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					84 lines
				
				1.9 KiB
			
		
		
			
		
	
	
					84 lines
				
				1.9 KiB
			| 
											6 years ago
										 | #!/usr/bin/env python
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|  | import os
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|  | import sys
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|  | import matplotlib.pyplot as plt
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|  | import numpy as np
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|  | import cereal.messaging as messaging
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|  | import time
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|  | 
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|  | 
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|  | # tool to plot one or more signals live. Call ex:
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|  | #./rqplot.py log.carState.vEgo log.carState.aEgo
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|  | 
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|  | # TODO: can this tool consume 10x less cpu?
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|  | 
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|  | def recursive_getattr(x, name):
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|  |    l = name.split('.')
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|  |    if len(l) == 1:
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|  |      return getattr(x, name)
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|  |    else:
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|  |      return recursive_getattr(getattr(x, l[0]), ".".join(l[1:]) )
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|  | 
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|  | 
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|  | if __name__ == "__main__":
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|  |   poller = messaging.Poller()
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|  | 
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|  |   services = []
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|  |   fields = []
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|  |   subs = []
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|  |   values = []
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|  | 
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|  |   plt.ion()
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|  |   fig, ax = plt.subplots()
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|  |   #fig = plt.figure(figsize=(10, 15))
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|  |   #ax = fig.add_subplot(111)
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|  |   ax.grid(True)
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|  |   fig.canvas.draw()
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|  | 
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|  |   subs_name = sys.argv[1:]
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|  |   lines = []
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|  |   x, y = [], []
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|  |   LEN = 500
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|  | 
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|  |   for i, sub in enumerate(subs_name):
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|  |     sub_split = sub.split(".")
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|  |     services.append(sub_split[0])
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|  |     fields.append(".".join(sub_split[1:]))
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|  |     subs.append(messaging.sub_sock(sub_split[0], poller))
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|  | 
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|  |     x.append(np.ones(LEN)*np.nan)
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|  |     y.append(np.ones(LEN)*np.nan)
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|  |     lines.append(ax.plot(x[i], y[i])[0])
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|  | 
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|  |   for l in lines:
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|  |     l.set_marker("*")
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|  | 
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|  |   cur_t = 0.
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|  |   ax.legend(subs_name)
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|  |   ax.set_xlabel('time [s]')
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|  | 
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|  |   while 1:
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|  |     print(1./(time.time() - cur_t))
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|  |     cur_t = time.time()
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|  |     for i, s in enumerate(subs):
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|  |       msg = messaging.recv_sock(s)
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|  |       #msg = messaging.recv_one_or_none(s)
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|  |       if msg is not None:
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|  |         x[i] = np.append(x[i], getattr(msg, 'logMonoTime') / float(1e9))
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|  |         x[i] = np.delete(x[i], 0)
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|  |         y[i] = np.append(y[i], recursive_getattr(msg, subs_name[i]))
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|  |         y[i] = np.delete(y[i], 0)
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|  | 
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|  |         lines[i].set_xdata(x[i])
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|  |         lines[i].set_ydata(y[i])
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|  | 
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|  |     ax.relim()
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|  |     ax.autoscale_view(True, scaley=True, scalex=True)
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|  | 
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|  |     fig.canvas.blit(ax.bbox)
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|  |     fig.canvas.flush_events()
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|  | 
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|  |     # just a bit of wait to avoid 100% CPU usage
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|  |     time.sleep(0.001)
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|  | 
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