在Jupyter Notebook中同时使用Matplotlib内联和QT

Question

I am using Jupyter (with IPython) to analyze research data, as well as export figures. I really like the notebook approach offered by Jupyter: when I revisit an experiment after a long time, I can easily see how the figures correspond to the data. This is of course using the inline backend.

However, when I want to explore new data, I prefer to use the QT backend. It is faster than the inline one, and allows to easily scale, zoom in and out, and nicely displays the X and Y coordinates in the bottom left corner. Moreover, I can use the QT backend to determine good x and y limits to use in the inline backend.

I have tried using the %matplotlib notebook magic, but it is simply too slow. For some experiments I am plotting ~500 spectra (each consists of ~1000 data points), which is already slow in the inline backend. Even with less data points, the notebook backend is just too slow to use.

Therefore, I would like to use both the QT backend, and the inline backend whenever I plot something. (So, whenever I execute a cell which plots data, it should both display the inline image, and pop up a QT backend window). This way, I still have a nice overview of plots in my notebook, while also allowing me to easily explore my data. Is there a way to achieve this?

Answer 1

This allows you to run QtConsole, plotting with the plotSin function, both inline and through the QtConsole.

import matplotlib
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline  

..

def plotChirp(Type, Exp, Rand):

    # Orignal Chirp Funciton From:
    # http://stackoverflow.com/questions/19410042/how-to-make-ipython-notebook-matplotlib-plot-inline
    x = np.linspace(0, 3*np.pi, Rand)
    plt.plot(x, np.sin(x**int(Exp)))
    plt.title('A simple chirp ' + Type)
    plt.show()

..

plotChirp("A", 5, 200) # Plots inline if you choose

%connect_info # For your own connection
%qtconsole

QtConsole opens and now you can call your function to plot externally..

Using %matplotlib qt allows for printing in a loop, but unfortunately it seems to overlap the plots. Looking into subplots as a possible solution.

%matplotlib qt
for i in range(0,2):

    if i == 0:
        plotChirp("B",1, 400)
    else:
        plotChirp("c",6, 1000)