import numpy as np

from .electric_motor import ElectricMotor


class DcMotor(ElectricMotor):
    """
    The DcMotor and its subclasses implement the technical system of a dc motor.

    This includes the system equations, the motor parameters of the equivalent circuit diagram,
    as well as limits.


    =====================  ==========  ============= ===========================================
    Motor Parameter        Unit        Default Value Description
    =====================  ==========  ============= ===========================================
    r_a                    Ohm         16e-3         Armature circuit resistance
    r_e                    Ohm         16e-2         Exciting circuit resistance
    l_a                    H           19e-6         Armature circuit inductance
    l_e                    H           5.4e-3        Exciting circuit inductance
    l_e_prime              H           1.7e-3        Effective excitation inductance
    j_rotor                kg/m^2      0.025         Moment of inertia of the rotor
    =====================  ==========  ============= ===========================================

    ..note ::
        The motor parameter are based on the following DC Motor (slightly adapted):
        https://www.heinzmann-electric-motors.com/en/products/dc-motors/pmg-132-dc-motor

    =============== ====== =============================================
    Motor Currents  Unit   Description
    =============== ====== =============================================
    i_a             A      Armature circuit current
    i_e             A      Exciting circuit current
    =============== ====== =============================================
    =============== ====== =============================================
    Motor Voltages  Unit   Description
    =============== ====== =============================================
    u_a             V      Armature circuit voltage
    u_e             v      Exciting circuit voltage
    =============== ====== =============================================

    ======== ===========================================================
    Limits / Nominal Value Dictionary Entries:
    -------- -----------------------------------------------------------
    Entry    Description
    ======== ===========================================================
    i_a      Armature current
    i_e      Exciting current
    omega    Angular Velocity
    torque   Motor generated torque
    u_a      Armature Voltage
    u_e      Exciting Voltage
    ======== ===========================================================
    """

    # Indices for array accesses
    I_A_IDX = 0
    I_E_IDX = 1
    CURRENTS_IDX = [0, 1]
    CURRENTS = ["i_a", "i_e"]
    VOLTAGES = ["u_a", "u_e"]

    # Motor parameter, nominal values and limits are based on the following DC Motor:
    # https://www.heinzmann-electric-motors.com/en/products/dc-motors/pmg-132-dc-motor
    _default_motor_parameter = {
        "r_a": 16e-3,
        "r_e": 16e-2,
        "l_a": 19e-6,
        "l_e_prime": 1.7e-3,
        "l_e": 5.4e-3,
        "j_rotor": 0.0025,
    }
    _default_nominal_values = dict(omega=300, torque=16.0, i=97, i_a=97, i_e=97, u=60, u_a=60, u_e=60)
    _default_limits = dict(omega=400, torque=38.0, i=210, i_a=210, i_e=210, u=60, u_a=60, u_e=60)
    _default_initializer = {
        "states": {"i_a": 0.0, "i_e": 0.0},
        "interval": None,
        "random_init": None,
        "random_params": (None, None),
    }

    def __init__(
        self,
        motor_parameter=None,
        nominal_values=None,
        limit_values=None,
        motor_initializer=None,
    ):
        # Docstring of superclass
        super().__init__(motor_parameter, nominal_values, limit_values, motor_initializer)
        #: Matrix that contains the constant parameters of the systems equation for faster computation
        self._model_constants = None
        self._update_model()
        self._update_limits()

    def _update_model(self):
        """Updates the motor's model parameters with the motor parameters.

        Called internally when the motor parameters are changed or the motor is initialized.
        """
        mp = self._motor_parameter
        self._model_constants = np.array([[-mp["r_a"], 0, -mp["l_e_prime"], 1, 0], [0, -mp["r_e"], 0, 0, 1]])
        self._model_constants[self.I_A_IDX] = self._model_constants[self.I_A_IDX] / mp["l_a"]
        self._model_constants[self.I_E_IDX] = self._model_constants[self.I_E_IDX] / mp["l_e"]

    def torque(self, currents):
        # Docstring of superclass
        return self._motor_parameter["l_e_prime"] * currents[self.I_A_IDX] * currents[self.I_E_IDX]

    def i_in(self, currents):
        # Docstring of superclass
        return list(currents)

    def electrical_ode(self, state, u_in, omega, *_):
        # Docstring of superclass
        return np.matmul(
            self._model_constants,
            np.array(
                [
                    state[self.I_A_IDX],
                    state[self.I_E_IDX],
                    omega * state[self.I_E_IDX],
                    u_in[0],
                    u_in[1],
                ]
            ),
        )

    def get_state_space(self, input_currents, input_voltages):
        """
        Calculate the possible normalized state space for the motor as a tuple of dictionaries "low" and "high".

        Args:
            input_currents: Tuple of the two converters possible output currents.
            input_voltages: Tuple of the two converters possible output voltages.

        Returns:
             tuple(dict,dict): Dictionaries defining if positive and negative values are possible for each motors state.
        """
        a_converter = 0
        e_converter = 1
        low = {
            "omega": -1 if input_voltages.low[a_converter] == -1 or input_voltages.low[e_converter] == -1 else 0,
            "torque": -1 if input_currents.low[a_converter] == -1 or input_currents.low[e_converter] == -1 else 0,
            "i_a": -1 if input_currents.low[a_converter] == -1 else 0,
            "i_e": -1 if input_currents.low[e_converter] == -1 else 0,
            "u_a": -1 if input_voltages.low[a_converter] == -1 else 0,
            "u_e": -1 if input_voltages.low[e_converter] == -1 else 0,
        }
        high = {"omega": 1, "torque": 1, "i_a": 1, "i_e": 1, "u_a": 1, "u_e": 1}
        return low, high

    def _update_limits(self, limits_d=None, nominal_d=None):
        # Docstring of superclass
        if limits_d is None:
            limits_d = dict()

        # torque is replaced the same way for all DC motors
        limits_d.update(dict(torque=self.torque([self._limits[state] for state in self.CURRENTS])))
        super()._update_limits(limits_d)
