拾取事件演示

您可以通过设置艺术家的“Picker”属性来启用拾取（例如，matplotlib line2d、text、patch、polygon、axesImage等）

picker属性有多种含义：

• None -已为此艺术家禁用拾取（默认）

• 布尔-如果 True 然后将启用拾取，并且如果鼠标事件位于艺术家上方，艺术家将触发拾取事件。

  设置 pickradius 将在点中添加epsilon容差，并且如果事件的数据在鼠标事件的epsilon范围内，则艺术家将触发事件。对于线和面片集合之类的一些美工人员，美工人员可以向生成的pick事件提供附加数据，例如，pick事件epsilon内的数据索引

• 函数-如果picker是可调用的，则它是用户提供的函数，用于确定艺术家是否被鼠标事件击中。

  点击，道具=选取器（艺术家，鼠标事件）

  以确定命中测试。如果鼠标事件位于艺术家上方，则返回hit=True，并且props是要添加到PickEvent属性的属性字典。

通过设置“Picker”属性启用艺术家拾取后，需要连接到“Figure Canvas Pick_”事件以获取鼠标按下事件的拾取回调。例如，

def pick_处理程序（事件）：

mouseEvent=event.mouseEvent artist=event.artist现在就做点什么……

传递给回调的pick事件（matplotlib.backend_base.pick event）始终使用两个属性激发：

mouseEvent-生成pick事件的鼠标事件。这个

鼠标事件依次具有x和y（显示空间中的坐标，例如从左到下的像素）和x data、y data（数据空间中的坐标）等属性。此外，您还可以获取有关按下了哪些按钮、按下了哪些键、鼠标位于哪个轴上等的信息。有关详细信息，请参阅matplotlib.backend_base.mouseEvent。

artist-生成pick事件的matplotlib.artist。

此外，某些艺术家（如Line2d和PatchCollection）可以将其他元数据（如索引）附加到符合选取器标准的数据中（例如，行中所有在指定epsilon公差范围内的点）。

下面的例子说明了这些方法中的每一种。

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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


# Fixing random state for reproducibility
np.random.seed(19680801)


def pick_simple():
    # 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=True, pickradius=5)

    # pick the rectangle
    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:', np.column_stack([xdata[ind], 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)


def pick_custom_hit():
    # 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(d <= maxd)
        if len(ind):
            pickx = xdata[ind]
            picky = 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)


def pick_scatter_plot():
    # 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, x[ind], y[ind])

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


def pick_image():
    # picking images (matplotlib.image.AxesImage)
    fig, ax = plt.subplots()
    ax.imshow(rand(10, 5), extent=(1, 2, 1, 2), picker=True)
    ax.imshow(rand(5, 10), extent=(3, 4, 1, 2), picker=True)
    ax.imshow(rand(20, 25), extent=(1, 2, 3, 4), picker=True)
    ax.imshow(rand(30, 12), extent=(3, 4, 3, 4), picker=True)
    ax.set(xlim=(0, 5), ylim=(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)


if __name__ == '__main__':
    pick_simple()
    pick_custom_hit()
    pick_scatter_plot()
    pick_image()
    plt.show()