Updates

Things/Ant_Colony/agents/nestmate.py

@@ -1,17 +1,13 @@
 """
 Nestmate Agent Implementation
 
-This module implements the Nestmate agent class, which represents an individual ant
-in the colony using the Free Energy Principle (FEP) and Active Inference framework.
+This module implements a simplified version of the Nestmate agent class,
+representing an individual ant in the colony.
 """
 
 import numpy as np
-from typing import Dict, List, Tuple, Optional
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from dataclasses import dataclass
 from enum import Enum
+from dataclasses import dataclass
 
 class TaskType(Enum):
     """Possible task types for a Nestmate agent."""
@@ -22,314 +18,149 @@ class TaskType(Enum):
     EXPLORATION = "exploration"
 
 @dataclass
-class Observation:
-    """Container for sensory observations."""
-    pheromone: np.ndarray  # Pheromone gradients
-    food: np.ndarray       # Food source locations
-    nestmates: np.ndarray  # Other agent positions
-    obstacles: np.ndarray  # Obstacle positions
-    nest: np.ndarray      # Nest location/gradient
-
-class GenerativeModel(nn.Module):
-    """Hierarchical generative model for active inference."""
-    
-    def __init__(self, config: dict):
-        super().__init__()
-        
-        # Model dimensions
-        self.obs_dim = config['dimensions']['observations']
-        self.state_dim = config['dimensions']['states']
-        self.action_dim = config['dimensions']['actions']
-        self.temporal_horizon = config['dimensions']['planning_horizon']
-        
-        # Hierarchical layers
-        self.layers = nn.ModuleList([
-            nn.Linear(self.state_dim, self.state_dim) 
-            for _ in range(config['active_inference']['model']['hierarchical_levels'])
-        ])
-        
-        # State transition model (dynamics)
-        self.transition = nn.Sequential(
-            nn.Linear(self.state_dim + self.action_dim, self.state_dim * 2),
-            nn.ReLU(),
-            nn.Linear(self.state_dim * 2, self.state_dim)
-        )
-        
-        # Observation model
-        self.observation = nn.Sequential(
-            nn.Linear(self.state_dim, self.obs_dim * 2),
-            nn.ReLU(),
-            nn.Linear(self.obs_dim * 2, self.obs_dim)
-        )
-        
-        # Policy network
-        self.policy = nn.Sequential(
-            nn.Linear(self.state_dim, self.action_dim * 2),
-            nn.ReLU(),
-            nn.Linear(self.action_dim * 2, self.action_dim)
-        )
-        
-        # Precision parameters
-        self.alpha = nn.Parameter(torch.ones(1))  # Precision of beliefs
-        self.beta = nn.Parameter(torch.ones(1))   # Precision of policies
-        
-    def forward(self, 
-                state: torch.Tensor, 
-                action: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Forward pass through the generative model."""
-        
-        # Hierarchical state processing
-        for layer in self.layers:
-            state = F.relu(layer(state))
-            
-        # Generate observations
-        predicted_obs = self.observation(state)
-        
-        # If action provided, predict next state
-        if action is not None:
-            state_action = torch.cat([state, action], dim=-1)
-            next_state = self.transition(state_action)
-            return predicted_obs, next_state
-            
-        return predicted_obs, None
-    
-    def infer_state(self, 
-                   obs: torch.Tensor, 
-                   prev_state: Optional[torch.Tensor] = None,
-                   n_steps: int = 10) -> torch.Tensor:
-        """Infer hidden state through iterative message passing."""
-        
-        if prev_state is None:
-            state = torch.zeros(obs.shape[0], self.state_dim)
-        else:
-            state = prev_state
-            
-        state.requires_grad = True
-        optimizer = torch.optim.Adam([state], lr=0.1)
-        
-        for _ in range(n_steps):
-            optimizer.zero_grad()
-            
-            # Prediction errors
-            pred_obs, _ = self.forward(state)
-            obs_error = F.mse_loss(pred_obs, obs)
-            
-            if prev_state is not None:
-                state_error = F.mse_loss(state, prev_state)
-                loss = obs_error + self.alpha * state_error
-            else:
-                loss = obs_error
-                
-            loss.backward()
-            optimizer.step()
-            
-        return state.detach()
-    
-    def select_action(self, 
-                     state: torch.Tensor, 
-                     temperature: float = 1.0) -> torch.Tensor:
-        """Select action using active inference."""
-        
-        # Get action distribution
-        action_logits = self.policy(state)
-        action_probs = F.softmax(action_logits / temperature, dim=-1)
-        
-        # Sample action
-        action = torch.multinomial(action_probs, 1)
-        
-        return action
+class Position:
+    """2D position with orientation."""
+    x: float
+    y: float
+    theta: float = 0.0
 
 class Nestmate:
-    """
-    Individual ant agent implementing active inference for decision making.
-    """
+    """Individual ant agent with basic behaviors."""
     
     def __init__(self, config: dict):
         """Initialize Nestmate agent."""
         self.config = config
         
         # Physical state
-        self.position = np.zeros(2)
+        self.position = Position(0.0, 0.0, 0.0)
         self.velocity = np.zeros(2)
-        self.orientation = 0.0
         self.energy = config['physical']['energy']['initial']
         
         # Task state
         self.current_task = TaskType.EXPLORATION
         self.carrying = None
         
-        # Sensory state
-        self.observations = Observation(
-            pheromone=np.zeros(config['sensors']['pheromone']['types'].__len__()),
-            food=np.zeros(2),
-            nestmates=np.zeros(2),
-            obstacles=np.zeros(2),
-            nest=np.zeros(2)
-        )
+        # Sensors
+        self.sensor_range = config['physical']['sensor_range']
         
-        # Active inference components
-        self.generative_model = GenerativeModel(config)
-        self.current_state = None
-        self.previous_action = None
+        # Movement parameters
+        self.max_speed = config['physical']['max_speed']
+        self.turn_rate = config['physical']['turn_rate']
         
-        # Memory
-        self.memory = {
-            'spatial': [],
-            'temporal': [],
-            'social': []
+    def sense(self, world_state: dict) -> dict:
+        """Process sensory inputs from environment."""
+        # Get nearby entities within sensor range
+        nearby = {
+            'food': [],
+            'nestmates': [],
+            'obstacles': [],
+            'pheromones': {}
         }
         
-        # Learning parameters
-        self.learning_rate = config['learning']['parameters']['learning_rate']
-        self.exploration_rate = config['learning']['parameters']['exploration_rate']
+        # Process food sources
+        for food in world_state['resources']:
+            dist = self._distance_to(food.position)
+            if dist <= self.sensor_range:
+                nearby['food'].append((food, dist))
         
-    def update(self, observation: Observation) -> np.ndarray:
-        """
-        Update agent state and select action using active inference.
+        # Process other agents
+        for agent in world_state['agents']:
+            if agent != self:
+                dist = self._distance_to(agent.position)
+                if dist <= self.sensor_range:
+                    nearby['nestmates'].append((agent, dist))
         
-        Args:
-            observation: Current sensory observations
+        # Process pheromones
+        for p_type, value in world_state['pheromones'].items():
+            nearby['pheromones'][p_type] = value
             
-        Returns:
-            action: Selected action as numpy array
-        """
-        # Convert observation to tensor
-        obs_tensor = torch.tensor(self._preprocess_observation(observation))
+        return nearby
         
-        # State inference
-        inferred_state = self.generative_model.infer_state(
-            obs_tensor, 
-            prev_state=self.current_state
-        )
-        self.current_state = inferred_state
+    def decide_action(self, sensed: dict) -> tuple:
+        """Decide next action based on current state and sensory input."""
+        # Default behavior: random walk
+        speed = self.max_speed
+        turn = np.random.uniform(-self.turn_rate, self.turn_rate)
         
-        # Action selection
-        action = self.generative_model.select_action(
-            inferred_state,
-            temperature=self.config['active_inference']['free_energy']['temperature']
-        )
-        
-        # Update memory
-        self._update_memory(observation, action)
-        
-        # Update internal state
-        self._update_internal_state()
-        
-        return action.numpy()
-    
-    def _preprocess_observation(self, observation: Observation) -> np.ndarray:
-        """Preprocess raw observations into model input format."""
-        # Combine all observations into single vector
-        obs_vector = np.concatenate([
-            observation.pheromone,
-            observation.food,
-            observation.nestmates,
-            observation.obstacles,
-            observation.nest
-        ])
-        
-        # Normalize
-        obs_vector = (obs_vector - obs_vector.mean()) / (obs_vector.std() + 1e-8)
-        
-        return obs_vector
-    
-    def _update_memory(self, observation: Observation, action: torch.Tensor):
-        """Update agent's memory systems."""
-        # Spatial memory
-        self.memory['spatial'].append({
-            'position': self.position.copy(),
-            'observation': observation,
-            'timestamp': None  # Add actual timestamp in implementation
-        })
-        
-        # Temporal memory
-        self.memory['temporal'].append({
-            'state': self.current_state.detach().numpy(),
-            'action': action.numpy(),
-            'reward': self._compute_reward(observation)
-        })
-        
-        # Social memory (interactions with other agents)
-        if np.any(observation.nestmates):
-            self.memory['social'].append({
-                'nestmate_positions': observation.nestmates.copy(),
-                'interaction_type': self._classify_interaction(observation)
-            })
-            
-        # Maintain memory size limits
-        for memory_type in self.memory:
-            if len(self.memory[memory_type]) > self.config['memory'][memory_type]['capacity']:
-                self.memory[memory_type].pop(0)
-                
-    def _update_internal_state(self):
-        """Update agent's internal state variables."""
-        # Update energy
-        self.energy -= self.config['physical']['energy']['consumption_rate']
-        if self.carrying is not None:
-            self.energy -= self.config['physical']['energy']['consumption_rate'] * 2
-            
-        # Update task if needed
-        if self._should_switch_task():
-            self._switch_task()
-            
-        # Update learning parameters
-        self.exploration_rate *= self.config['learning']['parameters']['decay_rate']
-        self.exploration_rate = max(
-            self.exploration_rate,
-            self.config['learning']['parameters']['min_exploration']
-        )
-        
-    def _compute_reward(self, observation: Observation) -> float:
-        """Compute reward signal from current observation."""
-        reward = 0.0
-        
-        # Task-specific rewards
+        # Task-specific behaviors
         if self.current_task == TaskType.FORAGING:
-            reward += np.sum(observation.food) * self.config['active_inference']['preferences']['food_weight']
-            
-        # Distance to nest reward
-        nest_distance = np.linalg.norm(observation.nest)
-        reward -= nest_distance * self.config['active_inference']['preferences']['home_weight']
+            # If carrying food, head back to nest
+            if self.carrying:
+                turn = self._angle_to_nest()
+            # Otherwise, follow food pheromones or explore
+            elif 'food' in sensed['pheromones']:
+                pheromone_gradient = sensed['pheromones']['food']
+                if np.any(pheromone_gradient > 0):
+                    turn = self._follow_gradient(pheromone_gradient)
+                    
+        elif self.current_task == TaskType.EXPLORATION:
+            # Random walk with longer persistence
+            if np.random.random() < 0.1:  # 10% chance to change direction
+                turn = np.random.uniform(-np.pi, np.pi)
+                
+        return speed, turn
         
-        # Safety reward (avoiding obstacles)
-        obstacle_penalty = np.sum(1.0 / (1.0 + np.linalg.norm(observation.obstacles, axis=1)))
-        reward -= obstacle_penalty * self.config['active_inference']['preferences']['safety_weight']
+    def update(self, dt: float, world_state: dict):
+        """Update agent state."""
+        # Sense environment
+        sensed = self.sense(world_state)
         
