pytec: __name__ check, examples does not run when pytec module is imported, change autotune state management to use enum
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@ -3,35 +3,41 @@ import logging
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from time import time
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from collections import deque, namedtuple
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from pytec.client import Client
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from enum import Enum
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# Based on hirshmann pid-autotune libiary
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# See https://github.com/hirschmann/pid-autotune
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# Which is in turn based on a fork of Arduino PID AutoTune Library
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# See https://github.com/t0mpr1c3/Arduino-PID-AutoTune-Library
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# Auto tune parameters
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# Thermostat channel
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channel = 0
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# Target temperature of the autotune routine, celcius
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target_temperature = 30
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# Value by which output will be increased/decreased from zero, amps
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output_step = 1
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# Reference period for local minima/maxima, seconds
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lookback = 3
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# Determines by how much the input value must overshoot/undershoot the setpoint, celcius
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noiseband = 1.5
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if __name__ == "__main__":
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class PIDAutotune(object):
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# Auto tune parameters
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# Thermostat channel
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channel = 0
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# Target temperature of the autotune routine, celcius
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target_temperature = 30
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# Value by which output will be increased/decreased from zero, amps
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output_step = 1
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# Reference period for local minima/maxima, seconds
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lookback = 3
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# Determines by how much the input value must overshoot/undershoot the setpoint, celcius
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noiseband = 1.5
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PIDParams = namedtuple('PIDParams', ['Kp', 'Ki', 'Kd'])
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PEAK_AMPLITUDE_TOLERANCE = 0.05
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class PIDAutotuneState(Enum):
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STATE_OFF = 'off'
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STATE_RELAY_STEP_UP = 'relay step up'
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STATE_RELAY_STEP_DOWN = 'relay step down'
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STATE_SUCCEEDED = 'succeeded'
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STATE_FAILED = 'failed'
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class PIDAutotune():
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PIDParams = namedtuple('PIDParams', ['Kp', 'Ki', 'Kd'])
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PEAK_AMPLITUDE_TOLERANCE = 0.05
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_tuning_rules = {
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"ziegler-nichols": [0.6, 1.2, 0.075],
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"tyreus-luyben": [0.4545, 0.2066, 0.07214],
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@ -62,7 +68,7 @@ class PIDAutotune(object):
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self._noiseband = noiseband
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self._out_min = -out_step
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self._out_max = out_step
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self._state = PIDAutotune.STATE_OFF
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self._state = PIDAutotuneState.STATE_OFF
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self._peak_timestamps = deque(maxlen=5)
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self._peaks = deque(maxlen=5)
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self._output = 0
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@ -115,30 +121,30 @@ class PIDAutotune(object):
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"""
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now = time_input * 1000
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if (self._state == PIDAutotune.STATE_OFF
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or self._state == PIDAutotune.STATE_SUCCEEDED
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or self._state == PIDAutotune.STATE_FAILED):
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if (self._state == PIDAutotuneState.STATE_OFF
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or self._state == PIDAutotuneState.STATE_SUCCEEDED
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or self._state == PIDAutotuneState.STATE_FAILED):
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self._initTuner(input_val, now)
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self._last_run_timestamp = now
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# print("temp : ", input_val)
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# check input and change relay state if necessary
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if (self._state == PIDAutotune.STATE_RELAY_STEP_UP
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if (self._state == PIDAutotuneState.STATE_RELAY_STEP_UP
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and input_val > self._setpoint + self._noiseband):
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self._state = PIDAutotune.STATE_RELAY_STEP_DOWN
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self._state = PIDAutotuneState.STATE_RELAY_STEP_DOWN
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logging.debug('switched state: {0}'.format(self._state))
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logging.debug('input: {0}'.format(input_val))
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elif (self._state == PIDAutotune.STATE_RELAY_STEP_DOWN
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elif (self._state == PIDAutotuneState.STATE_RELAY_STEP_DOWN
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and input_val < self._setpoint - self._noiseband):
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self._state = PIDAutotune.STATE_RELAY_STEP_UP
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self._state = PIDAutotuneState.STATE_RELAY_STEP_UP
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logging.debug('switched state: {0}'.format(self._state))
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logging.debug('input: {0}'.format(input_val))
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# set output
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if (self._state == PIDAutotune.STATE_RELAY_STEP_UP):
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if (self._state == PIDAutotuneState.STATE_RELAY_STEP_UP):
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self._output = self._initial_output + self._outputstep
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elif self._state == PIDAutotune.STATE_RELAY_STEP_DOWN:
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elif self._state == PIDAutotuneState.STATE_RELAY_STEP_DOWN:
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self._output = self._initial_output - self._outputstep
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# respect output limits
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@ -204,17 +210,17 @@ class PIDAutotune(object):
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logging.debug('amplitude deviation: {0}'.format(amplitude_dev))
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if amplitude_dev < PIDAutotune.PEAK_AMPLITUDE_TOLERANCE:
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self._state = PIDAutotune.STATE_SUCCEEDED
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self._state = PIDAutotuneState.STATE_SUCCEEDED
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# logging.debug('peak finding succeeded')
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# if the autotune has not already converged
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# terminate after 10 cycles
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if self._peak_count >= 20:
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self._output = 0
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self._state = PIDAutotune.STATE_FAILED
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self._state = PIDAutotuneState.STATE_FAILED
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return True
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if self._state == PIDAutotune.STATE_SUCCEEDED:
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if self._state == PIDAutotuneState.STATE_SUCCEEDED:
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self._output = 0
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logging.debug('peak finding successful')
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@ -250,33 +256,34 @@ class PIDAutotune(object):
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self._peaks.clear()
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self._peak_timestamps.clear()
