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Autotune.py
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Autotune.py
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"""
Autotune for Redeem
Author: Elias Bakken
email: elias(dot)bakken(at)gmail(dot)com
Website: http://www.thing-printer.com
License: GNU GPL v3: http://www.gnu.org/copyleft/gpl.html
Redeem is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Redeem is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with Redeem. If not, see <http://www.gnu.org/licenses/>.
"""
from __future__ import division, print_function
from threading import Thread
import time
import logging
import numpy as np
class Autotune:
def __init__(self, heater, temp=100.0, cycles=3):
self.heater = heater
self.noise_band = 0.5
# Steady state starting temperture
self.steady_temperature = temp
self.cycles = cycles
# Degrees to step
self.output_step = 10.0
self.stable_start_seconds = 10
self.sleep = 0.1
def cancel(self):
self.running = False
self.t.join()
def run(self):
""" Start the PID autotune loop """
# Reset found peaks
self.running = True
self.temps = []
# Wait for temperature to stabilize
self.heater.set_target_temperature(self.steady_temperature)
while not self.heater.is_temperature_stable(self.stable_start_seconds):
time.sleep(1)
# Set the standard parameters
self.old_ok_range = self.heater.ok_range
self.heater.P = 0.5
self.heater.I = 0.0
self.heater.D = 0.0
self.heater.ok_range = 0.5
self.d = self.bias = 0.5
# Start stepping temperatures
self._tune()
logging.debug("Tuning data: "+str(self.temps))
# Smooth the data using hanning window
self.smooth_temps = self.smooth(np.array(self.temps))
# Discover peaks
peaks = self.detect_peaks(self.smooth_temps)
valleys = self.detect_peaks(self.smooth_temps, valley=True)
logging.debug("Found peaks: "+str(peaks))
logging.debug("Found valleys: "+str(valleys))
# Calculate the new PID values
self.calculate_PID(self.smooth_temps, peaks, valleys)
# Set the new PID settings
self.heater.ok_range = self.old_ok_range
self.heater.P = self.Kp
self.heater.I = self.Ki
self.heater.D = self.Kd
self.heater.set_target_temperature(self.steady_temperature)
def _tune(self):
for peak in range(self.cycles):
# Set upper temperature step
new_temp = self.steady_temperature + self.output_step
self.heater.set_target_temperature(new_temp)
logging.debug("Setting temp to "+str(new_temp))
# Wait for target temperature to be reached
while self.heater.get_temperature() < self.steady_temperature + self.noise_band:
temp = self.heater.get_temperature()
self.temps.append(temp)
if not self.running:
return
time.sleep(self.sleep)
# Set lower temperature step
new_temp = self.steady_temperature - self.output_step
self.heater.set_target_temperature(new_temp)
logging.debug("Setting temp to "+str(new_temp))
# Wait for target temperature to be reached
while self.heater.get_temperature() > self.steady_temperature - self.noise_band:
temp = self.heater.get_temperature()
self.temps.append(temp)
if not self.running:
return
time.sleep(self.sleep)
if peak == 0:
self.temps = []
continue
if peak >= 2:
smooth = self.smooth(np.array(self.temps))
peaks = self.detect_peaks(smooth)
diff = np.diff(smooth[peaks[-2:]])
logging.debug("Difference between last two peaks: "+str(diff)+" deg. C.")
print("Difference between last two peaks: "+str(diff)+" deg. C.")
# TODO: Adjust Power based on diff.
#self.heater.P = ??
