这张照片显示了我想要实现的目标:
我正在寻找一种解决方案,将光标添加到matplotlib
中的绘制线上。光标应该是可拖动的,但应仅在绘制的线上移动。标签应显示迹线上标记点的实际值。
我不知道将哪个对象用作此光标/标记。
答案 0 :(得分:2)
matplotlib页面上有一个示例,您可以调整该页面以显示感兴趣位置的点。
import matplotlib.pyplot as plt
import matplotlib.widgets as widgets
import numpy as np
class SnaptoCursor(object):
def __init__(self, ax, x, y):
self.ax = ax
self.ly = ax.axvline(color='k', alpha=0.2) # the vert line
self.marker, = ax.plot([0],[0], marker="o", color="crimson", zorder=3)
self.x = x
self.y = y
self.txt = ax.text(0.7, 0.9, '')
def mouse_move(self, event):
if not event.inaxes: return
x, y = event.xdata, event.ydata
indx = np.searchsorted(self.x, [x])[0]
x = self.x[indx]
y = self.y[indx]
self.ly.set_xdata(x)
self.marker.set_data([x],[y])
self.txt.set_text('x=%1.2f, y=%1.2f' % (x, y))
self.txt.set_position((x,y))
self.ax.figure.canvas.draw_idle()
t = np.arange(0.0, 1.0, 0.01)
s = np.sin(2*2*np.pi*t)
fig, ax = plt.subplots()
#cursor = Cursor(ax)
cursor = SnaptoCursor(ax, t, s)
cid = plt.connect('motion_notify_event', cursor.mouse_move)
ax.plot(t, s,)
plt.axis([0, 1, -1, 1])
plt.show()
答案 1 :(得分:0)
看这里:Is there a matplotlib equivalent of MATLAB's datacursormode?
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
plt.show()