-        # Social reward
-        if np.any(observation.nestmates):
-            social_reward = self.config['active_inference']['preferences']['social_weight']
-            reward += social_reward
+        # Decide action
+        speed, turn = self.decide_action(sensed)
+        
+        # Update position and orientation
+        self.position.theta += turn * dt
+        self.position.theta = self.position.theta % (2 * np.pi)
+        
+        dx = speed * np.cos(self.position.theta) * dt
+        dy = speed * np.sin(self.position.theta) * dt
+        
+        self.position.x += dx
+        self.position.y += dy
+        
+        # Update energy
+        self.energy -= self.config['physical']['energy']['consumption_rate'] * dt
+        if self.carrying:
+            self.energy -= self.config['physical']['energy']['consumption_rate'] * dt
             
-        return reward
-    
-    def _should_switch_task(self) -> bool:
-        """Determine if agent should switch its current task."""
-        # Energy-based switching
+        # Consider task switching
+        self._consider_task_switch(sensed)
+        
+    def _distance_to(self, other_pos: Position) -> float:
+        """Calculate distance to another position."""
+        dx = other_pos.x - self.position.x
+        dy = other_pos.y - self.position.y
+        return np.sqrt(dx*dx + dy*dy)
+        
+    def _angle_to_nest(self) -> float:
+        """Calculate turn angle towards nest."""
+        # Simplified: assume nest is at (0,0)
+        dx = -self.position.x
+        dy = -self.position.y
+        target_angle = np.arctan2(dy, dx)
+        current_angle = self.position.theta
+        
+        # Calculate shortest turn
+        diff = target_angle - current_angle
+        while diff > np.pi:
+            diff -= 2*np.pi
+        while diff < -np.pi:
+            diff += 2*np.pi
+            
+        return np.clip(diff, -self.turn_rate, self.turn_rate)
+        
+    def _follow_gradient(self, gradient: np.ndarray) -> float:
+        """Calculate turn angle to follow a pheromone gradient."""
+        # Simplified: assume gradient gives us desired direction
+        target_angle = np.arctan2(gradient[1], gradient[0])
+        return self._angle_to_nest()  # Reuse angle calculation
+        
+    def _consider_task_switch(self, sensed: dict):
+        """Consider switching current task."""
+        # Simple task switching based on energy and random chance
         if self.energy < self.config['physical']['energy']['critical_level']:
-            return True
-            
-        # Random switching based on flexibility
-        if np.random.random() < self.config['behavior']['task_switching']['flexibility']:
-            return True
-            
-        return False
-    
-    def _switch_task(self):
-        """Switch to a new task based on current conditions."""
-        # Get valid task options
-        valid_tasks = list(TaskType)
-        if self.current_task in valid_tasks:
-            valid_tasks.remove(self.current_task)
-            
-        # Select new task (can be made more sophisticated)
-        self.current_task = np.random.choice(valid_tasks)
-        
-    def _classify_interaction(self, observation: Observation) -> str:
-        """Classify type of interaction with nearby nestmates."""
-        # Simple distance-based classification
-        distances = np.linalg.norm(observation.nestmates, axis=1)
-        if np.any(distances < 1.0):
-            return "direct"
-        elif np.any(distances < 3.0):
-            return "indirect"
-        return "none" 
+            self.current_task = TaskType.FORAGING
+        elif np.random.random() < 0.001:  # 0.1% chance to switch tasks
+            available_tasks = list(TaskType)
+            available_tasks.remove(self.current_task)
+            self.current_task = np.random.choice(available_tasks) 

Things/Ant_Colony/ant_colony/__init__.py

@@ -0,0 +1,8 @@
+"""
+Ant Colony Simulation Package
+
+A multi-agent simulation of an ant colony using simplified behaviors
+and visualizations.
+"""
+
+__version__ = "1.0.0" 

Things/Ant_Colony/ant_colony/agents/__init__.py

@@ -0,0 +1,5 @@
+"""Agents package for ant colony simulation."""
+
+from .nestmate import Nestmate, Position, TaskType
+
+__all__ = ['Nestmate', 'Position', 'TaskType'] 

Things/Ant_Colony/ant_colony/agents/nestmate.py

@@ -0,0 +1,166 @@
+"""
+Nestmate Agent Implementation
+
+This module implements a simplified version of the Nestmate agent class,
+representing an individual ant in the colony.
+"""
+
+import numpy as np
+from enum import Enum
+from dataclasses import dataclass
+
+class TaskType(Enum):
+    """Possible task types for a Nestmate agent."""
+    FORAGING = "foraging"
+    MAINTENANCE = "maintenance"
+    NURSING = "nursing"
+    DEFENSE = "defense"
+    EXPLORATION = "exploration"
+
+@dataclass
+class Position:
+    """2D position with orientation."""
+    x: float
+    y: float
+    theta: float = 0.0
+
+class Nestmate:
+    """Individual ant agent with basic behaviors."""
+    
+    def __init__(self, config: dict):
+        """Initialize Nestmate agent."""
+        self.config = config
+        
+        # Physical state
+        self.position = Position(0.0, 0.0, 0.0)
+        self.velocity = np.zeros(2)
+        self.energy = config['physical']['energy']['initial']
+        
+        # Task state
+        self.current_task = TaskType.EXPLORATION
+        self.carrying = None
+        
+        # Sensors
+        self.sensor_range = config['physical']['sensor_range']
+        
+        # Movement parameters
+        self.max_speed = config['physical']['max_speed']
+        self.turn_rate = config['physical']['turn_rate']
+        
+    def sense(self, world_state: dict) -> dict:
+        """Process sensory inputs from environment."""
+        # Get nearby entities within sensor range
+        nearby = {
+            'food': [],
+            'nestmates': [],
+            'obstacles': [],
+            'pheromones': {}
+        }
+        
+        # Process food sources
+        for food in world_state['resources']:
+            dist = self._distance_to(food.position)
+            if dist <= self.sensor_range:
+                nearby['food'].append((food, dist))
+        
+        # Process other agents
+        for agent in world_state['agents']:
+            if agent != self:
+                dist = self._distance_to(agent.position)
+                if dist <= self.sensor_range:
+                    nearby['nestmates'].append((agent, dist))
+        
+        # Process pheromones
+        for p_type, value in world_state['pheromones'].items():
+            nearby['pheromones'][p_type] = value
+            
+        return nearby
+        
+    def decide_action(self, sensed: dict) -> tuple:
+        """Decide next action based on current state and sensory input."""
+        # Default behavior: random walk
+        speed = self.max_speed
+        turn = np.random.uniform(-self.turn_rate, self.turn_rate)
+        
+        # Task-specific behaviors
+        if self.current_task == TaskType.FORAGING:
+            # If carrying food, head back to nest
+            if self.carrying:
+                turn = self._angle_to_nest()
+            # Otherwise, follow food pheromones or explore
+            elif 'food' in sensed['pheromones']:
+                pheromone_gradient = sensed['pheromones']['food']
+                if np.any(pheromone_gradient > 0):
+                    turn = self._follow_gradient(pheromone_gradient)
+                    
+        elif self.current_task == TaskType.EXPLORATION:
+            # Random walk with longer persistence
+            if np.random.random() < 0.1:  # 10% chance to change direction
+                turn = np.random.uniform(-np.pi, np.pi)
+                
+        return speed, turn
+        
+    def update(self, dt: float, world_state: dict):
+        """Update agent state."""
+        # Sense environment
+        sensed = self.sense(world_state)
+        
+        # Decide action
+        speed, turn = self.decide_action(sensed)
+        
+        # Update position and orientation
+        self.position.theta += turn * dt
+        self.position.theta = self.position.theta % (2 * np.pi)
+        
+        dx = speed * np.cos(self.position.theta) * dt
+        dy = speed * np.sin(self.position.theta) * dt
+        
+        self.position.x += dx
+        self.position.y += dy
+        
+        # Update energy
+        self.energy -= self.config['physical']['energy']['consumption_rate'] * dt
+        if self.carrying:
+            self.energy -= self.config['physical']['energy']['consumption_rate'] * dt
+            
+        # Consider task switching
+        self._consider_task_switch(sensed)
+        
+    def _distance_to(self, other_pos: Position) -> float:
+        """Calculate distance to another position."""
+        dx = other_pos.x - self.position.x
+        dy = other_pos.y - self.position.y
+        return np.sqrt(dx*dx + dy*dy)
+        
+    def _angle_to_nest(self) -> float:
+        """Calculate turn angle towards nest."""
+        # Simplified: assume nest is at (0,0)
+        dx = -self.position.x
+        dy = -self.position.y
+        target_angle = np.arctan2(dy, dx)
+        current_angle = self.position.theta
+        
+        # Calculate shortest turn
+        diff = target_angle - current_angle
+        while diff > np.pi:
+            diff -= 2*np.pi
+        while diff < -np.pi:
+            diff += 2*np.pi
+            
+        return np.clip(diff, -self.turn_rate, self.turn_rate)
+        
+    def _follow_gradient(self, gradient: np.ndarray) -> float:
+        """Calculate turn angle to follow a pheromone gradient."""
+        # Simplified: assume gradient gives us desired direction
+        target_angle = np.arctan2(gradient[1], gradient[0])
+        return self._angle_to_nest()  # Reuse angle calculation
+        
+    def _consider_task_switch(self, sensed: dict):
+        """Consider switching current task."""
+        # Simple task switching based on energy and random chance
+        if self.energy < self.config['physical']['energy']['critical_level']:
+            self.current_task = TaskType.FORAGING
+        elif np.random.random() < 0.001:  # 0.1% chance to switch tasks
+            available_tasks = list(TaskType)
+            available_tasks.remove(self.current_task)
+            self.current_task = np.random.choice(available_tasks) 

