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# Pick Event Demo

You can enable picking by setting the “picker” property of an artist (for example, a matplotlib Line2D, Text, Patch, Polygon, AxesImage, etc…)

There are a variety of meanings of the picker property

> None - picking is disabled for this artist (default)

> boolean - if True then picking will be enabled and the
>   artist will fire a pick event if the mouse event is over the artist
> float - if picker is a number it is interpreted as an
>   epsilon tolerance in points and the artist will fire off an event if it’s data is within epsilon of the mouse event. For some artists like lines and patch collections, the artist may provide additional data to the pick event that is generated, for example, the indices of the data within epsilon of the pick event
> function - if picker is callable, it is a user supplied

>   function which determines whether the artist is hit by the mouse event.

>   > hit, props = picker(artist, mouseevent)

>   to determine the hit test. If the mouse event is over the artist, return hit=True and props is a dictionary of properties you want added to the PickEvent attributes

After you have enabled an artist for picking by setting the “picker” property, you need to connect to the figure canvas pick_event to get pick callbacks on mouse press events. For example,

> def pick_handler(event):
>   mouseevent = event.mouseevent artist = event.artist # now do something with this…

The pick event (matplotlib.backend_bases.PickEvent) which is passed to your callback is always fired with two attributes:

> mouseevent - the mouse event that generate the pick event. The
>   mouse event in turn has attributes like x and y (the coordinates in display space, such as pixels from left, bottom) and xdata, ydata (the coords in data space). Additionally, you can get information about which buttons were pressed, which keys were pressed, which Axes the mouse is over, etc. See matplotlib.backend_bases.MouseEvent for details.

> artist - the matplotlib.artist that generated the pick event.

Additionally, certain artists like Line2D and PatchCollection may attach additional meta data like the indices into the data that meet the picker criteria (for example, all the points in the line that are within the specified epsilon tolerance)

The examples below illustrate each of these methods.

    from __future__ import print_function
    import matplotlib.pyplot as plt
    from matplotlib.lines import Line2D
    from matplotlib.patches import Rectangle
    from matplotlib.text import Text
    from matplotlib.image import AxesImage
    import numpy as np
    from numpy.random import rand

    if 1:  # simple picking, lines, rectangles and text
        fig, (ax1, ax2) = plt.subplots(2, 1)
        ax1.set_title('click on points, rectangles or text', picker=True)
        ax1.set_ylabel('ylabel', picker=True, bbox=dict(facecolor='red'))
        line, = ax1.plot(rand(100), 'o', picker=5)  # 5 points tolerance

        # pick the rectangle
        bars = ax2.bar(range(10), rand(10), picker=True)
        for label in ax2.get_xticklabels():  # make the xtick labels pickable
            label.set_picker(True)

        def onpick1(event):
            if isinstance(event.artist, Line2D):
                thisline = event.artist
                xdata = thisline.get_xdata()
                ydata = thisline.get_ydata()
                ind = event.ind
                print('onpick1 line:', zip(np.take(xdata, ind), np.take(ydata, ind)))
            elif isinstance(event.artist, Rectangle):
                patch = event.artist
                print('onpick1 patch:', patch.get_path())
            elif isinstance(event.artist, Text):
                text = event.artist
                print('onpick1 text:', text.get_text())

        fig.canvas.mpl_connect('pick_event', onpick1)

    if 1:  # picking with a custom hit test function
        # you can define custom pickers by setting picker to a callable
        # function.  The function has the signature
        #
        #  hit, props = func(artist, mouseevent)
        #
        # to determine the hit test.  if the mouse event is over the artist,
        # return hit=True and props is a dictionary of
        # properties you want added to the PickEvent attributes

        def line_picker(line, mouseevent):
            """
            find the points within a certain distance from the mouseclick in
            data coords and attach some extra attributes, pickx and picky
            which are the data points that were picked
            """
            if mouseevent.xdata is None:
                return False, dict()
            xdata = line.get_xdata()
            ydata = line.get_ydata()
            maxd = 0.05
            d = np.sqrt((xdata - mouseevent.xdata)**2. + (ydata - mouseevent.ydata)**2.)

            ind = np.nonzero(np.less_equal(d, maxd))
            if len(ind):
                pickx = np.take(xdata, ind)
                picky = np.take(ydata, ind)
                props = dict(ind=ind, pickx=pickx, picky=picky)
                return True, props
            else:
                return False, dict()

        def onpick2(event):
            print('onpick2 line:', event.pickx, event.picky)

        fig, ax = plt.subplots()
        ax.set_title('custom picker for line data')
        line, = ax.plot(rand(100), rand(100), 'o', picker=line_picker)
        fig.canvas.mpl_connect('pick_event', onpick2)

    if 1:  # picking on a scatter plot (matplotlib.collections.RegularPolyCollection)

        x, y, c, s = rand(4, 100)

        def onpick3(event):
            ind = event.ind
            print('onpick3 scatter:', ind, np.take(x, ind), np.take(y, ind))

        fig, ax = plt.subplots()
        col = ax.scatter(x, y, 100*s, c, picker=True)
        #fig.savefig('pscoll.eps')
        fig.canvas.mpl_connect('pick_event', onpick3)

    if 1:  # picking images (matplotlib.image.AxesImage)
        fig, ax = plt.subplots()
        im1 = ax.imshow(rand(10, 5), extent=(1, 2, 1, 2), picker=True)
        im2 = ax.imshow(rand(5, 10), extent=(3, 4, 1, 2), picker=True)
        im3 = ax.imshow(rand(20, 25), extent=(1, 2, 3, 4), picker=True)
        im4 = ax.imshow(rand(30, 12), extent=(3, 4, 3, 4), picker=True)
        ax.axis([0, 5, 0, 5])

        def onpick4(event):
            artist = event.artist
            if isinstance(artist, AxesImage):
                im = artist
                A = im.get_array()
                print('onpick4 image', A.shape)

        fig.canvas.mpl_connect('pick_event', onpick4)

    plt.show()

Total running time of the script: ( 0 minutes 0.268 seconds)

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