blitting doesn't work - would be useful to speed up graph draw for faster realtime-graphs

Bắt đầu bằng cách tái hiện notebook được cung cấp với các phiên bản JupyterLab và ipympl được liệt kê, đồng thời so sánh fig.canvas.draw() với đường dẫn blitting. Theo dõi cách copy_from_bbox, restore_region, draw_artist và blit được xử lý, sau đó xác minh rằng các lần cập nhật slider làm mới biểu đồ một cách rõ ràng với tốc độ khung hình được cải thiện mà không thực hiện một draw đầy đủ.

I have the following code in jupyterlab. This allows me to move a slider and update a graph in realtime. However the framerate is quite low (1fps) if I call fig.canvas.draw(). I therefore tried blitting, however it does not seem to affect the graph. Is this supposed to work with ipympl?

Many thanks for your help.

Environment:
Jupyterlab==1.2.6
ipympl==0.5.6
jupyter labextension list:

@ibqn/jupyterlab-codecellbtn v0.1.3 enabled ok
@jupyter-widgets/jupyterlab-manager v1.1.0 enabled ok
@jupyterlab/google-drive v1.0.0 enabled ok
@jupyterlab/toc v1.0.1 enabled ok
jupyter-matplotlib v0.7.2 enabled ok
jupyterlab-plotly v1.5.2 enabled ok
plotlywidget v1.5.2 enabled ok

import pandas as pd, numpy as np
import time
import matplotlib.pyplot as plt

from ipywidgets import interact, interactive, fixed, interact_manual, Layout, VBox, HBox
import ipywidgets as widgets

from itertools import count
%matplotlib widget


blit = False # False works, True doesn't.

plt.close('all')
plt.ioff()

output = widgets.Output(layout={'width': '700px', 'height': '300px'})
fig, axs= plt.subplots(3, 2, figsize=(10, 8), sharex=True)
fig.canvas.header_visible = False
fig.canvas.toolbar_visible = False


for i in range(3):    
    axs[i,0].set_ylim(-1.5,1.5)
    axs[i,0].set_xlim(0,20)
    
# index giver
x_value = count()

# expanding dataset
x, y = [], []

# initialise dummy data
[x.append(next(x_value)) for i in range(2)]
[y.append([1]*3) for i in range(2)]

# setup desired and actual angle plots
col_names = ['col1', 'col2', 'col3']
ax_df = pd.DataFrame(index=x,columns=col_names, data=y).plot(subplots=True, ax=axs[:,0])

if blit:
    bgs = []
    for ax in ax_df:
        # cache the background
        ax_background = fig.canvas.copy_from_bbox(ax.bbox)
        bgs.append(ax_background)
    
    fig.canvas.draw()  # initial draw required

# monitor framerate
t_start = time.time()   

# event handler
def on_value_changed(change):    
    with output:    
        next_x = next(x_value) # generate next x axis value
        x.append(next_x)
        y.append([change.new]*3) 

        for i in range(3):            
            if blit:              
                # update data
                line = ax_df[i].get_lines()[0]
                line.set_data(x, pd.DataFrame(y).iloc[:,i])
                
                # restore background
                fig.canvas.restore_region(bgs[i])
                
                # redraw just the points
                ax_df[i].draw_artist(line)

                # fill in the axes rectangle
                fig.canvas.blit(ax_df[i].bbox)
                
            else:
                # update data
                ax_df[i].get_lines()[0].set_data(x, pd.DataFrame(y).iloc[:,i])

                # rescale view
                ax_df[i].autoscale_view(None,'x',None)
                ax_df[i].relim()
            
        fig.canvas.flush_events()
        
        if not blit:
            fig.canvas.draw() # this slows down framerate, not required for blit

        print(f"FPS: {round(next_x/(time.time() - t_start),2)}", end=", ")

sliders = []
int_slider = widgets.FloatSlider(description="test", 
                                 min=-1, max=1, 
                                 value = 0, continuous_update=True,
                                 orientation="horizontal",                                      
                                 layout=widgets.Layout(width="500px", height="20px"))    
int_slider.observe(on_value_changed, names="value")
sliders = widgets.VBox([int_slider, fig.canvas, output])
display(sliders)