Things/Ant_Colony/ant_colony/config/simulation_config.yaml

@@ -0,0 +1,295 @@
+# Simulation Configuration
+
+# Environment settings
+environment:
+  size: [100, 100]  # World dimensions
+  nest_location: [50, 50]  # Center of the world
+  obstacles:
+    count: 10
+    size_range: [2, 5]
+  food_sources:
+    count: 5
+    size_range: [1, 3]
+    value_range: [10, 50]
+  pheromone_decay: 0.995  # Decay rate per timestep
+
+# Colony settings
+colony:
+  initial_population: 50
+  max_population: 100
+  reproduction_rate: 0.001  # Chance per timestep
+
+# Agent settings
+agent:
+  physical:
+    sensor_range: 10.0
+    max_speed: 2.0
+    turn_rate: 0.5  # radians per timestep
+    energy:
+      initial: 100.0
+      critical_level: 30.0
+      consumption_rate: 0.1
+  
+  behavior:
+    task_switching:
+      flexibility: 0.01  # Base rate of task switching
+
+# Visualization settings
+visualization:
+  enabled: true
+  update_interval: 1  # Update every N timesteps
+  trail_length: 50  # Number of positions to keep for trails
+  colors:
+    background: [255, 255, 255]
+    obstacles: [100, 100, 100]
+    food: [0, 255, 0]
+    nest: [139, 69, 19]
+    agents:
+      foraging: [255, 0, 0]
+      maintenance: [0, 0, 255]
+      nursing: [255, 192, 203]
+      defense: [128, 0, 0]
+      exploration: [255, 165, 0]
+    pheromones:
+      food: [0, 255, 0, 128]
+      home: [255, 0, 0, 128]
+
+# Simulation settings
+simulation:
+  timestep: 0.1
+  max_steps: 10000
+  random_seed: 42
+
+# Runtime Parameters
+runtime:
+  # Time Settings
+  time:
+    max_steps: 100000
+    timestep: 0.1
+    real_time_factor: 1.0
+    
+  # Execution
+  execution:
+    num_threads: 4
+    gpu_enabled: false
+    seed: 42
+    deterministic: true
+
+# Initialization
+initialization:
+  # World Setup
+  world:
+    random_seed: 42
+    generate_terrain: true
+    place_resources: true
+    
+  # Colony Setup
+  colony:
+    random_seed: 43
+    place_nest: true
+    distribute_agents: true
+
+# Physics Engine
+physics:
+  # Engine Settings
+  engine:
+    type: "2D"
+    collision_detection: true
+    spatial_hash_size: 5.0
+    
+  # Parameters
+  parameters:
+    gravity: [0, 0]
+    friction: 0.5
+    restitution: 0.5
+    
+  # Constraints
+  constraints:
+    velocity_cap: 10.0
+    force_cap: 100.0
+    acceleration_cap: 20.0
+
+# Integration
+integration:
+  # Methods
+  method: "RK4"
+  substeps: 2
+  
+  # Error Control
+  error_tolerance: 1e-6
+  max_iterations: 100
+  
+  # Stability
+  stability_checks: true
+  energy_conservation: true
+
+# Active Inference Parameters
+active_inference:
+  # Global Parameters
+  global:
+    temperature: 1.0
+    learning_rate: 0.1
+    exploration_rate: 0.2
+    
+  # Hierarchical Settings
+  hierarchical:
+    levels: 3
+    top_down_weight: 0.7
+    bottom_up_weight: 0.3
+    
+  # Precision Settings
+  precision:
+    initial: 1.0
+    learning_enabled: true
+    adaptation_rate: 0.05
+
+# Multi-Agent System
+multi_agent:
+  # Coordination
+  coordination:
+    enabled: true
+    method: "decentralized"
+    communication_range: 5.0
+    
+  # Synchronization
+  synchronization:
+    enabled: true
+    update_frequency: 10
+    sync_tolerance: 0.1
+    
+  # Load Balancing
+  load_balancing:
+    enabled: true
+    method: "dynamic"
+    threshold: 0.8
+
+# Analysis Settings
+analysis:
+  # Data Collection
+  data_collection:
+    enabled: true
+    frequency: 100
+    detailed_logging: true
+    
+  # Metrics
+  metrics:
+    agent_level:
+      - "position"
+      - "velocity"
+      - "energy"
+      - "beliefs"
+    colony_level:
+      - "population"
+      - "resources"
+      - "efficiency"
+      - "coordination"
+    environment_level:
+      - "resource_distribution"
+      - "pheromone_maps"
+      - "agent_density"
+    
+  # Statistics
+  statistics:
+    compute_mean: true
+    compute_variance: true
+    compute_correlations: true
+    temporal_analysis: true
+
+# Visualization
+visualization:
+  # Real-time Display
+  realtime:
+    enabled: true
+    update_frequency: 10
+    quality: "medium"
+    
+  # Recording
+  recording:
+    enabled: true
+    format: "mp4"
+    framerate: 30
+    resolution: [1920, 1080]
+    
+  # Features
+  features:
+    show_agents: true
+    show_pheromones: true
+    show_resources: true
+    show_stats: true
+    
+  # UI Elements
+  ui:
+    show_controls: true
+    show_plots: true
+    show_metrics: true
+    interactive: true
+
+# Data Management
+data:
+  # Storage
+  storage:
+    format: "hdf5"
+    compression: true
+    backup_frequency: 1000
+    
+  # Export
+  export:
+    enabled: true
+    format: ["csv", "json"]
+    frequency: 1000
+    
+  # Checkpointing
+  checkpointing:
+    enabled: true
+    frequency: 5000
+    keep_last: 5
+    
+  # Analysis Output
+  analysis:
+    save_plots: true
+    save_metrics: true
+    save_trajectories: true
+    output_format: ["png", "pdf"]
+
+# Performance Monitoring
+performance:
+  # Monitoring
+  monitoring:
+    enabled: true
+    frequency: 100
+    
+  # Profiling
+  profiling:
+    enabled: true
+    detailed: true
+    
+  # Optimization
+  optimization:
+    auto_tune: true
+    target_fps: 30
+    
+  # Resource Usage
+  resources:
+    max_memory: "4GB"
+    max_cpu_percent: 80
+    gpu_memory_limit: "2GB"
+
+# Debug Settings
+debug:
+  # Logging
+  logging:
+    level: "INFO"
+    file: "logs/simulation.log"
+    console_output: true
+    
+  # Validation
+  validation:
+    check_constraints: true
+    verify_physics: true
+    test_consistency: true
+    
+  # Development
+  development:
+    assertions_enabled: true
+    extra_checks: true
+    profile_code: true 

Things/Ant_Colony/ant_colony/environment/__init__.py

@@ -0,0 +1,5 @@
+"""Environment package for ant colony simulation."""
+
+from .world import World, Position, Resource
+
+__all__ = ['World', 'Position', 'Resource'] 

Things/Ant_Colony/ant_colony/main.py

@@ -0,0 +1,164 @@
+"""
+Main entry point for the ant colony simulation.
+"""
+
+import argparse
+import yaml
+import numpy as np
+from ant_colony.visualization import SimulationRenderer
+from ant_colony.agents import Nestmate, Position, TaskType
+from dataclasses import dataclass
+from typing import List
+
+@dataclass
+class FoodSource:
+    """Represents a food source in the environment."""
+    position: Position
+    size: float
+    value: float
+    remaining: float
+
+@dataclass
+class Obstacle:
+    """Represents an obstacle in the environment."""
+    position: Position
+    size: float
+
+class Simulation:
+    """Main simulation class."""
+    
+    def __init__(self, config_path: str):
+        """Initialize simulation with configuration."""
+        with open(config_path, 'r') as f:
+            self.config = yaml.safe_load(f)
+            
+        # Set random seed
+        np.random.seed(self.config['simulation']['random_seed'])
+        
+        # Initialize environment
+        self.env_size = self.config['environment']['size']
+        self.nest_location = self.config['environment']['nest_location']
+        
+        # Initialize agents
+        self.agents = self._create_agents()
+        
+        # Initialize resources
+        self.food_sources = self._create_food_sources()
+        self.obstacles = self._create_obstacles()
+        
+        # Initialize pheromone grids
+        self.pheromones = {
+            'food': np.zeros(self.env_size),
+            'home': np.zeros(self.env_size)
+        }
+        
+        # Setup visualization if enabled
+        if self.config['visualization']['enabled']:
+            self.renderer = SimulationRenderer(self.config)
+        else:
+            self.renderer = None
+            
+    def _create_agents(self) -> List[Nestmate]:
+        """Create initial population of agents."""
+        agents = []
+        for _ in range(self.config['colony']['initial_population']):
+            # Start agents near nest
+            x = self.nest_location[0] + np.random.normal(0, 2)
+            y = self.nest_location[1] + np.random.normal(0, 2)
+            theta = np.random.uniform(0, 2 * np.pi)
+            
+            agent = Nestmate(self.config['agent'])
+            agent.position = Position(x, y, theta)
+            agents.append(agent)
+            
+        return agents
+        
+    def _create_food_sources(self) -> List[FoodSource]:
+        """Create initial food sources."""
+        sources = []
+        for _ in range(self.config['environment']['food_sources']['count']):
+            x = np.random.uniform(0, self.env_size[0])
+            y = np.random.uniform(0, self.env_size[1])
+            size = np.random.uniform(*self.config['environment']['food_sources']['size_range'])
+            value = np.random.uniform(*self.config['environment']['food_sources']['value_range'])
+            
+            source = FoodSource(
+                position=Position(x, y, 0),
+                size=size,
+                value=value,
+                remaining=value
+            )
+            sources.append(source)
+            
+        return sources
+        
+    def _create_obstacles(self) -> List[Obstacle]:
+        """Create initial obstacles."""
+        obstacles = []
+        for _ in range(self.config['environment']['obstacles']['count']):
+            x = np.random.uniform(0, self.env_size[0])
+            y = np.random.uniform(0, self.env_size[1])
+            size = np.random.uniform(*self.config['environment']['obstacles']['size_range'])
+            
+            obstacle = Obstacle(
+                position=Position(x, y, 0),
+                size=size
+            )
+            obstacles.append(obstacle)
+            
+        return obstacles
+        
+    def update(self) -> None:
+        """Update simulation state for one timestep."""
+        dt = self.config['simulation']['timestep']
+        
+        # Update agents
+        world_state = {
+            'agents': self.agents,
+            'resources': self.food_sources,
+            'obstacles': self.obstacles,
+            'pheromones': self.pheromones
+        }
+        
+        for agent in self.agents:
+            agent.update(dt, world_state)
+            
+        # Update pheromones
+        decay = self.config['environment']['pheromone_decay']
+        self.pheromones['food'] *= decay
+        self.pheromones['home'] *= decay
+        
+        # Update visualization
+        if self.renderer and self.config['visualization']['enabled']:
+            self.renderer.update(world_state)
+            
+    def run(self) -> None:
+        """Run the simulation."""
+        max_steps = self.config['simulation']['max_steps']
+        
+        try:
+            for step in range(max_steps):
+                self.update()
+                
+                if step % 100 == 0:
+                    print(f"Step {step}/{max_steps}")
+                    
+        except KeyboardInterrupt:
+            print("\nSimulation interrupted by user")
+            
+        finally:
+            if self.renderer:
+                self.renderer.show()
+                
+def main():
+    """Main entry point."""
+    parser = argparse.ArgumentParser(description="Ant Colony Simulation")
+    parser.add_argument('--config', type=str, required=True,
+                      help='Path to configuration file')
+    args = parser.parse_args()
+    
+    simulation = Simulation(args.config)
+    simulation.run()
+    
+if __name__ == '__main__':
+    main() 

Things/Ant_Colony/ant_colony/visualization/__init__.py

@@ -0,0 +1,5 @@
+"""Visualization package for ant colony simulation."""
+
+from .renderer import SimulationRenderer
+
+__all__ = ['SimulationRenderer'] 

Things/Ant_Colony/ant_colony/visualization/renderer.py

@@ -0,0 +1,123 @@
+"""
+Visualization module for the ant colony simulation using matplotlib.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Circle, Wedge
+from matplotlib.collections import PatchCollection
+import matplotlib.animation as animation
+
+class SimulationRenderer:
+    """Handles visualization of the ant colony simulation."""
+    
+    def __init__(self, config: dict):
+        """Initialize the renderer with configuration settings."""
+        self.config = config
+        self.viz_config = config['visualization']
+        self.env_size = config['environment']['size']
+        
+        # Setup the figure and axis
+        self.fig, self.ax = plt.subplots(figsize=(10, 10))
+        self.ax.set_xlim(0, self.env_size[0])
+        self.ax.set_ylim(0, self.env_size[1])
+        self.ax.set_aspect('equal')
+        
+        # Initialize collections for different elements
+        self.agent_patches = []
+        self.food_patches = []
+        self.obstacle_patches = []
+        self.pheromone_plots = {}
+        
+        # Setup the nest
+        nest_loc = config['environment']['nest_location']
+        nest = Circle(nest_loc, 5, color=self.viz_config['colors']['nest'])
+        self.ax.add_patch(nest)
+        
+    def update(self, world_state: dict) -> None:
+        """Update the visualization with current world state."""
+        # Clear previous patches
+        for patch in self.agent_patches + self.food_patches + self.obstacle_patches:
+            patch.remove()
+        self.agent_patches.clear()
+        self.food_patches.clear()
+        self.obstacle_patches.clear()
+        
+        # Update pheromones
+        for p_type, grid in world_state['pheromones'].items():
+            if p_type not in self.pheromone_plots:
+                color = self.viz_config['colors']['pheromones'][p_type]
+                self.pheromone_plots[p_type] = self.ax.imshow(
+                    grid,
+                    extent=[0, self.env_size[0], 0, self.env_size[1]],
+                    cmap='Greens' if p_type == 'food' else 'Reds',
+                    alpha=0.3,
+                    vmin=0,
+                    vmax=1
+                )
+            else:
+                self.pheromone_plots[p_type].set_array(grid)
+        
+        # Draw agents
+        for agent in world_state['agents']:
+            color = self.viz_config['colors']['agents'][agent.current_task.value]
+            agent_patch = self._create_agent_patch(agent, color)
+            self.ax.add_patch(agent_patch)
+            self.agent_patches.append(agent_patch)
+        
+        # Draw food sources
+        for food in world_state['resources']:
+            food_patch = Circle(
+                (food.position.x, food.position.y),
+                food.size,
+                color=self.viz_config['colors']['food']
+            )
+            self.ax.add_patch(food_patch)
+            self.food_patches.append(food_patch)
+        
+        # Draw obstacles
+        for obstacle in world_state['obstacles']:
+            obstacle_patch = Circle(
+                (obstacle.position.x, obstacle.position.y),
+                obstacle.size,
+                color=self.viz_config['colors']['obstacles']
+            )
+            self.ax.add_patch(obstacle_patch)
+            self.obstacle_patches.append(obstacle_patch)
+        
+        # Trigger redraw
+        self.fig.canvas.draw()
+        self.fig.canvas.flush_events()
+        
+    def _create_agent_patch(self, agent, color: str) -> Wedge:
+        """Create a wedge patch to represent an agent."""
+        # Create a wedge shape to show orientation
+        radius = 1.0
+        angle = np.degrees(agent.position.theta)
+        wedge = Wedge(
+            (agent.position.x, agent.position.y),
+            radius,
+            angle - 30,  # 60 degree wide wedge
+            angle + 30,
+            color=color
+        )
+        return wedge
+        
+    def save_animation(self, frames: list, filename: str) -> None:
+        """Save the simulation as an animation."""
+        anim = animation.ArtistAnimation(
+            self.fig,
+            frames,
+            interval=50,
+            blit=True,
+            repeat=False
+        )
+        anim.save(filename, writer='pillow')
+        
+    def show(self) -> None:
+        """Display the current visualization."""
+        plt.show()
+        
+    def close(self) -> None:
+        """Close the visualization window."""
+        plt.close(self.fig) 

