我的建议是使用python multiprocessing 模块而不是线程模块.我只能对您的示例代码进行少量修改，就可以成功地将matplotlib绘图卸载到子进程中，同时继续进行主进程中的控制流(请参见下面的代码).

My suggestion here is to use the python multiprocessing module instead of the threading module. I've been able to perform only slight modifications to your sample code and successfully offload the matplotlib plotting to a child process while the control flow in the main process continues (see code below).

如果您希望子流程进行回访，我建议阅读多处理文档或有关该主题的大量博客文章.在较大的代码控制流的上下文中与父级(在问题中没有完全描述)进行交互.请注意，多处理具有规避python 全局解释器锁允许您利用多核计算机体系结构.

I do suggest reading the multiprocessing documentation, or any of the plethora of blog posts on the subject, if you want the child process(es) to communicate back & forth with the parent in the context of your larger code control flow (which isn't fully described in your question). Note that multiprocessing has the added advantage of circumventing the python global interpreter lock & allowing you to exploit multi-core computer architectures.

#a slight modification of your code using multiprocessing
import matplotlib
matplotlib.use("qt4agg")
import matplotlib.pyplot as plt 
#import threading
#let's try using multiprocessing instead of threading module:
import multiprocessing
import time

#we'll keep the same plotting function, except for one change--I'm going to use the multiprocessing module to report the plotting of the graph from the child process (on another core):
def plot_a_graph():
    f,a = plt.subplots(1)
    line = plt.plot(range(10))
    print multiprocessing.current_process().name,"starting plot show process" #print statement preceded by true process name
    plt.show() #I think the code in the child will stop here until the graph is closed
    print multiprocessing.current_process().name,"plotted graph" #print statement preceded by true process name
    time.sleep(4)

#use the multiprocessing module to perform the plotting activity in another process (i.e., on another core):
job_for_another_core = multiprocessing.Process(target=plot_a_graph,args=())
job_for_another_core.start()

#the follow print statement will also be modified to demonstrate that it comes from the parent process, and should happen without substantial delay as another process performs the plotting operation:
print multiprocessing.current_process().name, "The main process is continuing while another process deals with plotting."