import numpy as np
from cereal import log
from opendbc.car.vehicle_model import ACCELERATION_DUE_TO_GRAVITY
from openpilot.common.realtime import DT_CTRL, DT_MDL

MIN_SPEED = 1.0
CONTROL_N = 17
CAR_ROTATION_RADIUS = 0.0
# This is a turn radius smaller than most cars can achieve
MAX_CURVATURE = 0.2
MAX_VEL_ERR = 5.0  # m/s

# EU guidelines
MAX_LATERAL_JERK = 5.0  # m/s^3
MAX_LATERAL_ACCEL_NO_ROLL = 3.0  # m/s^2


def clamp(val, min_val, max_val):
  clamped_val = float(np.clip(val, min_val, max_val))
  return clamped_val, clamped_val != val

def smooth_value(val, prev_val, tau):
  alpha = 1 - np.exp(-DT_MDL / tau) if tau > 0 else 1
  return alpha * val + (1 - alpha) * prev_val

def clip_curvature(v_ego, prev_curvature, new_curvature, roll):
  # This function respects ISO lateral jerk and acceleration limits + a max curvature
  v_ego = max(v_ego, MIN_SPEED)
  max_curvature_rate = MAX_LATERAL_JERK / (v_ego ** 2)  # inexact calculation, check https://github.com/commaai/openpilot/pull/24755
  new_curvature = np.clip(new_curvature,
                          prev_curvature - max_curvature_rate * DT_CTRL,
                          prev_curvature + max_curvature_rate * DT_CTRL)

  roll_compensation = roll * ACCELERATION_DUE_TO_GRAVITY
  max_lat_accel = MAX_LATERAL_ACCEL_NO_ROLL + roll_compensation
  min_lat_accel = -MAX_LATERAL_ACCEL_NO_ROLL + roll_compensation
  new_curvature, limited_accel = clamp(new_curvature, min_lat_accel / v_ego ** 2, max_lat_accel / v_ego ** 2)

  new_curvature, limited_max_curv = clamp(new_curvature, -MAX_CURVATURE, MAX_CURVATURE)
  return float(new_curvature), limited_accel or limited_max_curv


def get_speed_error(modelV2: log.ModelDataV2, v_ego: float) -> float:
  # ToDo: Try relative error, and absolute speed
  if len(modelV2.temporalPose.trans):
    vel_err = np.clip(modelV2.temporalPose.trans[0] - v_ego, -MAX_VEL_ERR, MAX_VEL_ERR)
    return float(vel_err)
  return 0.0


def get_accel_from_plan(speeds, accels, t_idxs, action_t=DT_MDL, vEgoStopping=0.05):
  if len(speeds) == len(t_idxs):
    v_now = speeds[0]
    a_now = accels[0]
    v_target = np.interp(action_t, t_idxs, speeds)
    a_target = 2 * (v_target - v_now) / (action_t) - a_now
    v_target_1sec = np.interp(action_t + 1.0, t_idxs, speeds)
  else:
    v_target = 0.0
    v_target_1sec = 0.0
    a_target = 0.0
  should_stop = (v_target < vEgoStopping and
                 v_target_1sec < vEgoStopping)
  return a_target, should_stop