Things/Ant_Colony/config/simulation_config.yaml

@@ -1,10 +1,64 @@
 # Simulation Configuration
 
-# Simulation Parameters
+# Environment settings
+environment:
+  size: [100, 100]  # World dimensions
+  nest_location: [50, 50]  # Center of the world
+  obstacles:
+    count: 10
+    size_range: [2, 5]
+  food_sources:
+    count: 5
+    size_range: [1, 3]
+    value_range: [10, 50]
+  pheromone_decay: 0.995  # Decay rate per timestep
+
+# Colony settings
+colony:
+  initial_population: 50
+  max_population: 100
+  reproduction_rate: 0.001  # Chance per timestep
+
+# Agent settings
+agent:
+  physical:
+    sensor_range: 10.0
+    max_speed: 2.0
+    turn_rate: 0.5  # radians per timestep
+    energy:
+      initial: 100.0
+      critical_level: 30.0
+      consumption_rate: 0.1
+  
+  behavior:
+    task_switching:
+      flexibility: 0.01  # Base rate of task switching
+
+# Visualization settings
+visualization:
+  enabled: true
+  update_interval: 1  # Update every N timesteps
+  trail_length: 50  # Number of positions to keep for trails
+  colors:
+    background: [255, 255, 255]
+    obstacles: [100, 100, 100]
+    food: [0, 255, 0]
+    nest: [139, 69, 19]
+    agents:
+      foraging: [255, 0, 0]
+      maintenance: [0, 0, 255]
+      nursing: [255, 192, 203]
+      defense: [128, 0, 0]
+      exploration: [255, 165, 0]
+    pheromones:
+      food: [0, 255, 0, 128]
+      home: [255, 0, 0, 128]
+
+# Simulation settings
 simulation:
-  name: "Ant Colony Simulation"
-  version: "1.0.0"
-  description: "Multi-agent ant colony simulation using active inference"
+  timestep: 0.1
+  max_steps: 10000
+  random_seed: 42
 
 # Runtime Parameters
 runtime:

Things/Ant_Colony/main.py

@@ -0,0 +1,164 @@
+"""
+Main entry point for the ant colony simulation.
+"""
+
+import argparse
+import yaml
+import numpy as np
+from visualization.renderer import SimulationRenderer
+from agents.nestmate import Nestmate, Position
+from dataclasses import dataclass
+from typing import List
+
+@dataclass
+class FoodSource:
+    """Represents a food source in the environment."""
+    position: Position
+    size: float
+    value: float
+    remaining: float
+
+@dataclass
+class Obstacle:
+    """Represents an obstacle in the environment."""
+    position: Position
+    size: float
+
+class Simulation:
+    """Main simulation class."""
+    
+    def __init__(self, config_path: str):
+        """Initialize simulation with configuration."""
+        with open(config_path, 'r') as f:
+            self.config = yaml.safe_load(f)
+            
+        # Set random seed
+        np.random.seed(self.config['simulation']['random_seed'])
+        
+        # Initialize environment
+        self.env_size = self.config['environment']['size']
+        self.nest_location = self.config['environment']['nest_location']
+        
+        # Initialize agents
+        self.agents = self._create_agents()
+        
+        # Initialize resources
+        self.food_sources = self._create_food_sources()
+        self.obstacles = self._create_obstacles()
+        
+        # Initialize pheromone grids
+        self.pheromones = {
+            'food': np.zeros(self.env_size),
+            'home': np.zeros(self.env_size)
+        }
+        
+        # Setup visualization if enabled
+        if self.config['visualization']['enabled']:
+            self.renderer = SimulationRenderer(self.config)
+        else:
+            self.renderer = None
+            
+    def _create_agents(self) -> List[Nestmate]:
+        """Create initial population of agents."""
+        agents = []
+        for _ in range(self.config['colony']['initial_population']):
+            # Start agents near nest
+            x = self.nest_location[0] + np.random.normal(0, 2)
+            y = self.nest_location[1] + np.random.normal(0, 2)
+            theta = np.random.uniform(0, 2 * np.pi)
+            
+            agent = Nestmate(self.config['agent'])
+            agent.position = Position(x, y, theta)
+            agents.append(agent)
+            
+        return agents
+        
+    def _create_food_sources(self) -> List[FoodSource]:
+        """Create initial food sources."""
+        sources = []
+        for _ in range(self.config['environment']['food_sources']['count']):
+            x = np.random.uniform(0, self.env_size[0])
+            y = np.random.uniform(0, self.env_size[1])
+            size = np.random.uniform(*self.config['environment']['food_sources']['size_range'])
+            value = np.random.uniform(*self.config['environment']['food_sources']['value_range'])
+            
+            source = FoodSource(
+                position=Position(x, y, 0),
+                size=size,
+                value=value,
+                remaining=value
+            )
+            sources.append(source)
+            
+        return sources
+        
+    def _create_obstacles(self) -> List[Obstacle]:
+        """Create initial obstacles."""
+        obstacles = []
+        for _ in range(self.config['environment']['obstacles']['count']):
+            x = np.random.uniform(0, self.env_size[0])
+            y = np.random.uniform(0, self.env_size[1])
+            size = np.random.uniform(*self.config['environment']['obstacles']['size_range'])
+            
+            obstacle = Obstacle(
+                position=Position(x, y, 0),
+                size=size
+            )
+            obstacles.append(obstacle)
+            
+        return obstacles
+        
+    def update(self) -> None:
+        """Update simulation state for one timestep."""
+        dt = self.config['simulation']['timestep']
+        
+        # Update agents
+        world_state = {
+            'agents': self.agents,
+            'resources': self.food_sources,
+            'obstacles': self.obstacles,
+            'pheromones': self.pheromones
+        }
+        
+        for agent in self.agents:
+            agent.update(dt, world_state)
+            
+        # Update pheromones
+        decay = self.config['environment']['pheromone_decay']
+        self.pheromones['food'] *= decay
+        self.pheromones['home'] *= decay
+        
+        # Update visualization
+        if self.renderer and self.config['visualization']['enabled']:
+            self.renderer.update(world_state)
+            
+    def run(self) -> None:
+        """Run the simulation."""
+        max_steps = self.config['simulation']['max_steps']
+        
+        try:
+            for step in range(max_steps):
+                self.update()
+                
+                if step % 100 == 0:
+                    print(f"Step {step}/{max_steps}")
+                    
+        except KeyboardInterrupt:
+            print("\nSimulation interrupted by user")
+            
+        finally:
+            if self.renderer:
+                self.renderer.show()
+                
+def main():
+    """Main entry point."""
+    parser = argparse.ArgumentParser(description="Ant Colony Simulation")
+    parser.add_argument('--config', type=str, required=True,
+                      help='Path to configuration file')
+    args = parser.parse_args()
+    
+    simulation = Simulation(args.config)
+    simulation.run()
+    
+if __name__ == '__main__':
+    main() 

Things/Ant_Colony/setup.py

@@ -0,0 +1,25 @@
+from setuptools import setup, find_packages
+
+setup(
+    name="ant_colony",
+    version="1.0.0",
+    description="Multi-agent ant colony simulation",
+    author="Your Name",
+    packages=find_packages(),
+    package_data={
+        'ant_colony': ['config/*.yaml']
+    },
+    install_requires=[
+        'numpy',
+        'matplotlib',
+        'networkx',
+        'pyyaml',
+        'noise'  # For terrain generation
+    ],
+    python_requires='>=3.7',
+    entry_points={
+        'console_scripts': [
+            'ant-colony=ant_colony.main:main',
+        ],
+    },
+) 

Things/Ant_Colony/simulation.py

@@ -1,180 +1,151 @@
 """
-Simulation Runner
+Simulation Runner for Ant Colony Simulation
 
-This module implements the simulation runner that manages the ant colony simulation,
-including visualization, data collection, and analysis.
+This module provides the main simulation runner that coordinates the colony,
+environment, and visualization components.
 """
 
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.animation import FuncAnimation
 import seaborn as sns
-from typing import Dict, List, Tuple, Optional
-import yaml
-import h5py
 import time
 import logging
+import yaml
+import h5py
 from pathlib import Path
 import pandas as pd
+from typing import Dict, List, Tuple, Optional, Any
 
-from environment.world import World
-from colony import Colony
+from .visualization import ColonyVisualizer
+from .colony import Colony
+from .environment import World
+from .utils.data_collection import DataCollector
 from agents.nestmate import TaskType
 
 class Simulation:
-    """
-    Manages the ant colony simulation, including visualization and data collection.
-    """
+    """Main simulation runner class."""
     
     def __init__(self, config_path: str):
-        """Initialize simulation."""
-        # Load configuration
+        """Initialize simulation with configuration."""
         self.config = self._load_config(config_path)
-        
-        # Set up logging
         self._setup_logging()
         
         # Initialize components
-        self.environment = World(self.config)
-        self.colony = Colony(self.config, self.environment)
-        
-        # Visualization setup
-        if self.config['visualization']['realtime']['enabled']:
-            self._setup_visualization()
-            
-        # Data collection setup
-        self.data = {
-            'time': [],
-            'population': [],
-            'resources': [],
-            'task_distribution': [],
-            'efficiency_metrics': [],
-            'coordination_metrics': []
-        }
-        
-        # Performance tracking
-        self.performance_metrics = {
-            'step_times': [],
-            'memory_usage': [],
-            'fps': []
-        }
+        self.environment = World(self.config['environment'])
+        self.colony = Colony(self.config['colony'], self.environment)
+        self.visualizer = ColonyVisualizer(self.config['visualization'])
+        self.data_collector = DataCollector(self.config['data_collection'])
         
         # Simulation state
         self.current_step = 0
-        self.running = False
+        self.max_steps = self.config['simulation']['max_steps']
+        self.timestep = self.config['simulation']['timestep']
         self.paused = False
+        self.data = {
+            'time': [],
+            'resources': [],
+            'task_distribution': [],
+            'efficiency_metrics': []
+        }
         
