Matplotlib event and replotting

Question

I want to be able to create a plot, press one button or another depending on what the plot shows, and then plot the following object. However, I am having some trouble wih it: it seems I can't make it "wait" untill a button is pressed. Also, I am wondering if it would be possible to pass some parameters to the press_event, like a path to save something.

Here is the scheme of the program in case it helps. Thanks a lot in advance!

# event definition
def ontype(event):
    if event.key == '1':
        do stuff 1
        plt.savefig(...)
        plt.clf()
    elif event.key == '2':
        do stuff 2
        plt.savefig(...)
        plt.clf()
    elif event.key == '3':
        do stuff 3
        plt.savefig(...)
        plt.clf()

# main program
...stuff
create figure
plt.show()
plt.gcf().canvas.mpl_connect('key_press_event',ontype)

Answer 1

You must call plt.gcf().canvas.mpl_connect('key_press_event',ontype) before plt.show(). In non-interactive mode, the execution waits at plt.show() until the plot-window is closed.

import pylab as plt

# event definition
def ontype(event):
    if event.key == '1':
        print "1"
    elif event.key == '2':
        print "2"
    elif event.key == '3':
        print "3"

# main program
plt.plot([1,6,3,8,7])
plt.gcf().canvas.mpl_connect('key_press_event',ontype)
plt.show()

Alternatively, replace in your sample plt.show() to plt.ion(), which enables interactive mode. But it depends on your specific needs which solution you prefer.

Edit

New example using Tkinter

import random
import matplotlib
matplotlib.use('TkAgg')
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
from matplotlib.figure import Figure

try:
    import Tkinter as Tk
except ImportError:
    import tkinter as Tk
import tkMessageBox

class PlotClassifier(Tk.Tk):
    def __init__(self, plot_generator, arguments, classes, classification_callback, *args, **kwargs):
        Tk.Tk.__init__(self, *args, **kwargs)
        self.title("Plot classifier, working on %i plots" % len(arguments))
        #self.label = Tk.Label(text="Plot classifier, working on %i plots" % len(arguments))
        #self.label.pack(padx=10, pady=10)
        self._plot_generator = plot_generator
        self._arguments = arguments
        self._classes = [str(x) for x in classes]
        self._classification_callback = classification_callback
        self._setup_gui()

    def _setup_gui(self):
        #self.columnconfigure(0, minsize=100, weight=2)
        #self.columnconfigure(1, minsize=500, weight=8)
        f = Figure()
        self._ax = f.add_subplot(111)
        buttons_frame = Tk.Frame(self)
        buttons_frame.pack(side=Tk.TOP, fill=Tk.BOTH, expand=True)
        buttons_class = []
        for i, cls in enumerate(self._classes):
            buttons_class.append(Tk.Button(master=buttons_frame, text=cls, 
                                           command=lambda x=i: self.button_classification_callback(self._current_args, x)))
            buttons_class[-1].pack(side=Tk.LEFT)
        button_quit = Tk.Button(master=buttons_frame, text='Quit', command=self.destroy)
        button_quit.pack(side=Tk.RIGHT) #.grid(row=0,column=0)

        self._canvas = FigureCanvasTkAgg(f, master=self)
        self._canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1) #.grid(row=0, column=1, rowspan=3) #
        self._canvas.show()

        toolbar = NavigationToolbar2TkAgg( self._canvas, self )
        toolbar.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1) #.grid(row=3, column=1) #
        toolbar.update()

    def button_classification_callback(self, args, class_idx):
        self._classification_callback(args, self._classes[class_idx])
        self.classify_next_plot()

    def classify_next_plot(self):
        try:
            self._current_args = self._arguments.pop(0)
            self._ax.cla()
            self._plot_generator(self._ax, *self._current_args)
            self._canvas.draw()
        except IndexError:
            tkMessageBox.showinfo("Complete!", "All plots were classified")
            self.destroy()        

def create_plot(ax, factor):
    ax.plot([(i*factor) % 11 for i in range(100)])

def announce_classification(arguments, class_):
    print arguments, class_

if __name__ == "__main__":
    classes = ["Class %i"%i for i in range(1, 6)]
    arguments_for_plot = [[random.randint(1,10)] for x in range(10)]
    root = PlotClassifier(create_plot, arguments_for_plot, classes, classification_callback=announce_classification)
    root.after(50, root.classify_next_plot)
    root.mainloop()

The class takes as arguments: * a callback to create each plot * a list of lists of arguments for each plot to generate (might each be an empty list) * a list of class-names. For each class, a button is created * a callback that is called each time a classification has been performed

Any feedback would be appreciated.

*EDIT 2 * For your comment, a slightly modified version. For every iteration of the loop, a new window is opened

import random
import matplotlib
matplotlib.use('TkAgg')
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
from matplotlib.figure import Figure

try:
    import Tkinter as Tk
except ImportError:
    import tkinter as Tk
import tkMessageBox

class PlotClassifier(Tk.Tk):
    def __init__(self, plot_generator, arguments, classes, *args, **kwargs):
        Tk.Tk.__init__(self, *args, **kwargs)
        self.title("Plot classifier")
        self._plot_generator = plot_generator
        self._arguments = arguments
        self._classes = [str(x) for x in classes]
        self.class_ = None
        self._setup_gui()

    def _setup_gui(self):
        #self.columnconfigure(0, minsize=100, weight=2)
        #self.columnconfigure(1, minsize=500, weight=8)
        f = Figure()
        self._ax = f.add_subplot(111)
        buttons_frame = Tk.Frame(self)
        buttons_frame.pack(side=Tk.TOP, fill=Tk.X, expand=True)
        buttons_class = []
        for i, cls in enumerate(self._classes):
            buttons_class.append(Tk.Button(master=buttons_frame, text=cls, 
                                           command=lambda x=i: self.button_classification_callback(x)))
            buttons_class[-1].pack(side=Tk.LEFT)
        button_quit = Tk.Button(master=buttons_frame, text='Quit', command=self.destroy)
        button_quit.pack(side=Tk.RIGHT) #.grid(row=0,column=0)

        self._canvas = FigureCanvasTkAgg(f, master=self)
        self._canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1) #.grid(row=0, column=1, rowspan=3) #
        self._canvas.show()

        toolbar = NavigationToolbar2TkAgg( self._canvas, self )
        toolbar.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1) #.grid(row=3, column=1) #
        toolbar.update()

    def button_classification_callback(self, class_idx):
        self.class_ = self._classes[class_idx]
        self.destroy()

    def classify_plot(self):
        self._ax.cla()
        self._plot_generator(self._ax, *self._arguments)
        self._canvas.draw()
        self.mainloop()
        return self.class_

def create_plot(ax, factor):
    ax.plot([(i*factor) % 11 for i in range(100)])


if __name__ == "__main__":
    classes = ["Class %i"%i for i in range(1, 6)]
    arguments_for_plot = [[random.randint(1,10)] for x in range(10)]
    for args in arguments_for_plot:
        classifier = PlotClassifier(create_plot, args, classes)
        class_ = classifier.classify_plot()
        print args, class_
        if class_ is None:
            break

This helps to fit into your own for-loop, but you still have to give a function to do the plotting after the GUI was created.