import math
import logging
from collections import deque, namedtuple
from enum import Enum
import socket
import json
import time
import signal
from driver.kirdy_async import Kirdy
import asyncio

# Based on hirshmann pid-autotune libiary
# See https://github.com/hirschmann/pid-autotune
# Which is in turn based on a fork of Arduino PID AutoTune Library
# See https://github.com/t0mpr1c3/Arduino-PID-AutoTune-Library


class PIDAutotuneState(Enum):
    STATE_OFF = 'off'
    STATE_RELAY_STEP_UP = 'relay step up'
    STATE_RELAY_STEP_DOWN = 'relay step down'
    STATE_SUCCEEDED = 'succeeded'
    STATE_FAILED = 'failed'


class PIDAutotune:
    PIDParams = namedtuple('PIDParams', ['Kp', 'Ki', 'Kd'])

    PEAK_AMPLITUDE_TOLERANCE = 0.05

    _tuning_rules = {
        "ziegler-nichols": [0.6, 1.2, 0.075],
        "tyreus-luyben": [0.4545,  0.2066, 0.07214],
        "ciancone-marlin": [0.303, 0.1364, 0.0481],
        "pessen-integral": [0.7, 1.75, 0.105],
        "some-overshoot": [0.333, 0.667,  0.111],
        "no-overshoot": [0.2, 0.4,  0.0667]
    }

    def __init__(self, setpoint, out_step=10, lookback=60,
                 noiseband=0.5, sampletime=1.2):
        if setpoint is None:
            raise ValueError('setpoint must be specified')

        self._inputs = deque(maxlen=round(lookback / sampletime))
        self._setpoint = setpoint
        self._outputstep = out_step
        self._noiseband = noiseband
        self._out_min = -out_step
        self._out_max = out_step
        self._state = PIDAutotuneState.STATE_OFF
        self._peak_timestamps = deque(maxlen=5)
        self._peaks = deque(maxlen=5)
        self._output = 0
        self._last_run_timestamp = 0
        self._peak_type = 0
        self._peak_count = 0
        self._initial_output = 0
        self._induced_amplitude = 0
        self._Ku = 0
        self._Pu = 0

    def state(self):
        """Get the current state."""
        return self._state

    def output(self):
        """Get the last output value."""
        return self._output

    def tuning_rules(self):
        """Get a list of all available tuning rules."""
        return self._tuning_rules.keys()

    def get_pid_parameters(self, tuning_rule='ziegler-nichols'):
        """Get PID parameters.

        Args:
            tuning_rule (str): Sets the rule which should be used to calculate
                the parameters.
        """
        divisors = self._tuning_rules[tuning_rule]
        kp = self._Ku * divisors[0]
        ki = divisors[1] * self._Ku / self._Pu
        kd = divisors[2] * self._Ku * self._Pu
        return PIDAutotune.PIDParams(kp, ki, kd)

    def run(self, input_val, time_input):
        """To autotune a system, this method must be called periodically.

        Args:
            input_val (float): The temperature input value.
            time_input (float): Current time in seconds.

        Returns:
            `true` if tuning is finished, otherwise `false`.
        """
        now = time_input * 1000

        if (self._state == PIDAutotuneState.STATE_OFF
                or self._state == PIDAutotuneState.STATE_SUCCEEDED
                or self._state == PIDAutotuneState.STATE_FAILED):
            self._state = PIDAutotuneState.STATE_RELAY_STEP_UP

        self._last_run_timestamp = now

        # check input and change relay state if necessary
        if (self._state == PIDAutotuneState.STATE_RELAY_STEP_UP
                and input_val > self._setpoint + self._noiseband):
            self._state = PIDAutotuneState.STATE_RELAY_STEP_DOWN
            logging.debug('switched state: {0}'.format(self._state))
            logging.debug('input: {0}'.format(input_val))
        elif (self._state == PIDAutotuneState.STATE_RELAY_STEP_DOWN
                and input_val < self._setpoint - self._noiseband):
            self._state = PIDAutotuneState.STATE_RELAY_STEP_UP
            logging.debug('switched state: {0}'.format(self._state))
            logging.debug('input: {0}'.format(input_val))

        # set output
        if (self._state == PIDAutotuneState.STATE_RELAY_STEP_UP):
            self._output = self._initial_output - self._outputstep
        elif self._state == PIDAutotuneState.STATE_RELAY_STEP_DOWN:
            self._output = self._initial_output + self._outputstep

        # respect output limits
        self._output = min(self._output, self._out_max)
        self._output = max(self._output, self._out_min)

        # identify peaks
        is_max = True
        is_min = True

        for val in self._inputs:
            is_max = is_max and (input_val >= val)
            is_min = is_min and (input_val <= val)

        self._inputs.append(input_val)

        # we don't trust the maxes or mins until the input array is full
        if len(self._inputs) < self._inputs.maxlen:
            return False

        # increment peak count and record peak time for maxima and minima
        inflection = False

        # peak types:
        # -1: minimum
        # +1: maximum
        if is_max:
            if self._peak_type == -1:
                inflection = True
            self._peak_type = 1
        elif is_min:
            if self._peak_type == 1:
                inflection = True
            self._peak_type = -1

        # update peak times and values
        if inflection:
            self._peak_count += 1
            self._peaks.append(input_val)
            self._peak_timestamps.append(now)
            logging.debug('found peak: {0}'.format(input_val))
            logging.debug('peak count: {0}'.format(self._peak_count))

