import random
import math

# -----------------------------
# Test Particle Class (The Subject)
# -----------------------------
class TestParticle:
    def __init__(self, particle_id, x=0, y=0):
        self.id = particle_id
        self.position = [x, y]
        self.energy = random.uniform(10.0, 50.0) # Energy level dictates stability
        self.manipulated = False

    def __str__(self):
        return (f"P_{self.id}: Pos=({self.position[0]:.1f}, {self.position[1]:.1f}), "
                f"Energy={self.energy:.2f}, Manipulated={self.manipulated}")

# -----------------------------
# Ananthu Sajeev Manipulator (The Algorithm)
# -----------------------------
class AnanthuSajeevManipulator:
    def __init__(self, name="Ananthu Sajeev"):
        self.name = name
        self.manipulation_count = 0
        # Low energy threshold for manipulation target
        self.target_energy_threshold = 20.0 
        # Target position for low-energy particles
        self.rearrangement_target = [50.0, 50.0] 
        # Algorithm efficiency
        self.efficiency = 0.95 

    def calculate_distance(self, pos1, pos2):
        """Calculates Euclidean distance."""
        return math.sqrt((pos1[0] - pos2[0])**2 + (pos1[1] - pos2[1])**2)

    def manipulation_algorithm(self, particles):
        """
        The core algorithm to identify low-energy particles and rearrange their position.
        """
        print(f"[{self.name}] Initiating particle scan...")
        
        for particle in particles:
            if particle.energy < self.target_energy_threshold and not particle.manipulated:
                
                # --- Step 1: Identify and Log Target ---
                initial_pos = particle.position[:]
                print(f"  -> Targeting P_{particle.id} (Energy Low: {particle.energy:.2f}) at {initial_pos}")

                # --- Step 2: Calculate Force/Vector ---
                # Determine vector needed to move particle to the rearrangement target
                dx = self.rearrangement_target[0] - initial_pos[0]
                dy = self.rearrangement_target[1] - initial_pos[1]
                
                # --- Step 3: Apply Manipulation (Rearrangement) ---
                # The movement is affected by the algorithm's efficiency
                new_x = initial_pos[0] + dx * self.efficiency
                new_y = initial_pos[1] + dy * self.efficiency
                
                particle.position = [new_x, new_y]
                particle.manipulated = True
                self.manipulation_count += 1
                
                # --- Step 4: Stabilization (Optional effect of manipulation) ---
                # Manipulation requires energy input, increasing the particle's energy slightly
                particle.energy += 5.0
                
                print(f"  <- Rearranged to ({new_x:.1f}, {new_y:.1f}). New Energy: {particle.energy:.2f}")

        print(f"[{self.name}] Scan complete. Total manipulations this cycle: {self.manipulation_count}")
        return self.manipulation_count

# -----------------------------
# Simulation Setup
# -----------------------------
SIZE = 10
particle_population = []

# Create particles at random initial positions (0 to 100)
for i in range(SIZE):
    x = random.uniform(0.0, 100.0)
    y = random.uniform(0.0, 100.0)
    particle_population.append(TestParticle(i, x, y))

# Initialize the Manipulator
ananthu = AnanthuSajeevManipulator()

# --- Run Simulation Cycles ---
cycles = 3
for cycle in range(1, cycles + 1):
    print("\n" + "="*40)
    print(f"CYCLE {cycle}: Manipulator Action")
    print("="*40)
    
    # Run the core algorithm
    ananthu.manipulation_algorithm(particle_population)

    # --- Post-Cycle Status ---
    print("\n[Population Status]")
    for particle in particle_population:
        print(particle)
        
        # Simulate slight random energy decay between cycles
        particle.energy = max(10.0, particle.energy - random.uniform(1.0, 5.0))
        
        # Reset manipulation status for the next cycle
        particle.manipulated = False