Python matplotlib交互绘图中的拖动点

我使用

matplotlib

和下面的答案创建了一个交互式绘图。绘图使用滑块，以允许用户控制点的垂直（y轴）位置，并在点发生更改时将样条曲线拟合到这些点。完成此操作的代码如下所示：

import matplotlib.animation as animation
from matplotlib.widgets import Slider
import matplotlib as mpl
from matplotlib import pyplot as plt
import scipy.interpolate as inter
import numpy as np



func = lambda x: 0.1*x**2

#get a list of points to fit a spline to as well
N = 10
xmin = 0
xmax = 10
x = np.linspace(xmin,xmax,N)

#spline fit
yvals = func(x)
spline = inter.InterpolatedUnivariateSpline (x, yvals)

#figure.subplot.right
mpl.rcParams['figure.subplot.right'] = 0.8

#set up a plot
fig,axes = plt.subplots(1,1,figsize=(9.0,8.0),sharex=True)
ax1 = axes


interval = 100 # ms, time between animation frames
loop_len = 5.0 # seconds per loop
scale = interval / 1000 / loop_len

def update(val):
  # update curve
  for i in np.arange(N):
    yvals[i] = sliders[i].val
  l.set_ydata(yvals)
  spline = inter.InterpolatedUnivariateSpline (x, yvals)
  m.set_ydata(spline(X))
  # redraw canvas while idle
  fig.canvas.draw_idle()

X = np.arange(0,xmax+1,0.1)
ax1.plot (X, func(X), 'k--', label='original')
l, = ax1.plot (x,yvals,color='k',linestyle='none',marker='o',markersize=8)
m, = ax1.plot (X, spline(X), 'r-', label='spline')



ax1.set_yscale('linear')
ax1.set_xlim(0, xmax)
ax1.set_ylim(0,xmax)
ax1.set_xlabel('x')
ax1.set_ylabel('y')
ax1.grid(True)
ax1.yaxis.grid(True,which='minor',linestyle='--')
ax1.legend(loc=2,prop={'size':22})

sliders = []

for i in np.arange(N):

    axamp = plt.axes([0.84, 0.8-(i*0.05), 0.12, 0.02])
    # Slider
    s = Slider(axamp, 'p{0}'.format(i), 0, 10, valinit=yvals[i])
    sliders.append(s)


for i in np.arange(N):
    #samp.on_changed(update_slider)
    sliders[i].on_changed(update)


plt.show()

我认为用户界面会更好，如果你可以直接点击这些点并垂直向上或向下拖动它们。

matplotlib

交互式代码的哪些功能最有助于实现这一点？我已经研究了各种各样的方法，但没有发现任何对我来说是直接有用的方法。最接近的似乎是，但我不确定如何适应它，以获得我需要的

编辑：

这个答案可能适合我所需要的。这与我所需要的完全不同之处在于，示例沿现有直线拾取一个点（基于鼠标单击和拖动），并将其高亮显示。我需要单击并选择小范围内的现有点（如果没有，则拖动不起任何作用）。然后，代码应在拖动该点时更改该点，并在拖动操作期间重新装配样条曲线。通过上面的示例，我可能能够做到这一点，因为它让我更好地理解了如何使用

mpl\u connect

进行不同的操作（并给出了一些示例操作）

最终的代码应该只允许垂直拖动点，但我认为约束很容易构建（即只使用鼠标单击/释放的y坐标）

编辑2：

---

示例似乎非常接近我需要的，我可以从中发展。

好的，非常依赖示例，我构建了一个示例，可以使用滑块更改点值，也可以单击并拖动点

代码如下，并注释了诊断打印：

import matplotlib.animation as animation
from matplotlib.widgets import Slider, Button
import matplotlib as mpl
from matplotlib import pyplot as plt
import scipy.interpolate as inter
import numpy as np



func = lambda x: 0.1*x**2

#get a list of points to fit a spline to as well
N = 10
xmin = 0 
xmax = 10 
x = np.linspace(xmin,xmax,N)

#spline fit
yvals = func(x)
spline = inter.InterpolatedUnivariateSpline (x, yvals)