-    def _load_config(self, config_path: str) -> dict:
+    def _load_config(self, config_path: str) -> Dict[str, Any]:
         """Load configuration from YAML file."""
         with open(config_path, 'r') as f:
             config = yaml.safe_load(f)
         return config
         
     def _setup_logging(self):
-        """Set up logging configuration."""
+        """Setup logging configuration."""
+        log_config = self.config['debug']['logging']
         logging.basicConfig(
-            level=self.config['debug']['logging']['level'],
-            format=self.config['debug']['logging']['format'],
-            filename=self.config['debug']['logging']['file']
+            level=getattr(logging, log_config['level']),
+            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+            filename=log_config['file']
         )
         self.logger = logging.getLogger(__name__)
         
-    def _setup_visualization(self):
-        """Set up visualization components."""
-        plt.style.use('dark_background')
-        
-        # Create figure and subplots
-        self.fig = plt.figure(figsize=self.config['visualization']['plots']['figure_size'])
-        
-        # Main simulation view
-        self.ax_main = self.fig.add_subplot(221)
-        self.ax_main.set_title('Colony Simulation')
-        
-        # Resource levels
-        self.ax_resources = self.fig.add_subplot(222)
-        self.ax_resources.set_title('Resource Levels')
-        
-        # Task distribution
-        self.ax_tasks = self.fig.add_subplot(223)
-        self.ax_tasks.set_title('Task Distribution')
-        
-        # Efficiency metrics
-        self.ax_metrics = self.fig.add_subplot(224)
-        self.ax_metrics.set_title('Performance Metrics')
-        
-        plt.tight_layout()
-        
-    def run(self, num_steps: Optional[int] = None):
-        """Run simulation for specified number of steps."""
-        self.running = True
-        start_time = time.time()
-        
-        max_steps = num_steps if num_steps is not None else self.config['runtime']['time']['max_steps']
-        
-        try:
-            while self.running and self.current_step < max_steps:
-                if not self.paused:
-                    self.step()
-                    
-                    # Update visualization
-                    if self.config['visualization']['realtime']['enabled'] and \
-                       self.current_step % self.config['visualization']['realtime']['update_frequency'] == 0:
-                        self._update_visualization()
-                        
-                    # Save data
-                    if self.current_step % self.config['data']['export']['frequency'] == 0:
-                        self._save_data()
-                        
-                    # Performance monitoring
-                    if self.config['performance']['monitoring']['enabled'] and \
-                       self.current_step % self.config['performance']['monitoring']['frequency'] == 0:
-                        self._monitor_performance()
-                        
-        except KeyboardInterrupt:
-            self.logger.info("Simulation interrupted by user")
-        finally:
-            self._cleanup()
-            
-        end_time = time.time()
-        self.logger.info(f"Simulation completed in {end_time - start_time:.2f} seconds")
-        
     def step(self):
         """Execute one simulation step."""
-        step_start = time.time()
-        
         # Update environment
-        self.environment.step(self.config['runtime']['time']['timestep'])
+        self.environment.update(self.timestep)
         
         # Update colony
-        self.colony.step(self.config['runtime']['time']['timestep'])
+        self.colony.update(self.timestep)
         
         # Collect data
         self._collect_data()
         
-        # Update step counter
+        # Increment step counter
         self.current_step += 1
         
-        # Log step time
-        step_time = time.time() - step_start
-        self.performance_metrics['step_times'].append(step_time)
-        
     def _collect_data(self):
-        """Collect simulation data."""
-        # Basic metrics
-        self.data['time'].append(self.current_step * self.config['runtime']['time']['timestep'])
-        self.data['population'].append(self.colony.stats.population)
+        """Collect simulation data for analysis and visualization."""
+        self.data['time'].append(self.current_step * self.timestep)
         
-        # Resources
-        self.data['resources'].append(self.colony.stats.resource_levels.copy())
+        # Collect resource data
+        resource_data = {
+            rtype: sum(r.amount for r in resources)
+            for rtype, resources in self.environment.resources.items()
+        }
+        self.data['resources'].append(resource_data)
         
-        # Task distribution
-        self.data['task_distribution'].append(self.colony.stats.task_distribution.copy())
+        # Collect task distribution
+        task_dist = self.colony.get_task_distribution()
+        self.data['task_distribution'].append(task_dist)
         
-        # Efficiency metrics
-        self.data['efficiency_metrics'].append(self.colony.stats.efficiency_metrics.copy())
+        # Collect efficiency metrics
+        metrics = self.colony.compute_efficiency_metrics()
+        self.data['efficiency_metrics'].append(metrics)
         
-        # Coordination metrics
-        self.data['coordination_metrics'].append(self.colony.stats.coordination_metrics.copy())
+    def run(self, headless: bool = False):
+        """Run the simulation."""
+        self.logger.info("Starting simulation...")
+        
+        if not headless:
+            # Create and show animation
+            animation = self.visualizer.create_animation(self)
+            plt.show()
+        else:
+            # Run without visualization
+            try:
+                while self.current_step < self.max_steps and not self.paused:
+                    self.step()
+                    
+                    # Save data periodically
+                    if self.current_step % self.config['data_collection']['frequency'] == 0:
+                        self.data_collector.save_data(self.data)
+                        
+            except KeyboardInterrupt:
+                self.logger.info("Simulation interrupted by user")
+            finally:
+                # Save final data
+                self.data_collector.save_data(self.data)
+                
+        self.logger.info(f"Simulation completed after {self.current_step} steps")
+        
+    def pause(self):
+        """Pause the simulation."""
+        self.paused = True
+        self.logger.info("Simulation paused")
+        
+    def resume(self):
+        """Resume the simulation."""
+        self.paused = False
+        self.logger.info("Simulation resumed")
+        
+    def reset(self):
+        """Reset the simulation to initial state."""
+        self.logger.info("Resetting simulation...")
+        self.current_step = 0
+        self.environment.reset()
+        self.colony.reset()
+        self.data = {
+            'time': [],
+            'resources': [],
+            'task_distribution': [],
+            'efficiency_metrics': []
+        }
         
     def _update_visualization(self):
         """Update visualization plots."""
@@ -299,10 +270,10 @@ class Simulation:
             sim_group.create_dataset('step', data=self.current_step)
             
             # Save colony data
-            colony_group.create_dataset('population', data=np.array(self.data['population']))
+            colony_group.create_dataset('population', data=np.array(self.colony.stats.population))
             
             # Create datasets for dictionary data
-            for key in ['resources', 'task_distribution', 'efficiency_metrics', 'coordination_metrics']:
+            for key in ['resources', 'task_distribution', 'efficiency_metrics']:
                 if len(self.data[key]) > 0:
                     group = colony_group.create_group(key)
                     for metric_key in self.data[key][0].keys():
@@ -317,7 +288,7 @@ class Simulation:
         # Save basic metrics
         pd.DataFrame({
             'time': self.data['time'],
-            'population': self.data['population']
+            'population': self.colony.stats.population
         }).to_csv(output_dir / 'basic_metrics.csv', index=False)
         
         # Save resource data
@@ -378,17 +349,16 @@ class Simulation:
         self.logger.info(f"Final population: {self.colony.stats.population}")
         self.logger.info(f"Final resource levels: {self.colony.stats.resource_levels}")
         
-    def pause(self):
-        """Pause the simulation."""
-        self.paused = True
-        self.logger.info("Simulation paused")
-        
-    def resume(self):
-        """Resume the simulation."""
-        self.paused = False
-        self.logger.info("Simulation resumed")
-        
-    def stop(self):
-        """Stop the simulation."""
-        self.running = False
-        self.logger.info("Simulation stopped") 
+if __name__ == '__main__':
+    import argparse
+    
+    parser = argparse.ArgumentParser(description='Run Ant Colony Simulation')
+    parser.add_argument('--config', type=str, required=True,
+                       help='Path to configuration file')
+    parser.add_argument('--headless', action='store_true',
+                       help='Run without visualization')
+    args = parser.parse_args()
+    
+    # Create and run simulation
+    sim = Simulation(args.config)
+    sim.run(headless=args.headless) 