logging.debug("Setting heater P: "+str(self.heater.P))
def calculate_PID(self, temps, peaks, valleys):
abs_max = temps[peaks[-1]]
abs_min = temps[valleys[-1]]
logging.debug("Temperature Gain: "+str(abs_max-abs_min)+" deg. C")
print("Gain: "+str(abs_max-abs_min))
# Calculate the oscillation period of the peaks
Pu = (peaks[-1]-peaks[-2])*self.heater.sleep
# Calculate the ultimate gain
Ku = 4.0*self.output_step/(np.pi*(abs_max-abs_min)/2.0)
logging.debug("Oscillation period: "+str(Pu)+" seconds")
logging.debug("Ultimate gain: "+str(Ku))
print("Oscillation period: "+str(Pu))
print("Ultimate gain: "+str(Ku))
# Redeem uses 0..1 instead of 0..255
# TODO: This is probably not right...
factor = 10.0
self.Kp = (0.6*Ku)/factor
self.Ki = (1.2*Ku / Pu)/factor
self.Kd = (0.075 * Ku * Pu)/factor
self.Ku = Ku
self.Pu = Pu
self.max_temp = abs_max
self.min_temp = abs_min
def smooth(self, x,window_len=100,window='hanning'):
"""
smooth the data using a window with requested size.
This method is based on the convolution of a scaled window with the signal.
The signal is prepared by introducing reflected copies of the signal
(with the window size) in both ends so that transient parts are minimized
in the begining and end part of the output signal.
Note: this function was taken from the SciPy cookbook:
http://wiki.scipy.org/Cookbook/SignalSmooth
"""
if x.size < window_len:
raise ValueError, "Input vector needs to be bigger than window size."
if window_len<3:
return x
if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
raise ValueError, "Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'"
s=np.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]
#print(len(s))
if window == 'flat': #moving average
w=np.ones(window_len,'d')
else:
w=eval('np.'+window+'(window_len)')
y=np.convolve(w/w.sum(),s,mode='valid')
return y
def detect_peaks(self, x, mph=None, mpd=1, threshold=0, edge='rising',
kpsh=False, valley=False, show=False, ax=None):
__author__ = "Marcos Duarte, https://github.com/demotu/BMC"
__version__ = "1.0.4"
__license__ = "MIT"
"""Detect peaks in data based on their amplitude and other features.
Parameters
----------
x : 1D array_like
data.
mph : {None, number}, optional (default = None)
detect peaks that are greater than minimum peak height.
mpd : positive integer, optional (default = 1)
detect peaks that are at least separated by minimum peak distance (in
number of data).
threshold : positive number, optional (default = 0)
detect peaks (valleys) that are greater (smaller) than `threshold`
in relation to their immediate neighbors.
edge : {None, 'rising', 'falling', 'both'}, optional (default = 'rising')
for a flat peak, keep only the rising edge ('rising'), only the
falling edge ('falling'), both edges ('both'), or don't detect a
flat peak (None).
kpsh : bool, optional (default = False)
keep peaks with same height even if they are closer than `mpd`.
valley : bool, optional (default = False)
if True (1), detect valleys (local minima) instead of peaks.
show : bool, optional (default = False)
if True (1), plot data in matplotlib figure.
ax : a matplotlib.axes.Axes instance, optional (default = None).
Returns
-------
ind : 1D array_like
indeces of the peaks in `x`.
Notes
-----
The detection of valleys instead of peaks is performed internally by simply
negating the data: `ind_valleys = detect_peaks(-x)`
The function can handle NaN's
See this IPython Notebook [1]_.