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self._peak_timestamps.append(timestamp)
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self._state = PIDAutotune.STATE_RELAY_STEP_UP
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self._state = PIDAutotuneState.STATE_RELAY_STEP_UP
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# logging.basicConfig(level=logging.DEBUG)
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if __name__ == "__main__":
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tec = Client() #(host="localhost", port=6667)
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# logging.basicConfig(level=logging.DEBUG)
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data = next(tec.report_mode())
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ch = data[channel]
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tec = Client() #(host="localhost", port=6667)
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tuner = PIDAutotune(target_temperature, output_step, lookback, noiseband, ch['interval'])
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data = next(tec.report_mode())
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ch = data[channel]
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for data in tec.report_mode():
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tuner = PIDAutotune(target_temperature, output_step, lookback, noiseband, ch['interval'])
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try:
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ch = data[channel]
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for data in tec.report_mode():
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temperature = ch['temperature']
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try:
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ch = data[channel]
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if (tuner.run(temperature, ch['time'])):
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# logging.debug('true')
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break
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temperature = ch['temperature']
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tunerOut = tuner.output()
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if (tuner.run(temperature, ch['time'])):
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break
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tec.set_param("pwm", channel, "i_set" , tunerOut)
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tunerOut = tuner.output()
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except:
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pass
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tec.set_param("pwm", channel, "i_set" , tunerOut)
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tec.set_param("pwm", channel, "i_set" , channel)
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except:
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pass
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tec.set_param("pwm", channel, "i_set" , channel)
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@ -1,11 +1,13 @@
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from pytec.client import Client
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if __name__ == "__main__":
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tec = Client() #(host="localhost", port=6667)
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tec.set_param("s-h", 1, "t0", 20)
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print(tec.get_pwm())
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print(tec.get_pid())
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print(tec.get_pwm())
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print(tec.get_postfilter())
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print(tec.get_steinhart_hart())
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for data in tec.report_mode():
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print(data)
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from pytec.client import Client
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tec = Client() #(host="localhost", port=6667)
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tec.set_param("s-h", 1, "t0", 20)
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print(tec.get_pwm())
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print(tec.get_pid())
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print(tec.get_pwm())
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print(tec.get_postfilter())
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print(tec.get_steinhart_hart())
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for data in tec.report_mode():
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print(data)
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214
pytec/plot.py
214
pytec/plot.py
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@ -4,125 +4,127 @@ import matplotlib.animation as animation
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from threading import Thread, Lock
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from pytec.client import Client
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TIME_WINDOW = 300.0
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if __name__ == "__main__":
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tec = Client()
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target_temperature = tec.get_pid()[0]['target']
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print("Channel 0 target temperature: {:.3f}".format(target_temperature))
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TIME_WINDOW = 300.0
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class Series:
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def __init__(self, conv=lambda x: x):
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self.conv = conv
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self.x_data = []
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self.y_data = []
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tec = Client()
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target_temperature = tec.get_pid()[0]['target']
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print("Channel 0 target temperature: {:.3f}".format(target_temperature))
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def append(self, x, y):
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self.x_data.append(x)
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self.y_data.append(self.conv(y))
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class Series:
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def __init__(self, conv=lambda x: x):
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self.conv = conv
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self.x_data = []
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self.y_data = []
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def clip(self, min_x):
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drop = 0
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while drop < len(self.x_data) and self.x_data[drop] < min_x:
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drop += 1
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self.x_data = self.x_data[drop:]
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self.y_data = self.y_data[drop:]
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def append(self, x, y):
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self.x_data.append(x)
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self.y_data.append(self.conv(y))
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series = {
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'adc': Series(),
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'sens': Series(lambda x: x * 0.0001),
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'temperature': Series(lambda t: t - target_temperature),
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'i_set': Series(),
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'pid_output': Series(),
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'vref': Series(),
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'dac_value': Series(),
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'dac_feedback': Series(),
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'i_tec': Series(),
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'tec_i': Series(),
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'tec_u_meas': Series(),
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'interval': Series(),
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}
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series_lock = Lock()
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def clip(self, min_x):
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drop = 0
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while drop < len(self.x_data) and self.x_data[drop] < min_x:
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drop += 1
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self.x_data = self.x_data[drop:]
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self.y_data = self.y_data[drop:]
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quit = False
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series = {
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'adc': Series(),
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'sens': Series(lambda x: x * 0.0001),
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'temperature': Series(),
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'i_set': Series(),
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'pid_output': Series(),
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'vref': Series(),
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'dac_value': Series(),
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'dac_feedback': Series(),
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'i_tec': Series(),
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'tec_i': Series(),
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'tec_u_meas': Series(),
