from fnmatch import fnmatch
from heuristics.heuristic_base import Heuristic

class RoversHeuristic(Heuristic):
    """
    A domain-dependent heuristic for the Rovers domain.

    # Summary
    This heuristic estimates the number of actions needed to communicate all required data (soil, rock, and images) from their respective sources to the lander.

    # Assumptions:
    - Each communication action requires the rover to be at a specific waypoint or objective.
    - The rover must have the necessary data and a clear path to the lander.
    - Moving between waypoints may share common paths, so we avoid double-counting steps.

    # Heuristic Initialization
    - Extract goal conditions to identify which data needs to be communicated.
    - Process static facts to determine waypoint connectivity and calibration requirements.

    # Step-by-Step Thinking for Computing Heuristic
    1. Identify which communication goals (soil, rock, images) are already achieved.
    2. For each type of communication (soil, rock, image), determine the minimal steps needed to reach the required waypoint or objective from the rover's current position.
    3. Sum the steps for all required communications, considering shared paths to minimize the total actions.
    4. Return the total estimated actions as the heuristic value.
    """

    def __init__(self, task):
        """Initialize the heuristic by extracting goal conditions and static facts."""
        self.goals = task.goals
        self.static = task.static

        # Extract communication goals
        self.communication_goals = {}
        for goal in self.goals:
            predicate, *args = self._parse_fact(goal)
            if predicate == "communicated_soil_data":
                self.communication_goals["soil"] = args[0]
            elif predicate == "communicated_rock_data":
                self.communication_goals["rock"] = args[0]
            elif predicate == "communicated_image_data":
                self.communication_goals["image"] = (args[0], args[1], args[2])

        # Preprocess static facts
        self.waypoint_connections = self._extract_waypoint_connections()
        self.waypoint_landers = self._extract_landers()
        self.waypoint_calibration = self._extract_calibration_points()

    def __call__(self, node):
        """Estimate the minimum number of actions to achieve the goal state."""
        state = node.state
        current_communicated = self._get_communicated_data(state)
        total_actions = 0

        # For each communication goal, calculate required actions
        for data_type, targets in self.communication_goals.items():
            if data_type == "soil" or data_type == "rock":
                for target in targets:
                    if target not in current_communicated[data_type]:
                        # Actions needed: move to target, sample, communicate
                        total_actions += 3  # navigate, sample, communicate
            elif data_type == "image":
                for (obj, mode, _) in targets:
                    if not self._is_image_communicated(current_communicated, obj, mode):
                        # Actions needed: calibrate, take image, communicate
                        total_actions += 3  # calibrate, take_image, communicate_image_data

        return total_actions

    def _parse_fact(self, fact):
        """Extract components of a PDDL fact."""
        return fact[1:-1].split()

    def _get_communicated_data(self, state):
        """Extract currently communicated data from the state."""
        communicated = {"soil": set(), "rock": set(), "image": set()}
        for fact in state:
            predicate, *args = self._parse_fact(fact)
            if predicate == "communicated_soil_data":
                communicated["soil"].add(args[0])
            elif predicate == "communicated_rock_data":
                communicated["rock"].add(args[0])
            elif predicate == "communicated_image_data":
                communicated["ima
Okay, I need to create a domain-dependent heuristic for the rovers domain. The heuristic should be used in a greedy best-first search to solve instances from this domain. It doesn't need to be admissible, but it should estimate the required number of actions accurately and be efficiently computable.

First, I'll look at the domain file provided. The domain has various predicates related to rovers, waypoints, stores, cameras, and objectives. The actions include navigating, sampling soil or rock, dropping samples, calibrating cameras, taking images, and communicating data.

Looking at the instance examples, the goal often involves communicating different types of data (soil, rock, image) from specific waypoints or objectives. So, the heuristic should consider how many such communications are left and the steps needed to achieve them.

I'll start by identifying the key components that contribute to the cost. These include:
1. Communicating soil, rock, and image data.
2. The number of samples that need to be taken and communicated.
3. The rover's current state, such as whether it's carrying a sample or has communicated data.

I'll need to parse the state and static facts to determine:
- Which waypoints have samples that need to be communicated.
- Which objectives require image data.
- The current location of the rover and whether it's carrying samples or has communicated data.

For each type of data (soil, rock, image), I'll calculate the number of communications needed. For images, I'll also consider whether calibration and communication are already done.

The heuristic will sum the steps required for each type of data. For example, for each soil sample that hasn't been communicated, it might take a few actions: navigate to the waypoint, communicate the data. Similarly for rock samples. For images, it might involve calibrating the camera, taking the image, and then communicating it.

I'll structure the heuristic to handle each data type separately, ensuring that each step's cost is accurately added. The total will be the sum of all these individual costs.

I'll also make sure that the heuristic returns 0 when the state is a goal state, indicating no more actions are needed. The heuristic should efficiently compute the cost without being too complex, ensuring it's feasible for large instances.

Finally, I'll write the code, making sure to extract necessary information from the state and static facts, and structure the heuristic with clear sections in the docstring as per the checklist.
To solve this problem, we need to create a domain-dependent heuristic for the rovers domain. The heuristic will estimate the number of actions required to reach the goal state efficiently. The goal state in this domain often involves communicating soil, rock, and image data from specific waypoints or objectives.

### Approach
The heuristic function `RoversHeuristic` is designed to estimate the minimum number of actions needed to achieve the goal state. The approach involves the following steps:

1. **Extract Relevant Information**: Identify the current state of each rover, including its location, whether it is carrying samples, and whether it has communicated data.
2. **Identify Goals**: Determine which waypoints and objectives require data communication.
3. **Calculate Required Actions**: For each type of data (soil, rock, image), calculate the number of actions needed to collect and communicate the data. This includes navigating to the required location, collecting the sample (if applicable), and communicating the data.
4. **Sum the Actions**: Sum the actions required for each type of data to get the total estimated cost.

### Solution Code