Things/Ant_Colony/visualization.py

@@ -0,0 +1,374 @@
+"""
+Enhanced Visualization Module for Ant Colony Simulation
+
+This module provides advanced visualization capabilities for the ant colony simulation,
+including animated pheromone trails, agent movements, and colony statistics.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+import seaborn as sns
+from typing import Dict, List, Optional
+from matplotlib.patches import Circle, Wedge, Path, PathPatch
+from matplotlib.collections import PatchCollection
+import colorsys
+import networkx as nx
+
+class ColonyVisualizer:
+    """Advanced visualization for the ant colony simulation."""
+    
+    def __init__(self, config: dict):
+        """Initialize visualizer."""
+        self.config = config
+        self.fig = None
+        self.axes = {}
+        self.artists = {}
+        self.animation = None
+        
+        # Color schemes
+        self.color_schemes = {
+            'terrain': plt.cm.terrain,
+            'pheromone': {
+                'food': plt.cm.Greens,
+                'home': plt.cm.Reds,
+                'alarm': plt.cm.Oranges,
+                'trail': plt.cm.Blues
+            },
+            'agents': {
+                'foraging': '#2ecc71',
+                'maintenance': '#3498db',
+                'nursing': '#9b59b6',
+                'defense': '#e74c3c',
+                'exploration': '#f1c40f'
+            }
+        }
+        
+        # Setup visualization
+        self._setup_visualization()
+        
+    def _setup_visualization(self):
+        """Set up the visualization layout."""
+        plt.style.use('dark_background')
+        
+        # Create figure with custom layout
+        self.fig = plt.figure(figsize=(16, 9))
+        gs = self.fig.add_gridspec(3, 3)
+        
+        # Main simulation view (larger)
+        self.axes['main'] = self.fig.add_subplot(gs[0:2, 0:2])
+        self.axes['main'].set_title('Colony Simulation')
+        
+        # Pheromone levels
+        self.axes['pheromone'] = self.fig.add_subplot(gs[0, 2])
+        self.axes['pheromone'].set_title('Pheromone Levels')
+        
+        # Resource levels
+        self.axes['resources'] = self.fig.add_subplot(gs[1, 2])
+        self.axes['resources'].set_title('Resource Levels')
+        
+        # Task distribution
+        self.axes['tasks'] = self.fig.add_subplot(gs[2, 0])
+        self.axes['tasks'].set_title('Task Distribution')
+        
+        # Efficiency metrics
+        self.axes['metrics'] = self.fig.add_subplot(gs[2, 1])
+        self.axes['metrics'].set_title('Colony Metrics')
+        
+        # Network view
+        self.axes['network'] = self.fig.add_subplot(gs[2, 2])
+        self.axes['network'].set_title('Social Network')
+        
+        plt.tight_layout()
+        
+    def create_animation(self, simulation, interval: int = 50) -> FuncAnimation:
+        """Create animation of the colony simulation."""
+        def update(frame):
+            # Update simulation state
+            if not simulation.paused:
+                simulation.step()
+            
+            # Clear all axes
+            for ax in self.axes.values():
+                ax.clear()
+                
+            # Update all plots
+            artists = []
+            artists.extend(self._plot_main_view(simulation))
+            artists.extend(self._plot_pheromone_levels(simulation))
+            artists.extend(self._plot_resource_levels(simulation))
+            artists.extend(self._plot_task_distribution(simulation))
+            artists.extend(self._plot_efficiency_metrics(simulation))
+            artists.extend(self._plot_social_network(simulation))
+            
+            return artists
+            
+        self.animation = FuncAnimation(
+            self.fig,
+            update,
+            frames=None,
+            interval=interval,
+            blit=True
+        )
+        
+        return self.animation
+        
+    def _plot_main_view(self, simulation) -> List:
+        """Plot main simulation view with enhanced graphics."""
+        ax = self.axes['main']
+        artists = []
+        
+        # Plot terrain with enhanced colormap
+        terrain = simulation.environment.terrain.height_map
+        terrain_img = ax.imshow(terrain, cmap=self.color_schemes['terrain'])
+        artists.append(terrain_img)
+        
+        # Plot pheromone trails with alpha blending
+        for ptype, pdata in simulation.environment.pheromones.layers.items():
+            pheromone_grid = pdata['grid']
+            if np.any(pheromone_grid > 0):
+                pheromone_img = ax.imshow(
+                    pheromone_grid,
+                    cmap=self.color_schemes['pheromone'][ptype],
+                    alpha=0.5
+                )
+                artists.append(pheromone_img)
+        
+        # Plot agents with directional markers
+        for agent in simulation.colony.agents:
+            agent_color = self.color_schemes['agents'][agent.current_task.value]
+            
+            # Create ant shape
+            ant = self._create_ant_shape(agent.position, agent.orientation, 
+                                       size=1.0, color=agent_color)
+            ax.add_patch(ant)
+            artists.append(ant)
+            
+            # Add carrying indicator if needed
+            if agent.carrying is not None:
+                carry_indicator = Circle(
+                    (agent.position[0], agent.position[1]),
+                    radius=0.3,
+                    color='yellow',
+                    alpha=0.7
+                )
+                ax.add_patch(carry_indicator)
+                artists.append(carry_indicator)
+        
+        # Plot resources
+        for resource in simulation.environment.resources:
+            if resource.type == 'food':
+                marker = '*'
+                color = 'yellow'
+            else:
+                marker = 's'
+                color = 'cyan'
+                
+            resource_dot = ax.scatter(
+                resource.position.x,
+                resource.position.y,
+                c=color,
+                marker=marker,
+                s=50
+            )
+            artists.append(resource_dot)
+        
+        # Plot nest with concentric circles
+        nest_x = simulation.colony.nest_position.x
+        nest_y = simulation.colony.nest_position.y
+        
+        for radius in [1, 2, 3]:
+            nest_circle = Circle(
+                (nest_x, nest_y),
+                radius,
+                color='white',
+                fill=False,
+                alpha=0.5
+            )
+            ax.add_patch(nest_circle)
+            artists.append(nest_circle)
+            
+        # Add nest center
+        nest_center = ax.scatter(
+            [nest_x], [nest_y],
+            c='white',
+            marker='s',
+            s=100
+        )
+        artists.append(nest_center)
+        
+        # Set limits and title
+        ax.set_xlim(0, simulation.environment.size[0])
+        ax.set_ylim(0, simulation.environment.size[1])
+        ax.set_title(f'Colony Simulation (Step {simulation.current_step})')
+        
+        return artists
+        
+    def _create_ant_shape(self, position, orientation, size=1.0, color='white'):
+        """Create an ant-like shape for visualization."""
+        # Create ant body segments
+        body_verts = [
+            (-0.5, -0.2),  # Abdomen
+            (0.0, -0.1),   # Thorax
+            (0.5, 0.0),    # Head
+            (0.0, 0.1),
+            (-0.5, 0.2)
+        ]
+        
+        # Scale and rotate vertices
+        cos_theta = np.cos(orientation)
+        sin_theta = np.sin(orientation)
+        
+        transformed_verts = []
+        for x, y in body_verts:
+            x_rot = x * cos_theta - y * sin_theta
+            y_rot = x * sin_theta + y * cos_theta
+            transformed_verts.append(
+                (position[0] + x_rot * size, 
+                 position[1] + y_rot * size)
+            )
+            
+        # Create path
+        codes = [Path.MOVETO] + [Path.LINETO] * (len(transformed_verts) - 1)
+        path = Path(transformed_verts, codes)
+        
+        return PathPatch(path, facecolor=color, edgecolor='none', alpha=0.7)
+        
+    def _plot_pheromone_levels(self, simulation) -> List:
+        """Plot pheromone concentration levels."""
+        ax = self.axes['pheromone']
+        artists = []
+        
+        # Get pheromone data
+        pheromone_levels = []
+        labels = []
+        colors = []
+        
+        for ptype, pdata in simulation.environment.pheromones.layers.items():
+            pheromone_levels.append(np.mean(pdata['grid']))
+            labels.append(ptype)
+            colors.append(self.color_schemes['agents'].get(ptype, '#95a5a6'))
+            
+        # Create bar plot
+        if pheromone_levels:
+            bars = ax.bar(range(len(pheromone_levels)), pheromone_levels,
+                         color=colors)
+            artists.extend(bars)
+            
+            ax.set_xticks(range(len(labels)))
+            ax.set_xticklabels(labels, rotation=45)
+            ax.set_ylabel('Average Concentration')
+            
+        return artists
+        
+    def _plot_resource_levels(self, simulation) -> List:
+        """Plot resource levels over time."""
+        ax = self.axes['resources']
+        artists = []
+        
+        if len(simulation.data['time']) > 0:
+            for resource_type in simulation.colony.resources.keys():
+                values = [d[resource_type] for d in simulation.data['resources']]
+                line = ax.plot(simulation.data['time'][-100:], values[-100:],
+                             label=resource_type)
+                artists.extend(line)
+                
+            ax.legend()
+            ax.set_xlabel('Time')
+            ax.set_ylabel('Amount')
+            
+        return artists
+        
+    def _plot_task_distribution(self, simulation) -> List:
+        """Plot task distribution with enhanced graphics."""
+        ax = self.axes['tasks']
+        artists = []
+        
+        if len(simulation.data['task_distribution']) > 0:
+            latest_dist = simulation.data['task_distribution'][-1]
+            tasks = list(latest_dist.keys())
+            counts = [latest_dist[task] for task in tasks]
+            colors = [self.color_schemes['agents'][task.value] for task in tasks]
+            
+            # Create stacked bars for current and needed agents
+            bars = ax.bar(range(len(tasks)), counts, color=colors)
+            artists.extend(bars)
+            
+            # Add target lines for needed agents
+            for i, task in enumerate(tasks):
+                need = simulation.colony.task_needs[task] * len(simulation.colony.agents)
+                line = ax.hlines(need, i-0.4, i+0.4, colors='white', linestyles='--')
+                artists.append(line)
+                
+            ax.set_xticks(range(len(tasks)))
+            ax.set_xticklabels([task.value for task in tasks], rotation=45)
+            
+        return artists
+        
+    def _plot_efficiency_metrics(self, simulation) -> List:
+        """Plot efficiency metrics with enhanced graphics."""
+        ax = self.axes['metrics']
+        artists = []
+        
+        if len(simulation.data['efficiency_metrics']) > 0:
+            metrics = simulation.data['efficiency_metrics'][-1]
+            values = list(metrics.values())
+            labels = list(metrics.keys())
+            
+            # Create radar chart
+            angles = np.linspace(0, 2*np.pi, len(labels), endpoint=False)
+            values = np.concatenate((values, [values[0]]))  # Close the polygon
+            angles = np.concatenate((angles, [angles[0]]))  # Close the polygon
+            
+            # Plot radar
+            line = ax.plot(angles, values)
+            artists.extend(line)
+            
+            # Fill radar
+            fill = ax.fill(angles, values, alpha=0.25)
+            artists.extend(fill)
+            
+            # Add labels
+            for angle, label in zip(angles[:-1], labels):
+                ha = 'right' if np.cos(angle) < 0 else 'left'
+                va = 'top' if np.sin(angle) < 0 else 'bottom'
+                
+                ax.text(angle, 1.2, label,
+                       ha=ha, va=va,
+                       rotation=np.degrees(angle))
+                
+            ax.set_ylim(0, 1)
+            ax.set_xticks([])
+            
+        return artists
+        
+    def _plot_social_network(self, simulation) -> List:
+        """Plot social network with enhanced graphics."""
+        ax = self.axes['network']
+        artists = []
+        
+        # Get network data
+        G = simulation.colony.interaction_network
+        
+        if len(G.nodes) > 0:
+            # Calculate node positions using spring layout
+            pos = nx.spring_layout(G)
+            
+            # Draw edges
+            for edge in G.edges():
+                x = [pos[edge[0]][0], pos[edge[1]][0]]
+                y = [pos[edge[0]][1], pos[edge[1]][1]]
+                line = ax.plot(x, y, 'w-', alpha=0.2)
+                artists.extend(line)
+            
+            # Draw nodes
+            for node in G.nodes():
+                color = self.color_schemes['agents'][node.current_task.value]
+                dot = ax.scatter(pos[node][0], pos[node][1],
+                               c=color, s=50)
+                artists.append(dot)
+                
+        ax.set_xticks([])
+        ax.set_yticks([])
+        
+        return artists 

Things/Ant_Colony/visualization/renderer.py

@@ -0,0 +1,123 @@
+"""
+Visualization module for the ant colony simulation using matplotlib.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Circle, Wedge
+from matplotlib.collections import PatchCollection
+import matplotlib.animation as animation
+
+class SimulationRenderer:
+    """Handles visualization of the ant colony simulation."""
+    
+    def __init__(self, config: dict):
+        """Initialize the renderer with configuration settings."""
+        self.config = config
+        self.viz_config = config['visualization']
+        self.env_size = config['environment']['size']
+        
+        # Setup the figure and axis
+        self.fig, self.ax = plt.subplots(figsize=(10, 10))
+        self.ax.set_xlim(0, self.env_size[0])
+        self.ax.set_ylim(0, self.env_size[1])
+        self.ax.set_aspect('equal')
+        
+        # Initialize collections for different elements
+        self.agent_patches = []
+        self.food_patches = []
+        self.obstacle_patches = []
+        self.pheromone_plots = {}
+        
+        # Setup the nest
+        nest_loc = config['environment']['nest_location']
+        nest = Circle(nest_loc, 5, color=self.viz_config['colors']['nest'])
+        self.ax.add_patch(nest)
+        
+    def update(self, world_state: dict) -> None:
+        """Update the visualization with current world state."""
+        # Clear previous patches
+        for patch in self.agent_patches + self.food_patches + self.obstacle_patches:
+            patch.remove()
+        self.agent_patches.clear()
+        self.food_patches.clear()
+        self.obstacle_patches.clear()
+        
+        # Update pheromones
+        for p_type, grid in world_state['pheromones'].items():
+            if p_type not in self.pheromone_plots:
+                color = self.viz_config['colors']['pheromones'][p_type]
+                self.pheromone_plots[p_type] = self.ax.imshow(
+                    grid,
+                    extent=[0, self.env_size[0], 0, self.env_size[1]],
+                    cmap='Greens' if p_type == 'food' else 'Reds',
+                    alpha=0.3,
+                    vmin=0,
+                    vmax=1
+                )
+            else:
+                self.pheromone_plots[p_type].set_array(grid)
+        
+        # Draw agents
+        for agent in world_state['agents']:
+            color = self.viz_config['colors']['agents'][agent.current_task.value]
+            agent_patch = self._create_agent_patch(agent, color)
+            self.ax.add_patch(agent_patch)
+            self.agent_patches.append(agent_patch)
+        
+        # Draw food sources
+        for food in world_state['resources']:
+            food_patch = Circle(
+                (food.position.x, food.position.y),
+                food.size,
+                color=self.viz_config['colors']['food']
+            )
+            self.ax.add_patch(food_patch)
+            self.food_patches.append(food_patch)
+        
+        # Draw obstacles
+        for obstacle in world_state['obstacles']:
+            obstacle_patch = Circle(
+                (obstacle.position.x, obstacle.position.y),
+                obstacle.size,
+                color=self.viz_config['colors']['obstacles']
+            )
+            self.ax.add_patch(obstacle_patch)
+            self.obstacle_patches.append(obstacle_patch)
+        
+        # Trigger redraw
+        self.fig.canvas.draw()
+        self.fig.canvas.flush_events()
+        
+    def _create_agent_patch(self, agent, color: str) -> Wedge:
+        """Create a wedge patch to represent an agent."""
+        # Create a wedge shape to show orientation
+        radius = 1.0
+        angle = np.degrees(agent.position.theta)
+        wedge = Wedge(
+            (agent.position.x, agent.position.y),
+            radius,
+            angle - 30,  # 60 degree wide wedge
+            angle + 30,
+            color=color
+        )
+        return wedge
+        
+    def save_animation(self, frames: list, filename: str) -> None:
+        """Save the simulation as an animation."""
+        anim = animation.ArtistAnimation(
+            self.fig,
+            frames,
+            interval=50,
+            blit=True,
+            repeat=False
+        )
+        anim.save(filename, writer='pillow')
+        
+    def show(self) -> None:
+        """Display the current visualization."""
+        plt.show()
+        
+    def close(self) -> None:
+        """Close the visualization window."""
+        plt.close(self.fig) 