References
----------
.. [1] http://nbviewer.ipython.org/github/demotu/BMC/blob/master/notebooks/DetectPeaks.ipynb
"""
x = np.atleast_1d(x).astype('float64')
if x.size < 3:
return np.array([], dtype=int)
if valley:
x = -x
# find indices of all peaks
dx = x[1:] - x[:-1]
# handle NaN's
indnan = np.where(np.isnan(x))[0]
if indnan.size:
x[indnan] = np.inf
dx[np.where(np.isnan(dx))[0]] = np.inf
ine, ire, ife = np.array([[], [], []], dtype=int)
if not edge:
ine = np.where((np.hstack((dx, 0)) < 0) & (np.hstack((0, dx)) > 0))[0]
else:
if edge.lower() in ['rising', 'both']:
ire = np.where((np.hstack((dx, 0)) <= 0) & (np.hstack((0, dx)) > 0))[0]
if edge.lower() in ['falling', 'both']:
ife = np.where((np.hstack((dx, 0)) < 0) & (np.hstack((0, dx)) >= 0))[0]
ind = np.unique(np.hstack((ine, ire, ife)))
# handle NaN's
if ind.size and indnan.size:
# NaN's and values close to NaN's cannot be peaks
ind = ind[np.in1d(ind, np.unique(np.hstack((indnan, indnan-1, indnan+1))), invert=True)]
# first and last values of x cannot be peaks
if ind.size and ind[0] == 0:
ind = ind[1:]
if ind.size and ind[-1] == x.size-1:
ind = ind[:-1]
# remove peaks < minimum peak height
if ind.size and mph is not None:
ind = ind[x[ind] >= mph]
# remove peaks - neighbors < threshold
if ind.size and threshold > 0:
dx = np.min(np.vstack([x[ind]-x[ind-1], x[ind]-x[ind+1]]), axis=0)
ind = np.delete(ind, np.where(dx < threshold)[0])
# detect small peaks closer than minimum peak distance
if ind.size and mpd > 1:
ind = ind[np.argsort(x[ind])][::-1] # sort ind by peak height
idel = np.zeros(ind.size, dtype=bool)
for i in range(ind.size):
if not idel[i]:
# keep peaks with the same height if kpsh is True
idel = idel | (ind >= ind[i] - mpd) & (ind <= ind[i] + mpd) \
& (x[ind[i]] > x[ind] if kpsh else True)
idel[i] = 0 # Keep current peak
# remove the small peaks and sort back the indices by their occurrence
ind = np.sort(ind[~idel])
if show:
if indnan.size:
x[indnan] = np.nan
if valley:
x = -x
self._plot(x, mph, mpd, threshold, edge, valley, ax, ind)
return ind
def _plot(self, x, mph, mpd, threshold, edge, valley, ax, ind):
"""Plot results of the detect_peaks function, see its help."""
try:
import matplotlib.pyplot as plt
except ImportError:
print('matplotlib is not available.')
else:
if ax is None:
_, ax = plt.subplots(1, 1, figsize=(8, 4))
ax.plot(x, 'b', lw=1)
if ind.size:
label = 'valley' if valley else 'peak'
label = label + 's' if ind.size > 1 else label
ax.plot(ind, x[ind], '+', mfc=None, mec='r', mew=2, ms=8,
label='%d %s' % (ind.size, label))
ax.legend(loc='best', framealpha=.5, numpoints=1)
ax.set_xlim(-.02*x.size, x.size*1.02-1)
ymin, ymax = x[np.isfinite(x)].min(), x[np.isfinite(x)].max()
yrange = ymax - ymin if ymax > ymin else 1
ax.set_ylim(ymin - 0.1*yrange, ymax + 0.1*yrange)
ax.set_xlabel('Data #', fontsize=14)
ax.set_ylabel('Amplitude', fontsize=14)
mode = 'Valley detection' if valley else 'Peak detection'
ax.set_title("%s (mph=%s, mpd=%d, threshold=%s, edge='%s')"
% (mode, str(mph), mpd, str(threshold), edge))
# plt.grid()
plt.show()
if __name__ == '__main__':
import matplotlib.pyplot as plt
