Probably like your CSS, my headings and paragraphs have bottom margins by default. import timeĪx.Using flexbox in CSS, I've dynamically aligned the right column content with the image on the left hand side. The drawnow(makeFig) line can be replaced with a makeFig() plt.draw() sequence and it still works OK.Īn example use-case to plot CPU usage in real-time. #makeFig() The drawnow(makeFig) command can be replaced Plt.scatter(xList,yList) # I think you meant this Here is a version that I got to work on my system. It is especially useful when data is generated by remote headless server processes. R.data_source.stream()Ĭurdoc().add_periodic_callback(update, 100)īokeh shows the result in a web browser via websocket communications. Here is a bokeh version of the code in the question: from otting import curdoc, figure IMO, it is a good alternative at least for real-time plots. draw() to update.Īnother option is to go with bokeh. You can try setting the keyword-argument block like this: plt.show(block=False) once at the beginning and then use. To resolve that problem just call plt.show() once after your loop. You should be able to test that: If you close the window and then another window should pop up. The program will stop at that point and only resume once you close the window. The problem seems to be that you expect plt.show() to show the window and then to return. Python-drawnow is a thin wrapper around plt.draw but provides the ability to confirm (or debug) after figure display. Y.append(temp_y) # or any arbitrary update to your figure's data Plt.scatter(x, y) # I think you meant this This provides an interface similar to MATLAB's drawnow - you can easily update a figure.Īn example for your use case: import matplotlib.pyplot as plt I know this question is old, but there's now a package available called drawnow on GitHub as "python-drawnow". You can also explore blit for even better performance as in FuncAnimation documentation.Īn example from the blit documentation: import numpy as npĪni = FuncAnimation(fig, update, frames=np.linspace(0, 2*np.pi, 128), Line, = ot_date(x_data, y_data, '-')Īnimation = FuncAnimation(figure, update, interval=200) It plots current time with a random number in [0, 100) every 200ms infinitely, while also handling auto rescaling of the view: from datetime import datetimeįrom matplotlib.animation import FuncAnimation TL DR: you may want to use matplotlib.animation ( as mentioned in documentation).Īfter digging around various answers and pieces of code, this in fact proved to be a smooth way of drawing incoming data infinitely for me. It is not only slow, but also causes focus to be grabbed upon each update (I had a hard time stopping the plotting python process). The top (and many other) answers were built upon plt.pause(), but that was an old way of animating the plot in matplotlib. If I make these changes to your example it works for me and I see each point appearing one at a time. You also might want to include a small time delay (e.g., time.sleep(0.05)) in the loop so that you can see the plots happening. ![]() What I would do is use pyplot.draw() instead. Show is probably not the best choice for this. ![]() So your code should look like this, and it will work import matplotlib.pyplot as plt ![]() Real time matplotlib plot is not working while still in a loop You should get a beautiful, smoothly moving graph: Copy-paste this code in a new python-file, and run it. tStyle(QStyleFactory.create('Plastique')) MySrc.data_signal.emit(y) # <- Here you emit a signal! # Believe me, if you don't do this right, things # send data to your GUI in a thread-safe way. # You need to setup a signal slot mechanism, to Self.line1_t_data(np.append(self.n, self.n), np.append(self.y, self.y)) ![]() alpha) * gen.randn(2, n) * sigma + alpha * old_delta In particular, using blit to avoid redrawing the background on every frame can give you substantial speed gains (~10x): #!/usr/bin/env pythonĭef randomwalk(dims=(256, 256), n=20, sigma=5, alpha=0.95, seed=1):ĭelta = (1. If you're interested in realtime plotting, I'd recommend looking into matplotlib's animation API. Note the call to plt.pause(0.05), which both draws the new data and runs the GUI's event loop (allowing for mouse interaction). Here's the working version of the code in question (requires at least version Matplotlib 1.1.0 from ): import numpy as np
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