diff --git a/pytec/autotune.py b/pytec/autotune.py
new file mode 100644
index 0000000..fb4f59b
--- /dev/null
+++ b/pytec/autotune.py
@@ -0,0 +1,282 @@
+import math
+import logging
+from time import time
+from collections import deque, namedtuple
+from pytec.client import Client
+
+# 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
+
+# Auto tune parameters
+# Thermostat channel
+channel = 0
+# Target temperature of the autotune routine, celcius
+target_temperature = 30
+# Value by which output will be increased/decreased from zero, amps
+output_step = 1
+# Reference period for local minima/maxima, seconds
+lookback = 3
+# Determines by how much the input value must overshoot/undershoot the setpoint, celcius
+noiseband = 1.5
+
+class PIDAutotune(object):
+    
+    PIDParams = namedtuple('PIDParams', ['Kp', 'Ki', 'Kd'])
+
+    PEAK_AMPLITUDE_TOLERANCE = 0.05
+    STATE_OFF = 'off'
+    STATE_RELAY_STEP_UP = 'relay step up'
+    STATE_RELAY_STEP_DOWN = 'relay step down'
+    STATE_SUCCEEDED = 'succeeded'
+    STATE_FAILED = 'failed'
+
+    _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')
+        # if out_step < 1:
+        #     raise ValueError('out_step must be greater or equal to 1')
+        # if sampletime < 1:
+        #     raise ValueError('sampletime must be greater or equal to 1')
+        # if lookback < sampletime:
+        #     raise ValueError('lookback must be greater or equal to sampletime')
+        # if out_min >= out_max:
+        #     raise ValueError('out_min must be less than out_max')
+
+        # self._time = time
+        # logging = logging.getLogger(type(self).__name__)
+        self._inputs = deque(maxlen=round(lookback / sampletime))
+        # self._sampletime = sampletime * 1000
+        self._setpoint = setpoint
+        self._outputstep = out_step
+        self._noiseband = noiseband
+        self._out_min = -out_step
+        self._out_max = out_step
+        self._state = PIDAutotune.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
+
+    # @property
+    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 = kp / (self._Pu / divisors[1])
+        ki = divisors[1] * self._Ku / self._Pu
+        # kd = kp * (self._Pu / divisors[2])
+        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 input value.
+
+        Returns:
+            `true` if tuning is finished, otherwise `false`.
+        """
+        now = time_input * 1000
+
+        if (self._state == PIDAutotune.STATE_OFF
+                or self._state == PIDAutotune.STATE_SUCCEEDED
+                or self._state == PIDAutotune.STATE_FAILED):
+            self._initTuner(input_val, now)
+
+        self._last_run_timestamp = now
+        # print("temp : ", input_val)
+
+        # check input and change relay state if necessary
+        if (self._state == PIDAutotune.STATE_RELAY_STEP_UP
+                and input_val > self._setpoint + self._noiseband):
+            self._state = PIDAutotune.STATE_RELAY_STEP_DOWN
+            logging.debug('switched state: {0}'.format(self._state))
+            logging.debug('input: {0}'.format(input_val))
+        elif (self._state == PIDAutotune.STATE_RELAY_STEP_DOWN
+                and input_val < self._setpoint - self._noiseband):
+            self._state = PIDAutotune.STATE_RELAY_STEP_UP
+            logging.debug('switched state: {0}'.format(self._state))
+            logging.debug('input: {0}'.format(input_val))
+
+        # set output
+        if (self._state == PIDAutotune.STATE_RELAY_STEP_UP):
+            self._output = self._initial_output + self._outputstep
+        elif self._state == PIDAutotune.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 want to 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 = PIDAutotune.STATE_SUCCEEDED
+                # logging.debug('peak finding succeeded')
+        
+        # if the autotune has not already converged
+        # terminate after 10 cycles
+        if self._peak_count >= 20:
+            self._output = 0
+            self._state = PIDAutotune.STATE_FAILED
+            return True
+
+        if self._state == PIDAutotune.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
+
+    def _initTuner(self, inputValue, timestamp):
+        self._peak_type = 0
+        self._peak_count = 0
+        self._output = 0
+        self._initial_output = 0
+        self._Ku = 0
+        self._Pu = 0
+        self._inputs.clear()
+        self._peaks.clear()
+        self._peak_timestamps.clear()
+        self._peak_timestamps.append(timestamp)
+        self._state = PIDAutotune.STATE_RELAY_STEP_UP
+
+# logging.basicConfig(level=logging.DEBUG)
+
+tec = Client() #(host="localhost", port=6667)
+
+data = next(tec.report_mode())
+ch = data[channel]
+
+tuner = PIDAutotune(target_temperature, output_step, lookback, noiseband, ch['interval'])
+
+for data in tec.report_mode():
+
+    try:
+        ch = data[channel]
+
+        temperature = ch['temperature']
+
+        if (tuner.run(temperature, ch['time'])):
+            # logging.debug('true')
+            break
+
+        tunerOut = tuner.output()
+
+        tec.set_param("pwm", channel, "i_set" , tunerOut)
+
+    except:
+        pass
+
+tec.set_param("pwm", channel, "i_set" , channel)
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