data = np.array([99.5, 99.400000000000006, 99.299999999999997, 99.200000000000003, 99.200000000000003, 99.099999999999994, 99.099999999999994, 99.099999999999994, 99.099999999999994, 99.099999999999994, 99.099999999999994, 99.099999999999994, 99.099999999999994, 99.0, 98.900000000000006, 98.900000000000006, 99.0, 99.0, 99.0, 98.900000000000006, 99.0, 99.0, 99.099999999999994, 99.200000000000003, 99.400000000000006, 99.599999999999994, 99.599999999999994, 99.799999999999997, 99.799999999999997, 99.900000000000006, 100.09999999999999, 100.09999999999999, 100.3, 100.40000000000001, 100.59999999999999, 100.8, 100.8, 101.0, 101.09999999999999, 101.3, 101.5, 101.7, 102.0, 102.2, 102.2, 102.5, 102.59999999999999, 102.8, 103.0, 103.2, 103.40000000000001, 103.40000000000001, 103.7, 103.90000000000001, 104.09999999999999, 104.3, 104.40000000000001, 104.59999999999999, 104.8, 104.8, 104.90000000000001, 105.0, 105.2, 105.2, 105.2, 105.3, 105.3, 105.5, 105.7, 105.8, 105.90000000000001, 106.09999999999999, 106.2, 106.40000000000001, 106.59999999999999, 106.59999999999999, 106.8, 107.0, 107.0, 107.09999999999999, 107.3, 107.3, 107.3, 107.3, 107.40000000000001, 107.3, 107.40000000000001, 107.40000000000001, 107.3, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.5, 107.59999999999999, 107.59999999999999, 107.5, 107.5, 107.5, 107.5, 107.40000000000001, 107.3, 107.3, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.5, 107.5, 107.5, 107.40000000000001, 107.40000000000001, 107.3, 107.40000000000001, 107.40000000000001, 107.5, 107.5, 107.5, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.3, 107.40000000000001, 107.3, 107.3, 107.3, 107.40000000000001, 107.3, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.2,107.2, 107.2, 107.2, 107.2, 107.2, 107.09999999999999, 107.0, 107.0, 107.0, 107.0, 107.0, 107.09999999999999, 107.0, 107.0, 106.90000000000001, 106.90000000000001, 106.90000000000001, 106.90000000000001, 106.8, 106.7, 106.90000000000001, 106.7, 106.7, 106.59999999999999, 106.59999999999999, 106.59999999999999, 106.5, 106.40000000000001, 106.3, 106.3, 106.3, 106.09999999999999, 106.09999999999999, 106.09999999999999, 106.09999999999999, 105.90000000000001, 105.8, 105.8, 105.7, 105.7, 105.59999999999999, 105.59999999999999, 105.59999999999999, 105.5, 105.40000000000001, 105.40000000000001, 105.40000000000001, 105.40000000000001, 105.3, 105.3, 105.3, 105.2, 105.09999999999999, 105.09999999999999, 105.09999999999999, 105.0, 105.0, 105.0, 105.0, 105.0, 105.0, 105.0, 104.90000000000001, 104.90000000000001, 104.90000000000001, 104.8, 104.8, 104.7, 104.59999999999999, 104.59999999999999, 104.59999999999999, 104.5, 104.5, 104.5, 104.5, 104.5, 104.59999999999999, 104.59999999999999, 104.59999999999999, 104.59999999999999, 104.5, 104.40000000000001, 104.40000000000001, 104.40000000000001, 104.3, 104.2, 104.09999999999999, 104.0, 104.0, 104.0, 104.0, 104.0, 104.0, 104.0, 104.0, 103.90000000000001, 103.90000000000001, 103.90000000000001, 103.90000000000001, 103.8, 103.8, 103.8, 103.8, 103.8, 103.7, 103.8, 103.7, 103.59999999999999, 103.59999999999999, 103.59999999999999, 103.5, 103.5, 103.40000000000001, 103.40000000000001, 103.3, 103.3, 103.2, 103.2, 103.2, 103.09999999999999, 103.09999999999999, 103.09999999999999, 103.09999999999999, 103.0, 103.0, 103.0, 103.0, 103.0, 103.0, 103.0, 103.0, 103.0, 102.90000000000001, 102.8, 102.8, 102.8, 102.8, 102.8, 102.7, 102.59999999999999, 102.5, 102.5, 102.5, 102.5, 102.5, 102.40000000000001, 102.3, 102.2, 102.09999999999999, 102.09999999999999, 102.09999999999999, 102.09999999999999, 102.0, 102.0, 102.0, 102.0, 101.90000000000001, 101.90000000000001, 101.90000000000001, 101.90000000000001, 101.90000000000001, 