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"""
===============
Multiprocessing
===============
Demo of using multiprocessing for generating data in one process and
plotting in another.
Written by Robert Cimrman
"""
import multiprocessing as mp
import time
import matplotlib.pyplot as plt
import numpy as np
# Fixing random state for reproducibility
np.random.seed(19680801)
# %%
#
# Processing Class
# ================
#
# This class plots data it receives from a pipe.
#
class ProcessPlotter:
def __init__(self):
self.x = []
self.y = []
def terminate(self):
plt.close('all')
def call_back(self):
while self.pipe.poll():
command = self.pipe.recv()
if command is None:
self.terminate()
return False
else:
self.x.append(command[0])
self.y.append(command[1])
self.ax.plot(self.x, self.y, 'ro')
self.fig.canvas.draw()
return True
def __call__(self, pipe):
print('starting plotter...')
self.pipe = pipe
self.fig, self.ax = plt.subplots()
timer = self.fig.canvas.new_timer(interval=1000)
timer.add_callback(self.call_back)
timer.start()
print('...done')
plt.show()
# %%
#
# Plotting class
# ==============
#
# This class uses multiprocessing to spawn a process to run code from the
# class above. When initialized, it creates a pipe and an instance of
# ``ProcessPlotter`` which will be run in a separate process.
#
# When run from the command line, the parent process sends data to the spawned
# process which is then plotted via the callback function specified in
# ``ProcessPlotter:__call__``.
#
class NBPlot:
def __init__(self):
self.plot_pipe, plotter_pipe = mp.Pipe()
self.plotter = ProcessPlotter()
self.plot_process = mp.Process(
target=self.plotter, args=(plotter_pipe,), daemon=True)
self.plot_process.start()
def plot(self, finished=False):
send = self.plot_pipe.send
if finished:
send(None)
else:
data = np.random.random(2)
send(data)
def main():
pl = NBPlot()
for _ in range(10):
pl.plot()
time.sleep(0.5)
pl.plot(finished=True)
if __name__ == '__main__':
if plt.get_backend() == "MacOSX":
mp.set_start_method("forkserver")
main()
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