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I wish to run a simulation while at the same time output its progress in a plot. I've been looking through a lot of examples of threading and multiprocessing, but they are all pretty complex. So I thought with Python's new asyncio library this should be easier.

I found an example (How to use 'yield' inside async function?) and modified it for my cause:

import matplotlib.pyplot as plt import asyncio import numpy as np class DataAnalysis(): def __init__(self): # asyncio so we can plot data and run simulation in parallel loop = asyncio.get_event_loop() try: loop.run_until_complete(self.plot_reward()) finally: loop.run_until_complete( loop.shutdown_asyncgens()) # see: https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.AbstractEventLoop.shutdown_asyncgens loop.close() async def async_generator(self): for i in range(3): await asyncio.sleep(.4) yield i * i async def plot_reward(self): # Prepare the data x = np.linspace(0, 10, 100) # Plot the data plt.plot(x, x, label='linear') #plt.show() # add lines to plot async for i in self.async_generator(): print(i) # Show the plot plt.show() if __name__ == '__main__': DataAnalysis()

Question

I added a simple plt.show() and the program still freezes. I thought with asyncio I could run it in parallel? Obviously my knowledge is still lacking. An example that does the following would be really helpful:

  • Add a line to a plot (of matplotlib) everytime async_generator returns a value.
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1 Answer 1

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First of all, I misunderstood asyncio, it doesn't make run things in parallel (use asyncio for parallel tasks).

It seems the only thing that worked for me was plt.pause(0.001) (Plotting in a non-blocking way with Matplotlib). plt.draw() opened a window, but it didn't show anything and plt.show freezes the program. It seems that plt.show(block=False) is deprecated and using plt.ion gives the problem that the final result closes when the program is finished. Also await asyncio.sleep(0.1) didn't make the plot draw a line.

Working code

import matplotlib.pyplot as plt import asyncio import matplotlib.cbook import warnings warnings.filterwarnings("ignore",category=matplotlib.cbook.mplDeprecation) class DataAnalysis(): def __init__(self): # asyncio so we can plot data and run simulation in parallel loop = asyncio.get_event_loop() try: loop.run_until_complete(self.plot_reward()) finally: loop.run_until_complete( loop.shutdown_asyncgens()) # see: https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.AbstractEventLoop.shutdown_asyncgens loop.close() # keep plot window open plt.show() async def async_generator(self): for i in range(3): await asyncio.sleep(.4) yield i * i async def plot_reward(self): #plt.ion() # enable interactive mode # receive dicts with training results async for i in self.async_generator(): print(i) # update plot if i == 0: plt.plot([2, 3, 4]) elif i == 1: plt.plot([3, 4, 5]) #plt.draw() plt.pause(0.1) #await asyncio.sleep(0.4) if __name__ == '__main__': da = DataAnalysis()

Notes

  • You get however a deprecated message: python3.6/site-packages/matplotlib/backend_bases.py:2445: MatplotlibDeprecationWarning: Using default event loop until function specific to this GUI is implemented warnings.warn(str, mplDeprecation), which you can suppress with: warnings.filterwarnings().

  • I'm not sure if asyncio was actually necessary for my use case...

  • Difference between threading and multiprocessing for who's interested: Multiprocessing vs Threading Python

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