ant_colony_env/bin/Activate.ps1

@@ -0,0 +1,247 @@
+<#
+.Synopsis
+Activate a Python virtual environment for the current PowerShell session.
+
+.Description
+Pushes the python executable for a virtual environment to the front of the
+$Env:PATH environment variable and sets the prompt to signify that you are
+in a Python virtual environment. Makes use of the command line switches as
+well as the `pyvenv.cfg` file values present in the virtual environment.
+
+.Parameter VenvDir
+Path to the directory that contains the virtual environment to activate. The
+default value for this is the parent of the directory that the Activate.ps1
+script is located within.
+
+.Parameter Prompt
+The prompt prefix to display when this virtual environment is activated. By
+default, this prompt is the name of the virtual environment folder (VenvDir)
+surrounded by parentheses and followed by a single space (ie. '(.venv) ').
+
+.Example
+Activate.ps1
+Activates the Python virtual environment that contains the Activate.ps1 script.
+
+.Example
+Activate.ps1 -Verbose
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and shows extra information about the activation as it executes.
+
+.Example
+Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv
+Activates the Python virtual environment located in the specified location.
+
+.Example
+Activate.ps1 -Prompt "MyPython"
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and prefixes the current prompt with the specified string (surrounded in
+parentheses) while the virtual environment is active.
+
+.Notes
+On Windows, it may be required to enable this Activate.ps1 script by setting the
+execution policy for the user. You can do this by issuing the following PowerShell
+command:
+
+PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
+
+For more information on Execution Policies: 
+https://go.microsoft.com/fwlink/?LinkID=135170
+
+#>
+Param(
+    [Parameter(Mandatory = $false)]
+    [String]
+    $VenvDir,
+    [Parameter(Mandatory = $false)]
+    [String]
+    $Prompt
+)
+
+<# Function declarations --------------------------------------------------- #>
+
+<#
+.Synopsis
+Remove all shell session elements added by the Activate script, including the
+addition of the virtual environment's Python executable from the beginning of
+the PATH variable.
+
+.Parameter NonDestructive
+If present, do not remove this function from the global namespace for the
+session.
+
+#>
+function global:deactivate ([switch]$NonDestructive) {
+    # Revert to original values
+
+    # The prior prompt:
+    if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) {
+        Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt
+        Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT
+    }
+
+    # The prior PYTHONHOME:
+    if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) {
+        Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME
+        Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME
+    }
+
+    # The prior PATH:
+    if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) {
+        Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH
+        Remove-Item -Path Env:_OLD_VIRTUAL_PATH
+    }
+
+    # Just remove the VIRTUAL_ENV altogether:
+    if (Test-Path -Path Env:VIRTUAL_ENV) {
+        Remove-Item -Path env:VIRTUAL_ENV
+    }
+
+    # Just remove VIRTUAL_ENV_PROMPT altogether.
+    if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) {
+        Remove-Item -Path env:VIRTUAL_ENV_PROMPT
+    }
+
+    # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether:
+    if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) {
+        Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force
+    }
+
+    # Leave deactivate function in the global namespace if requested:
+    if (-not $NonDestructive) {
+        Remove-Item -Path function:deactivate
+    }
+}
+
+<#
+.Description
+Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the
+given folder, and returns them in a map.
+
+For each line in the pyvenv.cfg file, if that line can be parsed into exactly
+two strings separated by `=` (with any amount of whitespace surrounding the =)
+then it is considered a `key = value` line. The left hand string is the key,
+the right hand is the value.
+
+If the value starts with a `'` or a `"` then the first and last character is
+stripped from the value before being captured.
+
+.Parameter ConfigDir
+Path to the directory that contains the `pyvenv.cfg` file.
+#>
+function Get-PyVenvConfig(
+    [String]
+    $ConfigDir
+) {
+    Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg"
+
+    # Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue).
+    $pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue
+
+    # An empty map will be returned if no config file is found.
+    $pyvenvConfig = @{ }
+
+    if ($pyvenvConfigPath) {
+
+        Write-Verbose "File exists, parse `key = value` lines"
+        $pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath
+
+        $pyvenvConfigContent | ForEach-Object {
+            $keyval = $PSItem -split "\s*=\s*", 2
+            if ($keyval[0] -and $keyval[1]) {
+                $val = $keyval[1]
+
+                # Remove extraneous quotations around a string value.
+                if ("'""".Contains($val.Substring(0, 1))) {
+                    $val = $val.Substring(1, $val.Length - 2)
+                }
+
+                $pyvenvConfig[$keyval[0]] = $val
+                Write-Verbose "Adding Key: '$($keyval[0])'='$val'"
+            }
+        }
+    }
+    return $pyvenvConfig
+}
+
+
+<# Begin Activate script --------------------------------------------------- #>
+
+# Determine the containing directory of this script
+$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition
+$VenvExecDir = Get-Item -Path $VenvExecPath
+
+Write-Verbose "Activation script is located in path: '$VenvExecPath'"
+Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)"
+Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)"
+
+# Set values required in priority: CmdLine, ConfigFile, Default
+# First, get the location of the virtual environment, it might not be
+# VenvExecDir if specified on the command line.
+if ($VenvDir) {
+    Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values"
+}
+else {
+    Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir."
+    $VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/")
+    Write-Verbose "VenvDir=$VenvDir"
+}
+
+# Next, read the `pyvenv.cfg` file to determine any required value such
+# as `prompt`.
+$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir
+
+# Next, set the prompt from the command line, or the config file, or
+# just use the name of the virtual environment folder.
+if ($Prompt) {
+    Write-Verbose "Prompt specified as argument, using '$Prompt'"
+}
+else {
+    Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value"
+    if ($pyvenvCfg -and $pyvenvCfg['prompt']) {
+        Write-Verbose "  Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'"
+        $Prompt = $pyvenvCfg['prompt'];
+    }
+    else {
+        Write-Verbose "  Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virtual environment)"
+        Write-Verbose "  Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'"
+        $Prompt = Split-Path -Path $venvDir -Leaf
+    }
+}
+
+Write-Verbose "Prompt = '$Prompt'"
+Write-Verbose "VenvDir='$VenvDir'"
+
+# Deactivate any currently active virtual environment, but leave the
+# deactivate function in place.
+deactivate -nondestructive
+
+# Now set the environment variable VIRTUAL_ENV, used by many tools to determine
+# that there is an activated venv.
+$env:VIRTUAL_ENV = $VenvDir
+
+if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) {
+
+    Write-Verbose "Setting prompt to '$Prompt'"
+
+    # Set the prompt to include the env name
+    # Make sure _OLD_VIRTUAL_PROMPT is global
+    function global:_OLD_VIRTUAL_PROMPT { "" }
+    Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT
+    New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt
+
+    function global:prompt {
+        Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) "
+        _OLD_VIRTUAL_PROMPT
+    }
+    $env:VIRTUAL_ENV_PROMPT = $Prompt
+}
+
+# Clear PYTHONHOME
+if (Test-Path -Path Env:PYTHONHOME) {
+    Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME
+    Remove-Item -Path Env:PYTHONHOME
+}
+
+# Add the venv to the PATH
+Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH
+$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH"

ant_colony_env/bin/activate

@@ -0,0 +1,69 @@
+# This file must be used with "source bin/activate" *from bash*
+# you cannot run it directly
+
+deactivate () {
+    # reset old environment variables
+    if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then
+        PATH="${_OLD_VIRTUAL_PATH:-}"
+        export PATH
+        unset _OLD_VIRTUAL_PATH
+    fi
+    if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then
+        PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}"
+        export PYTHONHOME
+        unset _OLD_VIRTUAL_PYTHONHOME
+    fi
+
+    # This should detect bash and zsh, which have a hash command that must
+    # be called to get it to forget past commands.  Without forgetting
+    # past commands the $PATH changes we made may not be respected
+    if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then
+        hash -r 2> /dev/null
+    fi
+
+    if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then
+        PS1="${_OLD_VIRTUAL_PS1:-}"
+        export PS1
+        unset _OLD_VIRTUAL_PS1
+    fi
+
+    unset VIRTUAL_ENV
+    unset VIRTUAL_ENV_PROMPT
+    if [ ! "${1:-}" = "nondestructive" ] ; then
+    # Self destruct!
+        unset -f deactivate
+    fi
+}
+
+# unset irrelevant variables
+deactivate nondestructive
+
+VIRTUAL_ENV=/home/trim/Documents/GitHub/cognitive/ant_colony_env
+export VIRTUAL_ENV
+
+_OLD_VIRTUAL_PATH="$PATH"
+PATH="$VIRTUAL_ENV/"bin":$PATH"
+export PATH
+
+# unset PYTHONHOME if set
+# this will fail if PYTHONHOME is set to the empty string (which is bad anyway)
+# could use `if (set -u; : $PYTHONHOME) ;` in bash
+if [ -n "${PYTHONHOME:-}" ] ; then
+    _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}"
+    unset PYTHONHOME
+fi
+
+if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then
+    _OLD_VIRTUAL_PS1="${PS1:-}"
+    PS1='(ant_colony_env) '"${PS1:-}"
+    export PS1
+    VIRTUAL_ENV_PROMPT='(ant_colony_env) '
+    export VIRTUAL_ENV_PROMPT
+fi
+
+# This should detect bash and zsh, which have a hash command that must
+# be called to get it to forget past commands.  Without forgetting
+# past commands the $PATH changes we made may not be respected
+if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then
+    hash -r 2> /dev/null
+fi

ant_colony_env/bin/activate.csh

@@ -0,0 +1,26 @@
+# This file must be used with "source bin/activate.csh" *from csh*.
+# You cannot run it directly.
+# Created by Davide Di Blasi <davidedb@gmail.com>.
+# Ported to Python 3.3 venv by Andrew Svetlov <andrew.svetlov@gmail.com>
+
+alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; unsetenv VIRTUAL_ENV_PROMPT; test "\!:*" != "nondestructive" && unalias deactivate'
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+setenv VIRTUAL_ENV /home/trim/Documents/GitHub/cognitive/ant_colony_env
+
+set _OLD_VIRTUAL_PATH="$PATH"
+setenv PATH "$VIRTUAL_ENV/"bin":$PATH"
+
+
+set _OLD_VIRTUAL_PROMPT="$prompt"
+
+if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then
+    set prompt = '(ant_colony_env) '"$prompt"
+    setenv VIRTUAL_ENV_PROMPT '(ant_colony_env) '
+endif
+
+alias pydoc python -m pydoc
+
+rehash

