- 1 year ago
How can I use multiprocessing to plot data while sampling sensors?
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To use multiprocessing
to plot data while sampling sensors in Python, you can create separate processes for data sampling and data plotting, and communicate between them using a Queue
or shared memory.
Here's an example to illustrate the concept:
pythonimport multiprocessing as mp
import matplotlib.pyplot as plt
import time
def sensor_sampling(queue):
# Simulate sensor sampling
while True:
# Sample sensor data
data = ... # Get sensor data
# Put data in the queue
queue.put(data)
time.sleep(1) # Sample data every second
def plot_data(queue):
# Initialize plot
plt.ion() # Turn on interactive mode
fig, ax = plt.subplots()
line, = ax.plot([], [])
# Update plot with data from the queue
while True:
# Get data from the queue
data = queue.get()
# Update plot
# ... Update plot with the new data
# Redraw the plot
fig.canvas.draw()
fig.canvas.flush_events()
if __name__ == '__main__':
# Create a queue to communicate between processes
queue = mp.Queue()
# Create processes for data sampling and plotting
sampling_process = mp.Process(target=sensor_sampling, args=(queue,))
plot_process = mp.Process(target=plot_data, args=(queue,))
# Start the processes
sampling_process.start()
plot_process.start()
# Wait for the processes to finish
sampling_process.join()
plot_process.join()
In this example, the sensor_sampling
function continuously samples sensor data and puts it into the queue. The plot_data
function continuously retrieves data from the queue and updates the plot with the new data.
By creating separate processes, the sensor sampling and plot updating can happen concurrently, allowing real-time plotting while continuously sampling the sensor data.
Note that you'll need to replace the placeholder code (...
) with your actual code for sensor sampling and plot updating.