import numpy as np

from openpilot.common.numpy_fast import interp
from openpilot.common.params import Params
from openpilot.system.hardware import HARDWARE


params_memory = Params("/dev/shm/params")

CITY_SPEED_LIMIT = 25
CRUISING_SPEED = 5  # Roughly the speed cars go when not touching the gas while in drive
PROBABILITY = 0.6   # 60% chance of condition being true
THRESHOLD = 5       # Time threshold (0.25s)

# Acceleration profiles - Credit goes to the DragonPilot team!
                 # MPH = [0., 18,  36,  63,  94]
A_CRUISE_MIN_BP_CUSTOM = [0., 8., 16., 28., 42.]
                 # MPH = [0., 6.71, 13.4, 17.9, 24.6, 33.6, 44.7, 55.9, 67.1, 123]
A_CRUISE_MAX_BP_CUSTOM = [0.,    3,   6.,   8.,  11.,  15.,  20.,  25.,  30., 55.]

A_CRUISE_MIN_VALS_ECO = [-0.001, -0.010, -0.28, -0.56, -0.56]
A_CRUISE_MAX_VALS_ECO = [3.5, 3.2, 2.3, 2.0, 1.15, .80, .58, .36, .30, .091]

A_CRUISE_MIN_VALS_SPORT = [-0.50, -0.52, -0.55, -0.57, -0.60]
A_CRUISE_MAX_VALS_SPORT = [3.5, 3.5, 3.3, 2.8, 1.5, 1.0, .75, .6, .38, .2]


class MovingAverageCalculator:
  def __init__(self):
    self.data = []
    self.total = 0

  def add_data(self, value):
    if len(self.data) == THRESHOLD:
      self.total -= self.data.pop(0)
    self.data.append(value)
    self.total += value

  def get_moving_average(self):
    if len(self.data) == 0:
      return None
    return self.total / len(self.data)

  def reset_data(self):
    self.data = []
    self.total = 0


class FrogPilotFunctions:
  def __init__(self) -> None:
    self.params = Params()

  @staticmethod
  def get_min_accel_eco(v_ego):
    return interp(v_ego, A_CRUISE_MIN_BP_CUSTOM, A_CRUISE_MIN_VALS_ECO)

  @staticmethod
  def get_max_accel_eco(v_ego):
    return interp(v_ego, A_CRUISE_MAX_BP_CUSTOM, A_CRUISE_MAX_VALS_ECO)

  @staticmethod
  def get_min_accel_sport(v_ego):
    return interp(v_ego, A_CRUISE_MIN_BP_CUSTOM, A_CRUISE_MIN_VALS_SPORT)

  @staticmethod
  def get_max_accel_sport(v_ego):
    return interp(v_ego, A_CRUISE_MAX_BP_CUSTOM, A_CRUISE_MAX_VALS_SPORT)

  @staticmethod
  def calculate_lane_width(lane, current_lane, road_edge):
    lane_x, lane_y = np.array(lane.x), np.array(lane.y)
    edge_x, edge_y = np.array(road_edge.x), np.array(road_edge.y)
    current_x, current_y = np.array(current_lane.x), np.array(current_lane.y)

    lane_y_interp = np.interp(current_x, lane_x[lane_x.argsort()], lane_y[lane_x.argsort()])
    road_edge_y_interp = np.interp(current_x, edge_x[edge_x.argsort()], edge_y[edge_x.argsort()])

    distance_to_lane = np.mean(np.abs(current_y - lane_y_interp))
    distance_to_road_edge = np.mean(np.abs(current_y - road_edge_y_interp))

    return min(distance_to_lane, distance_to_road_edge)

  @staticmethod
  def update_cestatus_distance(self):
    # Set "CEStatus" to work with "Conditional Experimental Mode"
    conditional_status = params_memory.get_int("CEStatus")
    override_value = 0 if conditional_status in (1, 2, 3, 4, 5, 6) else 3 if conditional_status >= 7 else 4
    params_memory.put_int("CEStatus", override_value)

  @staticmethod
  def update_cestatus_lkas(self):
    # Set "CEStatus" to work with "Conditional Experimental Mode"
    conditional_status = params_memory.get_int("CEStatus")
    override_value = 0 if conditional_status in (1, 2, 3, 4, 5, 6) else 5 if conditional_status >= 7 else 6
    params_memory.put_int("CEStatus", override_value)

  def update_experimental_mode(self):
    experimental_mode = self.params.get_bool("ExperimentalMode")
    # Invert the value of "ExperimentalMode"
    self.params.put_bool("ExperimentalMode", not experimental_mode)

  @staticmethod
  def road_curvature(modelData, v_ego):
    predicted_velocities = np.array(modelData.velocity.x)
    curvature_ratios = np.abs(np.array(modelData.acceleration.y)) / (predicted_velocities**2)
    return np.amax(curvature_ratios * (v_ego**2))