ant_colony_env/bin/activate.fish

@@ -0,0 +1,69 @@
+# This file must be used with "source <venv>/bin/activate.fish" *from fish*
+# (https://fishshell.com/); you cannot run it directly.
+
+function deactivate  -d "Exit virtual environment and return to normal shell environment"
+    # reset old environment variables
+    if test -n "$_OLD_VIRTUAL_PATH"
+        set -gx PATH $_OLD_VIRTUAL_PATH
+        set -e _OLD_VIRTUAL_PATH
+    end
+    if test -n "$_OLD_VIRTUAL_PYTHONHOME"
+        set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME
+        set -e _OLD_VIRTUAL_PYTHONHOME
+    end
+
+    if test -n "$_OLD_FISH_PROMPT_OVERRIDE"
+        set -e _OLD_FISH_PROMPT_OVERRIDE
+        # prevents error when using nested fish instances (Issue #93858)
+        if functions -q _old_fish_prompt
+            functions -e fish_prompt
+            functions -c _old_fish_prompt fish_prompt
+            functions -e _old_fish_prompt
+        end
+    end
+
+    set -e VIRTUAL_ENV
+    set -e VIRTUAL_ENV_PROMPT
+    if test "$argv[1]" != "nondestructive"
+        # Self-destruct!
+        functions -e deactivate
+    end
+end
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+set -gx VIRTUAL_ENV /home/trim/Documents/GitHub/cognitive/ant_colony_env
+
+set -gx _OLD_VIRTUAL_PATH $PATH
+set -gx PATH "$VIRTUAL_ENV/"bin $PATH
+
+# Unset PYTHONHOME if set.
+if set -q PYTHONHOME
+    set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME
+    set -e PYTHONHOME
+end
+
+if test -z "$VIRTUAL_ENV_DISABLE_PROMPT"
+    # fish uses a function instead of an env var to generate the prompt.
+
+    # Save the current fish_prompt function as the function _old_fish_prompt.
+    functions -c fish_prompt _old_fish_prompt
+
+    # With the original prompt function renamed, we can override with our own.
+    function fish_prompt
+        # Save the return status of the last command.
+        set -l old_status $status
+
+        # Output the venv prompt; color taken from the blue of the Python logo.
+        printf "%s%s%s" (set_color 4B8BBE) '(ant_colony_env) ' (set_color normal)
+
+        # Restore the return status of the previous command.
+        echo "exit $old_status" | .
+        # Output the original/"old" prompt.
+        _old_fish_prompt
+    end
+
+    set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV"
+    set -gx VIRTUAL_ENV_PROMPT '(ant_colony_env) '
+end

ant_colony_env/bin/ant-colony

@@ -0,0 +1,33 @@
+#!/home/trim/Documents/GitHub/cognitive/ant_colony_env/bin/python3
+# EASY-INSTALL-ENTRY-SCRIPT: 'ant-colony','console_scripts','ant-colony'
+import re
+import sys
+
+# for compatibility with easy_install; see #2198
+__requires__ = 'ant-colony'
+
+try:
+    from importlib.metadata import distribution
+except ImportError:
+    try:
+        from importlib_metadata import distribution
+    except ImportError:
+        from pkg_resources import load_entry_point
+
+
+def importlib_load_entry_point(spec, group, name):
+    dist_name, _, _ = spec.partition('==')
+    matches = (
+        entry_point
+        for entry_point in distribution(dist_name).entry_points
+        if entry_point.group == group and entry_point.name == name
+    )
+    return next(matches).load()
+
+
+globals().setdefault('load_entry_point', importlib_load_entry_point)
+
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(load_entry_point('ant-colony', 'console_scripts', 'ant-colony')())

ant_colony_env/bin/f2py

@@ -0,0 +1,8 @@
+#!/home/trim/Documents/GitHub/cognitive/ant_colony_env/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from numpy.f2py.f2py2e import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

ant_colony_env/bin/fonttools

@@ -0,0 +1,8 @@
+#!/home/trim/Documents/GitHub/cognitive/ant_colony_env/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from fontTools.__main__ import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

ant_colony_env/bin/numpy-config

@@ -0,0 +1,8 @@
+#!/home/trim/Documents/GitHub/cognitive/ant_colony_env/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from numpy._configtool import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

ant_colony_env/bin/pip

@@ -0,0 +1,8 @@
+#!/home/trim/Documents/GitHub/cognitive/ant_colony_env/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from pip._internal.cli.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

ant_colony_env/bin/pip3

@@ -0,0 +1,8 @@
+#!/home/trim/Documents/GitHub/cognitive/ant_colony_env/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from pip._internal.cli.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

ant_colony_env/bin/pip3.11

@@ -0,0 +1,8 @@
+#!/home/trim/Documents/GitHub/cognitive/ant_colony_env/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from pip._internal.cli.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

ant_colony_env/bin/pyftmerge

@@ -0,0 +1,8 @@
+#!/home/trim/Documents/GitHub/cognitive/ant_colony_env/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from fontTools.merge import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

ant_colony_env/bin/pyftsubset

@@ -0,0 +1,8 @@
+#!/home/trim/Documents/GitHub/cognitive/ant_colony_env/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from fontTools.subset import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

ant_colony_env/bin/python

@@ -0,0 +1 @@
+python3

ant_colony_env/bin/python3

@@ -0,0 +1 @@
+/usr/bin/python3

ant_colony_env/bin/python3.11

@@ -0,0 +1 @@
+python3

ant_colony_env/bin/ttx

@@ -0,0 +1,8 @@
+#!/home/trim/Documents/GitHub/cognitive/ant_colony_env/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from fontTools.ttx import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

ant_colony_env/lib64

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+lib

ant_colony_env/pyvenv.cfg

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+home = /usr/bin
+include-system-site-packages = false
+version = 3.11.2
+executable = /usr/bin/python3.11
+command = /home/trim/Documents/GitHub/cognitive/ant_colony_env/bin/python3 -m venv /home/trim/Documents/GitHub/cognitive/ant_colony_env

ant_colony_env/share/man/man1/ttx.1

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+.Dd May 18, 2004
+.\" ttx is not specific to any OS, but contrary to what groff_mdoc(7)
+.\" seems to imply, entirely omitting the .Os macro causes 'BSD' to
+.\" be used, so I give a zero-width space as its argument.
+.Os \&
+.\" The "FontTools Manual" argument apparently has no effect in
+.\" groff 1.18.1. I think it is a bug in the -mdoc groff package.
+.Dt TTX 1 "FontTools Manual"
+.Sh NAME
+.Nm ttx
+.Nd tool for manipulating TrueType and OpenType fonts
+.Sh SYNOPSIS
+.Nm
+.Bk
+.Op Ar option ...
+.Ek
+.Bk
+.Ar file ...
+.Ek
+.Sh DESCRIPTION
+.Nm
+is a tool for manipulating TrueType and OpenType fonts.  It can convert
+TrueType and OpenType fonts to and from an
+.Tn XML Ns -based format called
+.Tn TTX .
+.Tn TTX
+files have a
+.Ql .ttx
+extension.
+.Pp
+For each
+.Ar file
+argument it is given,
+.Nm
+detects whether it is a
+.Ql .ttf ,
+.Ql .otf
+or
+.Ql .ttx
+file and acts accordingly: if it is a
+.Ql .ttf
+or
+.Ql .otf
+file, it generates a
+.Ql .ttx
+file; if it is a
+.Ql .ttx
+file, it generates a
+.Ql .ttf
+or
+.Ql .otf
+file.
+.Pp
+By default, every output file is created in the same directory as the
+corresponding input file and with the same name except for the
+extension, which is substituted appropriately.
+.Nm
+never overwrites existing files; if necessary, it appends a suffix to
+the output file name before the extension, as in
+.Pa Arial#1.ttf .
+.Ss "General options"
+.Bl -tag -width ".Fl t Ar table"
+.It Fl h
+Display usage information.
+.It Fl d Ar dir
+Write the output files to directory
+.Ar dir
+instead of writing every output file to the same directory as the
+corresponding input file.
+.It Fl o Ar file
+Write the output to
+.Ar file
+instead of writing it to the same directory as the
+corresponding input file.
+.It Fl v
+Be verbose.  Write more messages to the standard output describing what
+is being done.
+.It Fl a
+Allow virtual glyphs ID's on compile or decompile.
+.El
+.Ss "Dump options"
+The following options control the process of dumping font files
+(TrueType or OpenType) to
+.Tn TTX
+files.
+.Bl -tag -width ".Fl t Ar table"
+.It Fl l
+List table information.  Instead of dumping the font to a
+.Tn TTX
+file, display minimal information about each table.
+.It Fl t Ar table
+Dump table
+.Ar table .
+This option may be given multiple times to dump several tables at
+once.  When not specified, all tables are dumped.
+.It Fl x Ar table
+Exclude table
+.Ar table
+from the list of tables to dump.  This option may be given multiple
+times to exclude several tables from the dump.  The
+.Fl t
+and
+.Fl x
+options are mutually exclusive.
+.It Fl s
+Split tables.  Dump each table to a separate
+.Tn TTX
+file and write (under the name that would have been used for the output
+file if the
+.Fl s
+option had not been given) one small
+.Tn TTX
+file containing references to the individual table dump files.  This
+file can be used as input to
+.Nm
+as long as the referenced files can be found in the same directory.
+.It Fl i
+.\" XXX: I suppose OpenType programs (exist and) are also affected.
+Don't disassemble TrueType instructions.  When this option is specified,
+all TrueType programs (glyph programs, the font program and the
+pre-program) are written to the
+.Tn TTX
+file as hexadecimal data instead of
+assembly.  This saves some time and results in smaller
+.Tn TTX
+files.
+.It Fl y Ar n
+When decompiling a TrueType Collection (TTC) file,
+decompile font number
+.Ar n ,
+starting from 0.
+.El
+.Ss "Compilation options"
+The following options control the process of compiling
+.Tn TTX
+files into font files (TrueType or OpenType):
+.Bl -tag -width ".Fl t Ar table"
+.It Fl m Ar fontfile
+Merge the input
+.Tn TTX
+file
+.Ar file
+with
+.Ar fontfile .
+No more than one
+.Ar file
+argument can be specified when this option is used.
+.It Fl b
+Don't recalculate glyph bounding boxes.  Use the values in the
+.Tn TTX
+file as is.
+.El
+.Sh "THE TTX FILE FORMAT"
+You can find some information about the
+.Tn TTX
+file format in
+.Pa documentation.html .
+In particular, you will find in that file the list of tables understood by
+.Nm
+and the relations between TrueType GlyphIDs and the glyph names used in
+.Tn TTX
+files.
+.Sh EXAMPLES
+In the following examples, all files are read from and written to the
+current directory.  Additionally, the name given for the output file
+assumes in every case that it did not exist before
+.Nm
+was invoked.
+.Pp
+Dump the TrueType font contained in
+.Pa FreeSans.ttf
+to
+.Pa FreeSans.ttx :
+.Pp
+.Dl ttx FreeSans.ttf
+.Pp
+Compile
+.Pa MyFont.ttx
+into a TrueType or OpenType font file:
+.Pp
+.Dl ttx MyFont.ttx
+.Pp
+List the tables in
+.Pa FreeSans.ttf
+along with some information:
+.Pp
+.Dl ttx -l FreeSans.ttf
+.Pp
+Dump the
+.Sq cmap
+table from
+.Pa FreeSans.ttf
+to
+.Pa FreeSans.ttx :
+.Pp
+.Dl ttx -t cmap FreeSans.ttf
+.Sh NOTES
+On MS\-Windows and MacOS,
+.Nm
+is available as a graphical application to which files can be dropped.
+.Sh SEE ALSO
+.Pa documentation.html
+.Pp
+.Xr fontforge 1 ,
+.Xr ftinfo 1 ,
+.Xr gfontview 1 ,
+.Xr xmbdfed 1 ,
+.Xr Font::TTF 3pm
+.Sh AUTHORS
+.Nm
+was written by
+.An -nosplit
+.An "Just van Rossum" Aq just@letterror.com .
+.Pp
+This manual page was written by
+.An "Florent Rougon" Aq f.rougon@free.fr
+for the Debian GNU/Linux system based on the existing FontTools
+documentation.  It may be freely used, modified and distributed without
+restrictions.
+.\" For Emacs:
+.\" Local Variables:
+.\" fill-column: 72
+.\" sentence-end: "[.?!][]\"')}]*\\($\\| $\\|   \\|  \\)[   \n]*"
+.\" sentence-end-double-space: t
+.\" End: