pytec: Set autotune and plot as entry points
For easier access to `plot.py` and `autotune.py` scripts.
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214
pytec/plot.py
214
pytec/plot.py
@ -4,125 +4,131 @@ 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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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 main():
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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(),
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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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nonlocal 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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plt.show()
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quit = True
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thread.join()
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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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if __name__ == "__main__":
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main()
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@ -9,4 +9,11 @@ setup(
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license="GPLv3",
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install_requires=["setuptools"],
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packages=find_packages(),
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entry_points={
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"gui_scripts": [
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"autotune = autotune:main",
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"plot = plot:main",
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]
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},
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py_modules=["autotune", "plot"],
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)
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