101.8, 101.8, 101.8, 101.8, 101.8, 101.7, 101.8, 101.7, 101.7, 101.59999999999999, 101.59999999999999, 101.5, 101.5, 101.40000000000001, 101.3, 101.3, 101.3, 101.2, 101.2, 101.09999999999999, 101.09999999999999, 101.09999999999999, 101.09999999999999, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 100.90000000000001, 100.90000000000001, 100.90000000000001, 100.90000000000001, 100.90000000000001, 100.8, 100.7, 100.7, 100.7, 100.59999999999999, 100.5, 100.59999999999999, 100.59999999999999, 100.59999999999999, 100.5, 100.3, 100.3, 100.40000000000001, 100.3, 100.3, 100.3, 100.3, 100.2, 100.09999999999999, 100.09999999999999, 100.0, 100.09999999999999, 100.09999999999999, 100.0, 100.0, 99.900000000000006, 99.900000000000006, 99.799999999999997, 99.799999999999997, 99.799999999999997, 99.900000000000006, 99.900000000000006, 99.900000000000006, 99.900000000000006, 99.900000000000006, 99.900000000000006, 99.900000000000006, 99.799999999999997, 99.799999999999997, 99.700000000000003, 99.599999999999994, 99.5, 99.5, 99.400000000000006, 99.299999999999997, 99.099999999999994, 99.099999999999994, 99.099999999999994, 99.0, 99.0, 99.099999999999994, 99.099999999999994, 99.099999999999994, 99.0, 99.0, 99.0, 99.099999999999994, 99.099999999999994, 99.099999999999994, 99.099999999999994, 99.099999999999994, 99.200000000000003, 99.200000000000003, 99.200000000000003, 99.299999999999997, 99.299999999999997, 99.400000000000006, 99.5, 99.599999999999994, 99.700000000000003, 99.799999999999997, 99.900000000000006, 99.900000000000006, 100.09999999999999, 100.3, 100.40000000000001, 100.5, 100.7, 100.8, 101.0, 101.2, 101.40000000000001, 101.7, 101.90000000000001, 102.09999999999999, 102.3, 102.5, 102.5, 102.7, 103.0, 103.2, 103.40000000000001, 103.40000000000001, 103.5, 103.7, 103.90000000000001, 104.09999999999999, 104.3, 104.40000000000001, 104.5, 104.59999999999999, 104.8, 104.8, 105.0, 105.2, 105.2, 105.2, 105.3, 105.5, 105.59999999999999, 105.7, 106.0, 106.09999999999999, 106.3, 106.5, 106.7, 106.90000000000001, 107.0, 107.0, 107.09999999999999, 107.3, 107.3, 107.2, 107.3, 107.40000000000001, 107.3, 107.3, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.5, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.3, 107.40000000000001, 107.5, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.5, 107.5, 107.5, 107.59999999999999, 107.59999999999999, 107.59999999999999, 107.5, 107.40000000000001, 107.5, 107.5, 107.5, 107.5, 107.5, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.40000000000001, 107.3, 107.40000000000001, 107.40000000000001, 107.3, 107.3, 107.3, 107.2, 107.2, 107.2, 107.3, 107.3, 107.3, 107.3, 107.2, 107.3, 107.40000000000001, 107.5, 107.5, 107.5, 107.59999999999999, 107.5, 107.59999999999999, 107.5, 107.59999999999999, 107.59999999999999, 107.5, 107.40000000000001, 107.40000000000001, 107.5, 107.5, 107.40000000000001, 107.40000000000001, 107.3, 107.40000000000001, 107.3, 107.3, 107.3, 107.3, 107.3, 107.2, 107.09999999999999, 107.09999999999999, 107.09999999999999, 106.90000000000001, 106.8, 106.59999999999999, 106.59999999999999, 106.5, 106.40000000000001, 106.3, 106.2, 106.2, 106.09999999999999, 106.0, 106.0, 105.90000000000001, 105.8, 105.7, 105.7, 105.59999999999999, 105.59999999999999, 105.59999999999999, 105.59999999999999, 105.59999999999999, 105.59999999999999, 105.59999999999999, 105.59999999999999, 105.7, 105.7, 105.7, 105.8, 