python - Python Matplotlib "ginput"可以独立于 "zoom to rectangle"吗？

Question

我正在使用 ginput 沿着时间信号对几个点进行图形选择。有时，当信号太密集时，在选择点之前放大一个区域可能很有用。我的问题是 zoom to rectangle 选项似乎在 ginput 中被考虑在内。

例如，使用此示例代码:

from __future__ import print_function
from pylab import arange, plot, sin, ginput, show
import numpy as np

t = np.linspace(0,25,500)
plot(t, sin(t))
x = ginput(3)
print("clicked",x)
show()

如果我放大信号的一部分，为缩放区域选择所做的点击将在 ginput 中计算...有没有办法避免这种情况并使缩放区域选择独立来自 ginput ?

Answer 1

我遇到了同样的问题，我的解决方法是编写一个使用鼠标滚轮进行缩放的函数，这样我就可以在不单击的情况下进行缩放。我从这个页面得到了这个功能:

Matplotlib plot zooming with scroll wheel

并修改了函数，使其不会缩放到超过原始 x 和 y 限制:

def zoom_factory(ax, max_xlim, max_ylim, base_scale = 2.):
    def zoom_fun(event):
        # get the current x and y limits
        cur_xlim = ax.get_xlim()
        cur_ylim = ax.get_ylim()
        xdata = event.xdata # get event x location
        ydata = event.ydata # get event y location
        if event.button == 'up':
            # deal with zoom in
            scale_factor = 1/base_scale
            x_scale = scale_factor / 2
        elif event.button == 'down':
            # deal with zoom out
            scale_factor = base_scale
            x_scale = scale_factor * 2
        else:
            # deal with something that should never happen
            scale_factor = 1
            print(event.button)
        # set new limits
        new_width = (cur_xlim[1] - cur_xlim[0]) * x_scale
        new_height = (cur_ylim[1] - cur_ylim[0]) * scale_factor

        relx = (cur_xlim[1] - xdata) / (cur_xlim[1] - cur_xlim[0])
        rely = (cur_ylim[1] - ydata) / (cur_ylim[1] - cur_ylim[0])

        if xdata - new_width * (1 - relx) > max_xlim[0]:
            x_min = xdata - new_width * (1 - relx)
        else:
            x_min = max_xlim[0]
        if xdata + new_width * (relx) < max_xlim[1]:
            x_max = xdata + new_width * (relx)
        else:
            x_max = max_xlim[1]
        if ydata - new_height * (1 - rely) > max_ylim[0]:
            y_min = ydata - new_height * (1 - rely)
        else:
            y_min = max_ylim[0]
        if ydata + new_height * (rely) < max_ylim[1]:
            y_max = ydata + new_height * (rely)
        else:
            y_max = max_ylim[1]
        ax.set_xlim([x_min, x_max])
        ax.set_ylim([y_min, y_max])
        ax.figure.canvas.draw()

    fig = ax.get_figure() # get the figure of interest
    # attach the call back
    fig.canvas.mpl_connect('scroll_event',zoom_fun)

    #return the function
    return zoom_fun

所以对于给定的数字:

t = np.linspace(0,250,5000)
y = sin(t)    

fig = figure()
ax = fig.add_subplot(111)
plt.plot(t,y)

max_xlim = ax.get_xlim() # get current x_limits to set max zoom out
max_ylim = ax.get_ylim() # get current y_limits to set max zoom out
f = zoom_factory(ax, max_xlim, max_ylim, base_scale=1.1)
show()

x = ginput(3)