import random as rd

class Particle():
    def __init__(self, nb_vehicles:int=10, delta_t:int=60, nb_of_ticks:int=72, 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.nb_of_ticks = nb_of_ticks # Accounting for time evolution of the solution (multiplied by delta_t)

        self.socs= self.generate_state_of_charges() # States of charge (initial, requested)

        #TODO: Move that to MOPSO (using one batch of times for multiples particles)
        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)
        self.eval = 0 #TODO: self.evaluate()

    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.nb_of_ticks * self.delta_t - self.delta_t) +1, self.delta_t)
            t_leaving = rd.randrange(t_arrived + self.delta_t, (self.nb_of_ticks*self.delta_t)+1, self.delta_t)
            times.append((t_arrived,t_leaving))
        return times
    
    #TODO: Modify for uses of ticks
    def generate_position(self):
        pos = []
        for _ in range(self.nb_of_ticks):
            x_tick = []
            for _ in range(self.nb_vehicles):
                x_tick.append(rd.randrange(self.a_min, self.a_max +1, 1))
            pos.append(x_tick)
        return pos
    
    def generate_velocity(self):
        vel = []
        vel_coeff = self.a_max - self.a_min
        for _ in range(self.nb_of_ticks):
            v_tick = []
            for _ in range(self.nb_vehicles):
                v_tick.append(rd.randrange(-vel_coeff, vel_coeff +1, 1) * self.alpha)
            vel.append(v_tick)
        return vel
    
    # Function objective
    def evaluate(self, elec_prices, max_power):
        pass

    # Calculate the price of the electricity consumption in the grid SUM(1_to_T)(Epsilon_t * A_t * delta_t)
    def f1(self, elec_prices):
        result = 0
        for tick in range(self.nb_of_ticks):
            grid_stress_tick = self.get_current_grid_stress(tick)
            result += elec_prices[tick] * grid_stress_tick * self.delta_t
        return result
    
    #TODO: Modify for uses of ticks
    # User's insatisfaction
    def f2(self):
        result = 0
        for i in range(self.nb_vehicles):
            soc_req_i = self.socs[i][1]
            result += max(0, )

    # Network Stress
    def f3(self):
        current_max = 0
        for tick in range(self.nb_of_ticks):
            current_max = max(current_max, self.get_current_grid_stress(tick))
        return current_max
    
    #TODO: Modify for uses of ticks
    def get_current_grid_stress(self, tick:int):
        assert tick < self.nb_of_ticks # Make sure the tick exist in the position x
        current_grid_stress = 0
        for i in range(self.nb_vehicles):
            current_grid_stress += self.x[tick][i]
        return current_grid_stress