105.7, 105.59999999999999, 105.5, 105.40000000000001, 105.3, 105.3, 105.3, 105.3, 105.2, 105.0, 105.0, 104.90000000000001, 104.90000000000001, 104.90000000000001, 104.8, 104.8, 104.8, 104.59999999999999, 104.59999999999999, 104.59999999999999, 104.7, 104.7, 104.8, 104.8, 104.8, 104.7, 104.59999999999999, 104.7, 104.7, 104.59999999999999, 104.5, 104.5, 104.40000000000001, 104.3, 104.3, 104.3, 104.3, 104.3, 104.2, 104.09999999999999, 104.09999999999999, 104.09999999999999, 104.09999999999999, 104.09999999999999, 104.09999999999999, 104.0, 104.0, 104.0, 104.0, 104.0, 104.0, 103.90000000000001, 103.90000000000001, 103.90000000000001, 103.90000000000001, 103.90000000000001, 103.90000000000001, 103.90000000000001, 103.90000000000001, 103.90000000000001, 103.90000000000001, 103.90000000000001, 104.0, 103.90000000000001, 103.8, 103.7, 103.7, 103.59999999999999, 103.5, 103.40000000000001, 103.3, 103.2, 103.09999999999999, 103.09999999999999, 103.09999999999999, 103.09999999999999, 103.09999999999999, 103.0, 103.0, 103.0, 103.0, 103.0, 102.90000000000001, 102.8, 102.8, 102.8, 102.8, 102.7, 102.7, 102.7, 102.7, 102.7, 102.7, 102.7, 102.7, 102.59999999999999, 102.59999999999999, 102.5, 102.5, 102.5, 102.40000000000001, 102.3, 102.2, 102.2, 102.09999999999999, 102.09999999999999, 102.09999999999999, 102.0, 102.0, 102.0, 101.8, 101.8, 101.8, 101.8, 101.8, 101.90000000000001, 101.90000000000001, 101.8, 101.8, 101.8, 101.8, 101.8, 101.7, 101.59999999999999, 101.5, 101.5, 101.40000000000001, 101.3, 101.40000000000001, 101.3, 101.09999999999999, 101.09999999999999, 101.09999999999999, 101.09999999999999, 101.09999999999999, 101.09999999999999, 101.09999999999999, 101.09999999999999, 101.09999999999999, 101.0, 101.0, 101.09999999999999, 101.09999999999999, 101.09999999999999, 101.0, 101.0, 101.0, 100.90000000000001, 100.8, 100.8, 100.8, 100.7, 100.7, 100.7, 100.7, 100.7, 100.7, 100.59999999999999, 100.59999999999999, 100.59999999999999, 100.59999999999999, 100.5, 100.40000000000001, 100.3, 100.2, 100.09999999999999, 100.0, 100.0, 99.900000000000006, 99.900000000000006, 99.799999999999997, 99.700000000000003, 99.700000000000003, 99.700000000000003, 99.799999999999997, 99.700000000000003, 99.700000000000003, 99.700000000000003, 99.700000000000003, 99.599999999999994, 99.599999999999994]
)
data2 = np.array( [57.200000000000003, 58.166699999999999, 58.166699999999999, 58.166699999999999, 58.166699999999999, 58.166699999999999, 58.166699999999999, 58.166699999999999, 59.133299999999998, 59.133299999999998, 59.133299999999998, 60.100000000000001, 59.133299999999998, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 59.133299999999998, 59.133299999999998, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 61.066699999999997, 60.100000000000001, 61.066699999999997, 61.066699999999997, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 59.133299999999998, 59.133299999999998, 60.100000000000001, 59.133299999999998, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001, 60.100000000000001])
class MyClass:
def __init__(self):
self.sleep = 0.1
tune = Autotune(MyClass())
tune.temps = data
print()
print(tune.get_periods())
peaks = tune.detect_peaks(data, show=True)
data = tune.smooth(data)
peaks = tune.detect_peaks(data, show=True)
valleys = tune.detect_peaks(data, valley=True, show=True)
tune.calculate_PID(data, peaks, valleys)
print(tune.Kp)
print(tune.Ki)
print(tune.Kd)