heuristic(state, automated_planner):
     return 0
 
+
 def goal_count_heuristic(state, automated_planner):
     count = 0
     for goal in automated_planner.goals:

tests/test_automated_planner.py

@@ -17,17 +17,20 @@ def test_available_actions():
     actions = apla.available_actions(apla.initial_state)
     assert len(actions) > 0
 
+
 def test_execute_action():
     apla = AutomatedPlanner("data/domain.pddl", "data/problem.pddl")
     actions = apla.available_actions(apla.initial_state)
     new_state = apla.transition(apla.initial_state, actions[0])
     assert str(new_state) != str(apla.initial_state)
 
+
 def test_state_has_term():
     apla = AutomatedPlanner("data/domain.pddl", "data/problem.pddl")
     is_goal = apla.state_has_term(apla.initial_state, apla.goals[0])
     assert is_goal == False
 
+
 def test_state_assertion():
     apla = AutomatedPlanner("data/domain.pddl", "data/problem.pddl")
     assert apla.satisfies(apla.problem.goal, apla.initial_state) == False

tests/test_heuristics.py

@@ -13,11 +13,13 @@
     - Run search algorithms and test value of h when at goal
 """
 
+
 def test_zero_heuristic():
     apla = AutomatedPlanner("data/domain.pddl", "data/problem.pddl")
     heuristic = hs.zero_heuristic(apla.initial_state, apla)
     assert heuristic == 0
 
+
 def test_goal_count_heuristic():
     apla = AutomatedPlanner("data/domain.pddl", "data/problem.pddl")
     heuristic = hs.goal_count_heuristic(apla.initial_state, apla)