问题描述
在MATLAB中，当用户将鼠标悬停在图形上时，可以使用datacursormode向图形添加注释. matplotlib中有这种东西吗?还是我需要使用matplotlib.text.Annotation编写自己的事件?
In MATLAB, one can use datacursormode to add annotation to a graph when user mouses over. Is there such thing in matplotlib? Or I need to write my own event using matplotlib.text.Annotation?
推荐答案
Late Edit/Shameless Plug:现在可以(具有更多功能)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).
Late Edit / Shameless Plug: This is now available (with much more functionality) as mpldatacursor. Calling mpldatacursor.datacursor() will enable it for all matplotlib artists (including basic support for z-values in images, etc).
据我所知，还没有实现，但是写类似的东西并不难:
As far as I know, there isn't one already implemented, but it's not too hard to write something similar:
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
似乎至少有人在使用它，所以我在下面添加了更新版本.
As it seems like at least a few people are using this, I've added an updated version below.
新版本使用更简单，文档更多(至少一点点).
The new version has a simpler usage and a lot more documentation (i.e. a tiny bit, at least).
基本上，您将使用类似于以下的方式:
Basically you'd use it similar to this:
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)无需手动调用line.set_picker(...)，b)无需手动调用fig.canvas.mpl_connect，c)此版本可处理多个轴和多个图形.
The main differences are that a) there's no need to manually call line.set_picker(...), b) there's no need to manually call fig.canvas.mpl_connect, and c) this version handles multiple axes and multiple figures.
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()
这篇关于是否有与MATLAB的datacursormode等效的matplotlib?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！