        # check for convergence of induced oscillation
        # convergence of amplitude assessed on last 4 peaks (1.5 cycles)
        self._induced_amplitude = 0

        if inflection and (self._peak_count > 4):
            abs_max = self._peaks[-2]
            abs_min = self._peaks[-2]
            for i in range(0, len(self._peaks) - 2):
                self._induced_amplitude += abs(self._peaks[i]
                                               - self._peaks[i+1])
                abs_max = max(self._peaks[i], abs_max)
                abs_min = min(self._peaks[i], abs_min)

            self._induced_amplitude /= 6.0

            # check convergence criterion for amplitude of induced oscillation
            amplitude_dev = ((0.5 * (abs_max - abs_min)
                              - self._induced_amplitude)
                             / self._induced_amplitude)

            logging.debug('amplitude: {0}'.format(self._induced_amplitude))
            logging.debug('amplitude deviation: {0}'.format(amplitude_dev))

            if amplitude_dev < PIDAutotune.PEAK_AMPLITUDE_TOLERANCE:
                self._state = PIDAutotuneState.STATE_SUCCEEDED

        # if the autotune has not already converged
        # terminate after 10 cycles
        if self._peak_count >= 20:
            self._output = 0
            self._state = PIDAutotuneState.STATE_FAILED
            return True

        if self._state == PIDAutotuneState.STATE_SUCCEEDED:
            self._output = 0
            logging.debug('peak finding successful')

            # calculate ultimate gain
            self._Ku = 4.0 * self._outputstep / \
                (self._induced_amplitude * math.pi)
            print('Ku: {0}'.format(self._Ku))

            # calculate ultimate period in seconds
            period1 = self._peak_timestamps[3] - self._peak_timestamps[1]
            period2 = self._peak_timestamps[4] - self._peak_timestamps[2]
            self._Pu = 0.5 * (period1 + period2) / 1000.0
            print('Pu: {0}'.format(self._Pu))

            for rule in self._tuning_rules:
                params = self.get_pid_parameters(rule)
                print('rule: {0}'.format(rule))
                print('Kp: {0}'.format(params.Kp))
                print('Ki: {0}'.format(params.Ki))
                print('Kd: {0}'.format(params.Kd))

            return True
        return False


async def main():
    """
        PID AutoTune Tools for Kirdy
            The obtained temperature works best at the target temperature specified.
            Before running PID AutoTune, please 
                1. Secure the laser diode onto the LD adapter and copper heat sink
                2. Make sure Kirdy has warmed up and reached thermal equilibrium state
        
        In case of PID Autotune Failure, you can
            1. Run the PID Autotune again
            2. Or increase the lookback period
            3. Or increase the sampling rate
    """

    # Target temperature of the autotune routine, celsius
    target_temperature = 20
    # Value by which output will be increased/decreased from zero, amps
    output_step = 1
    # Reference period for local minima/maxima, seconds
    lookback = 2.0
    # Determines by how much the input value must
    # overshoot/undershoot the setpoint, celsius
    noiseband = 1.5

    kirdy = Kirdy()
    await kirdy.start_session(host='192.168.1.128', port=1337, timeout=0.25)

    await kirdy.laser.set_power_on(False)
    await kirdy.laser.set_i(0)

    await kirdy.thermostat.set_power_on(False)
    await kirdy.thermostat.set_constant_current_control_mode()
    await kirdy.thermostat.set_tec_i_out(0)
    await kirdy.thermostat.clear_alarm()
    
    class SignalHandler:
        KEEP_PROCESSING = True
        def __init__(self):
            signal.signal(signal.SIGINT, self.exit_gracefully)
            signal.signal(signal.SIGTERM, self.exit_gracefully)

        def exit_gracefully(self, signum, frame):
            self.KEEP_PROCESSING = False
    signal_handler = SignalHandler()

    await kirdy.device.set_active_report_mode(False)

    # Configure the Thermistor Parameters
    await kirdy.thermostat.set_sh_beta(3900)
    await kirdy.thermostat.set_sh_r0(10.0 * 1000)
    await kirdy.thermostat.set_sh_t0(25)

    # Set a large enough temperature range so that it won't trigger overtemperature protection
    await kirdy.thermostat.set_temp_mon_upper_limit(target_temperature + 20)
    await kirdy.thermostat.set_temp_mon_lower_limit(target_temperature - 20)

    await kirdy.thermostat.set_tec_max_cooling_i(output_step)
    await kirdy.thermostat.set_tec_max_heating_i(output_step)

    # The Polling Rate of Temperature Adc is equal to the PID Update Interval
    await kirdy.thermostat.config_temp_adc_filter("Sinc5Sinc1With50hz60HzRejection", "F16SPS")
    settings = await kirdy.device.get_settings_summary()
    sampling_rate = settings["thermostat"]["temp_adc_settings"]["rate"]

    print("Settings: {0}".format(settings))

    tuner = PIDAutotune(target_temperature, output_step,
                        lookback, noiseband, 1/sampling_rate)

    await kirdy.thermostat.set_power_on(True)

    while True and signal_handler.KEEP_PROCESSING:
        status_report = await kirdy.device.get_status_report()
        
        temperature = status_report["thermostat"]["temperature"]
        ts = status_report['ts']
        print("Ts: {0} Current Temperature: {1} degree".format(ts, temperature))
        
        if (tuner.run(temperature, ts / 1000.0)):
            print(tuner._state)
            break

        tuner_out = tuner.output()
        await kirdy.thermostat.set_tec_i_out(float(tuner_out))
    
    await kirdy.thermostat.set_tec_i_out(0)
    await kirdy.thermostat.set_power_on(False)
   
    await kirdy.end_session()

if __name__ == "__main__":
    asyncio.run(main())