【转】用matplotlib绘制十字星光标
在matplotlib中绘制类似股票软件里面的十字星光标比较简单，这里我们来看看几种内置的光标：
1、单子图上面的十字星光标
from matplotlib.widgets import Cursor
import matplotlib.pyplot as plt
import numpy as npfig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)
t = np.arange(0.0, 2.0, 0.01)
ax1.plot(t, np.sin(2*np.pi*t))
ax2.plot(t, np.sin(4*np.pi*t))cursor = Cursor(ax1, useblit=True, color='r', lw=0.5)
plt.show()看看效果
2、多子图上面同时出现十字星光标
from matplotlib.widgets import Cursor,MultiCursor
import matplotlib.pyplot as plt
import numpy as npfig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)
t = np.arange(0.0, 2.0, 0.01)
ax1.plot(t, np.sin(2*np.pi*t))
ax2.plot(t, np.sin(4*np.pi*t))# cursor = Cursor(ax1, useblit=True, color='r', lw=0.5)
cursor = MultiCursor(fig.canvas, (ax1, ax2), useblit=True, horizOn=True, vertOn=True, color='r', lw=0.5)
plt.show()结果：
3、单+多十字星光标
这种在股票软件里面很常见，比如同花顺里面，同一日期里面的多个技术指标，他们只有一条水平线，而每个子图里面都有垂直线，找了下matplotlib里面没有这种实现，所以自己写了一个，代码见文后，先看看效果。
# from matplotlib.widgets import Cursor,MultiCursor
import matplotlib.pyplot as plt
import numpy as npfig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)
t = np.arange(0.0, 2.0, 0.01)
ax1.plot(t, np.sin(2*np.pi*t))
ax2.plot(t, np.sin(4*np.pi*t))# cursor = Cursor(ax1, useblit=True, color='r', lw=0.5)
# cursor = MultiCursor(fig.canvas, (ax1, ax2), useblit=True, horizOn=True, vertOn=True, color='r', lw=0.5)
cursor = SingleMultiCursor(fig.canvas, (ax1, ax2), single=0, color='r', lw=0.5)
plt.show()看看效果
如果把代码中的子图左右排列，并且single的类型改成1，看看效果
# from matplotlib.widgets import Cursor,MultiCursor
import matplotlib.pyplot as plt
import numpy as npfig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True)
t = np.arange(0.0, 2.0, 0.01)
ax1.plot(t, np.sin(2*np.pi*t))
ax2.plot(t, np.sin(4*np.pi*t))# cursor = Cursor(ax1, useblit=True, color='r', lw=0.5)
# cursor = MultiCursor(fig.canvas, (ax1, ax2), useblit=True, horizOn=True, vertOn=True, color='r', lw=0.5)
cursor = SingleMultiCursor(fig.canvas, (ax1, ax2), single=1, color='r', lw=0.5)
plt.show()附SingleMultiCursor的实现代码：
class SingleMultiCursor():"""一个用于多个子图（横排或者竖排）的十字星光标，可以在多个子图上同时出现single=0表示仅仅一个子图显示水平线，所有子图显示垂直线，用于竖排的子图single=1表示仅仅一个子图显示垂直线，所有子图显示水平线，用于横排的子图注意：为了能让光标响应事件处理，必须保持对它的引用（比如有个变量保存）用法::import matplotlib.pyplot as pltimport numpy as npfig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)t = np.arange(0.0, 2.0, 0.01)ax1.plot(t, np.sin(2*np.pi*t))ax2.plot(t, np.sin(4*np.pi*t))cursor = SingleMultiCursor(fig.canvas, (ax1, ax2), single=0, color='w', lw=0.5)plt.show()"""def __init__(self, canvas, axes, single=0, **lineprops):self.canvas = canvasself.axes = axesself.single = singleif single not in [0, 1]:raise ValueError('Unrecognized single value: ' + str(single) + ', must be 0 or 1')xmin, xmax = axes[-1].get_xlim()ymin, ymax = axes[-1].get_ylim()xmid = 0.5 * (xmin + xmax)ymid = 0.5 * (ymin + ymax)self.background = Noneself.needclear = Falselineprops['animated'] = True # for bltself.lines = [[ax.axhline(ymid, visible=False, **lineprops) for ax in axes],[ax.axvline(xmid, visible=False, **lineprops) for ax in axes]]self.canvas.mpl_connect('motion_notify_event', self.onmove)self.canvas.mpl_connect('draw_event', self.clear)def clear(self, event):self.background = (self.canvas.copy_from_bbox(self.canvas.figure.bbox))for line in self.lines[0] + self.lines[1]:line.set_visible(False)def onmove(self, event):if event.inaxes is None: returnif not self.canvas.widgetlock.available(self): returnself.needclear = Truefor i in range(len(self.axes)):if event.inaxes == self.axes[i]:if self.single == 0:for line in self.lines[1]:line.set_xdata((event.xdata, event.xdata))line.set_visible(True)line = self.lines[0][i]line.set_ydata((event.ydata, event.ydata))line.set_visible(True)else:for line in self.lines[0]:line.set_ydata((event.ydata, event.ydata))line.set_visible(True)line = self.lines[1][i]line.set_xdata((event.xdata, event.xdata))line.set_visible(True)else:self.lines[self.single][i].set_visible(False)if self.background is not None:self.canvas.restore_region(self.background)for lines in self.lines:for line in lines:if line.get_visible():line.axes.draw_artist(line)self.canvas.blit()
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