import random as rd

class Particle():
    def __init__(self, nb_vehicles:int=10, delta_t:int=60, sim_duration:int=4320, a_min=-100, a_max=100, alpha=0.1):
        # Problem specific attributes
        self.nb_vehicles = nb_vehicles # Number of vehicles handles for the generations of position x
        self.delta_t = delta_t # delta_t for update purposes
        self.sim_duration = sim_duration # max duration and number of updates (multiplied by delta_time)

        self.socs= self.generate_state_of_charges() # States of charge (initial, requested)
        self.times = self.generate_times() # Times (arrived, leaving)

        # Minima and maxima of a position value
        self.a_min = a_min
        self.a_max = a_max

        # Limitation of the velocity
        self.alpha = alpha
        self.r1 = [rd.randrange(0,101,1)/100 for _ in range(self.nb_vehicles)] # Variable trust of oneself
        self.r2 = [rd.randrange(0,101,1)/100 for _ in range(self.nb_vehicles)] # Variable trust of other particles

        # Particle attributes
        self.x = self.generate_position() # Position Vector (correspond to one solution for the problem)
        self.v = self.generate_velocity() # Velocity
        self.p_best = self.x # Best known position (starting with initial position x)

    def update_position(self):
        for i in range(self.nb_vehicles):
            new_pos_i = self.x[i] + self.v[i]
            self.x[i] = new_pos_i

    def update_velocity(self, leader, c1, c2, w=0.4):
        for i in range(self.nb_vehicles):
            new_vel_i = w * self.v[i] + (self.p_best - self.x[i]) * c1 * self.r1[i] + (leader - self.x[i]) * c2 * self.r2[i]
            self.v[i] = new_vel_i

    def generate_state_of_charges(self):
        socs = []
        # We ensure soc_req is greater than what the soc_init is (percentage transformed into floats)
        for _ in range(self.nb_vehicles):
            soc_init = rd.randrange(0,100,1)
            soc_req = rd.randrange(soc_init+1, 101,1)
            socs.append((soc_init/100, soc_req/100))
        return socs
    
    def generate_times(self):
        times = []
        for _ in range(self.nb_vehicles):
            # Minumun, we have one tick of charging during simulation
            t_arrived = rd.randrange(0, (self.sim_duration - self.delta_time) +1, self.delta_time)
            t_leaving = rd.randrange(t_arrived + self.delta_time, self.sim_duration+1, self.delta_time)
            times.append((t_arrived,t_leaving))
        return times
    
    def generate_position(self):
        pos = []
        for _ in range(self.nb_vehicles):
            pos.append(rd.randrange(self.a_min, self.a_max +1, 1))
        return pos
    
    def generate_velocity(self):
        vel = []
        vel_coeff = self.a_max - self.a_min
        for _ in range(self.nb_vehicles):
            vel.append(rd.randrange(-vel_coeff, vel_coeff +1, 1) * self.alpha)
        return vel