有matplotlib相当于MATLAB的datacursormode？
在MATLAB中，当用户鼠标hover时，可以使用datacursormode为graphics添加注释。 matplotlib中有这样的事吗？ 或者我需要使用matplotlib.text.Annotation写我自己的事件？
晚编辑/无耻插件：现在可以使用（function更多）作为mpldatacursor 。 调用mpldatacursor.datacursor()将为所有matplotlib艺术家启用它（包括对图像中z值的基本支持等）。
据我所知，还没有一个已经实现，但是写出类似的东西并不难：
import matplotlib.pyplot as plt class DataCursor(object): text_template = 'x: %0.2f\ny: %0.2f' x, y = 0.0, 0.0 xoffset, yoffset = -20, 20 text_template = 'x: %0.2f\ny: %0.2f' def __init__(self, ax): self.ax = ax self.annotation = ax.annotate(self.text_template, xy=(self.x, self.y), xytext=(self.xoffset, self.yoffset), textcoords='offset points', ha='right', va='bottom', bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5), arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0') ) self.annotation.set_visible(False) def __call__(self, event): self.event = event # xdata, ydata = event.artist.get_data() # self.x, self.y = xdata[event.ind], ydata[event.ind] self.x, self.y = event.mouseevent.xdata, event.mouseevent.ydata if self.x is not None: self.annotation.xy = self.x, self.y self.annotation.set_text(self.text_template % (self.x, self.y)) self.annotation.set_visible(True) event.canvas.draw() fig = plt.figure() line, = plt.plot(range(10), 'ro-') fig.canvas.mpl_connect('pick_event', DataCursor(plt.gca())) line.set_picker(5) # Tolerance in points
至less有一些人正在使用这个，我在下面添加了一个更新的版本。
新版本有一个更简单的用法和更多的文档（即一点点，至less）。
基本上你会使用它类似于这个：
plt.figure() plt.subplot(2,1,1) line1, = plt.plot(range(10), 'ro-') plt.subplot(2,1,2) line2, = plt.plot(range(10), 'bo-') DataCursor([line1, line2]) plt.show()
主要区别在于：a）不需要手动调用line.set_picker(...) ，b）不需要手动调用fig.canvas.mpl_connect ，c）该版本处理多个轴和多个数字。
from matplotlib import cbook class DataCursor(object): """A simple data cursor widget that displays the x,y location of a matplotlib artist when it is selected.""" def __init__(self, artists, tolerance=5, offsets=(-20, 20), template='x: %0.2f\ny: %0.2f', display_all=False): """Create the data cursor and connect it to the relevant figure. "artists" is the matplotlib artist or sequence of artists that will be selected. "tolerance" is the radius (in points) that the mouse click must be within to select the artist. "offsets" is a tuple of (x,y) offsets in points from the selected point to the displayed annotation box "template" is the format string to be used. Note: For compatibility with older versions of python, this uses the old-style (%) formatting specification. "display_all" controls whether more than one annotation box will be shown if there are multiple axes. Only one will be shown per-axis, regardless. """ self.template = template self.offsets = offsets self.display_all = display_all if not cbook.iterable(artists): artists = [artists] self.artists = artists self.axes = tuple(set(art.axes for art in self.artists)) self.figures = tuple(set(ax.figure for ax in self.axes)) self.annotations = {} for ax in self.axes: self.annotations[ax] = self.annotate(ax) for artist in self.artists: artist.set_picker(tolerance) for fig in self.figures: fig.canvas.mpl_connect('pick_event', self) def annotate(self, ax): """Draws and hides the annotation box for the given axis "ax".""" annotation = ax.annotate(self.template, xy=(0, 0), ha='right', xytext=self.offsets, textcoords='offset points', va='bottom', bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5), arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0') ) annotation.set_visible(False) return annotation def __call__(self, event): """Intended to be called through "mpl_connect".""" # Rather than trying to interpolate, just display the clicked coords # This will only be called if it's within "tolerance", anyway. x, y = event.mouseevent.xdata, event.mouseevent.ydata annotation = self.annotations[event.artist.axes] if x is not None: if not self.display_all: # Hide any other annotation boxes... for ann in self.annotations.values(): ann.set_visible(False) # Update the annotation in the current axis.. annotation.xy = x, y annotation.set_text(self.template % (x, y)) annotation.set_visible(True) event.canvas.draw() if __name__ == '__main__': import matplotlib.pyplot as plt plt.figure() plt.subplot(2,1,1) line1, = plt.plot(range(10), 'ro-') plt.subplot(2,1,2) line2, = plt.plot(range(10), 'bo-') DataCursor([line1, line2]) plt.show()