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'interval': Series(),
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}
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series_lock = Lock()
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quit = False
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def recv_data(tec):
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global last_packet_time
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for data in tec.report_mode():
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ch0 = data[0]
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series_lock.acquire()
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try:
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for k, s in series.items():
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if k in ch0:
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v = ch0[k]
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if type(v) is float:
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s.append(ch0['time'], v)
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finally:
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series_lock.release()
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if quit:
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break
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thread = Thread(target=recv_data, args=(tec,))
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thread.start()
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fig, ax = plt.subplots()
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for k, s in series.items():
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s.plot, = ax.plot([], [], label=k)
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legend = ax.legend()
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def animate(i):
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min_x, max_x, min_y, max_y = None, None, None, None
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def recv_data(tec):
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global last_packet_time
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for data in tec.report_mode():
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ch0 = data[0]
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series_lock.acquire()
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try:
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for k, s in series.items():
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if k in ch0:
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v = ch0[k]
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if type(v) is float:
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s.append(ch0['time'], v)
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s.plot.set_data(s.x_data, s.y_data)
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if len(s.y_data) > 0:
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s.plot.set_label("{}: {:.3f}".format(k, s.y_data[-1]))
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if len(s.x_data) > 0:
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min_x_ = min(s.x_data)
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if min_x is None:
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min_x = min_x_
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else:
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min_x = min(min_x, min_x_)
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max_x_ = max(s.x_data)
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if max_x is None:
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max_x = max_x_
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else:
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max_x = max(max_x, max_x_)
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if len(s.y_data) > 0:
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min_y_ = min(s.y_data)
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if min_y is None:
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min_y = min_y_
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else:
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min_y = min(min_y, min_y_)
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max_y_ = max(s.y_data)
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if max_y is None:
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max_y = max_y_
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else:
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max_y = max(max_y, max_y_)
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if min_x and max_x - TIME_WINDOW > min_x:
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for s in series.values():
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s.clip(max_x - TIME_WINDOW)
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finally:
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series_lock.release()
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if quit:
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break
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if min_x != max_x:
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ax.set_xlim(min_x, max_x)
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if min_y != max_y:
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margin_y = 0.01 * (max_y - min_y)
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ax.set_ylim(min_y - margin_y, max_y + margin_y)
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thread = Thread(target=recv_data, args=(tec,))
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thread.start()
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global legend
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legend.remove()
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legend = ax.legend()
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fig, ax = plt.subplots()
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ani = animation.FuncAnimation(
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fig, animate, interval=1, blit=False, save_count=50)
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for k, s in series.items():
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s.plot, = ax.plot([], [], label=k)
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legend = ax.legend()
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def animate(i):
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min_x, max_x, min_y, max_y = None, None, None, None
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series_lock.acquire()
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try:
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for k, s in series.items():
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s.plot.set_data(s.x_data, s.y_data)
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if len(s.y_data) > 0:
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s.plot.set_label("{}: {:.3f}".format(k, s.y_data[-1]))
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if len(s.x_data) > 0:
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min_x_ = min(s.x_data)
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if min_x is None:
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min_x = min_x_
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else:
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min_x = min(min_x, min_x_)
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max_x_ = max(s.x_data)
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if max_x is None:
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max_x = max_x_
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else:
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max_x = max(max_x, max_x_)
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if len(s.y_data) > 0:
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min_y_ = min(s.y_data)
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if min_y is None:
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min_y = min_y_
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else:
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min_y = min(min_y, min_y_)
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max_y_ = max(s.y_data)
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if max_y is None:
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max_y = max_y_
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else:
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max_y = max(max_y, max_y_)
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if min_x and max_x - TIME_WINDOW > min_x:
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for s in series.values():
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s.clip(max_x - TIME_WINDOW)
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finally:
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series_lock.release()
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if min_x != max_x:
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ax.set_xlim(min_x, max_x)
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if min_y != max_y:
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margin_y = 0.01 * (max_y - min_y)
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ax.set_ylim(min_y - margin_y, max_y + margin_y)
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global legend
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legend.remove()
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legend = ax.legend()
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ani = animation.FuncAnimation(
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fig, animate, interval=1, blit=False, save_count=50)
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plt.show()
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quit = True
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thread.join()
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plt.show()
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quit = True
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thread.join()
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