#figure.subplot.right
mpl.rcParams['figure.subplot.right'] = 0.8

#set up a plot
fig,axes = plt.subplots(1,1,figsize=(9.0,8.0),sharex=True)
ax1 = axes


pind = None #active point
epsilon = 5 #max pixel distance

def update(val):
    global yvals
    global spline
    # update curve
    for i in np.arange(N):
      yvals[i] = sliders[i].val 
    l.set_ydata(yvals)
    spline = inter.InterpolatedUnivariateSpline (x, yvals)
    m.set_ydata(spline(X))
    # redraw canvas while idle
    fig.canvas.draw_idle()

def reset(event):
    global yvals
    global spline
    #reset the values
    yvals = func(x)
    for i in np.arange(N):
      sliders[i].reset()
    spline = inter.InterpolatedUnivariateSpline (x, yvals)
    l.set_ydata(yvals)
    m.set_ydata(spline(X))
    # redraw canvas while idle
    fig.canvas.draw_idle()

def button_press_callback(event):
    'whenever a mouse button is pressed'
    global pind
    if event.inaxes is None:
        return
    if event.button != 1:
        return
    #print(pind)
    pind = get_ind_under_point(event)    

def button_release_callback(event):
    'whenever a mouse button is released'
    global pind
    if event.button != 1:
        return
    pind = None

def get_ind_under_point(event):
    'get the index of the vertex under point if within epsilon tolerance'

    # display coords
    #print('display x is: {0}; display y is: {1}'.format(event.x,event.y))
    t = ax1.transData.inverted()
    tinv = ax1.transData 
    xy = t.transform([event.x,event.y])
    #print('data x is: {0}; data y is: {1}'.format(xy[0],xy[1]))
    xr = np.reshape(x,(np.shape(x)[0],1))
    yr = np.reshape(yvals,(np.shape(yvals)[0],1))
    xy_vals = np.append(xr,yr,1)
    xyt = tinv.transform(xy_vals)
    xt, yt = xyt[:, 0], xyt[:, 1]
    d = np.hypot(xt - event.x, yt - event.y)
    indseq, = np.nonzero(d == d.min())
    ind = indseq[0]

    #print(d[ind])
    if d[ind] >= epsilon:
        ind = None
    
    #print(ind)
    return ind

def motion_notify_callback(event):
    'on mouse movement'
    global yvals
    if pind is None:
        return
    if event.inaxes is None:
        return
    if event.button != 1:
        return
    
    #update yvals
    #print('motion x: {0}; y: {1}'.format(event.xdata,event.ydata))
    yvals[pind] = event.ydata 

    # update curve via sliders and draw
    sliders[pind].set_val(yvals[pind])
    fig.canvas.draw_idle()

X = np.arange(0,xmax+1,0.1)
ax1.plot (X, func(X), 'k--', label='original')
l, = ax1.plot (x,yvals,color='k',linestyle='none',marker='o',markersize=8)
m, = ax1.plot (X, spline(X), 'r-', label='spline')



ax1.set_yscale('linear')
ax1.set_xlim(0, xmax)
ax1.set_ylim(0,xmax)
ax1.set_xlabel('x')
ax1.set_ylabel('y')
ax1.grid(True)
ax1.yaxis.grid(True,which='minor',linestyle='--')
ax1.legend(loc=2,prop={'size':22})

sliders = []

for i in np.arange(N):

    axamp = plt.axes([0.84, 0.8-(i*0.05), 0.12, 0.02])
    # Slider
    s = Slider(axamp, 'p{0}'.format(i), 0, 10, valinit=yvals[i])
    sliders.append(s)

    
for i in np.arange(N):
    #samp.on_changed(update_slider)
    sliders[i].on_changed(update)

axres = plt.axes([0.84, 0.8-((N)*0.05), 0.12, 0.02])
bres = Button(axres, 'Reset')
bres.on_clicked(reset)

fig.canvas.mpl_connect('button_press_event', button_press_callback)
fig.canvas.mpl_connect('button_release_event', button_release_callback)
fig.canvas.mpl_connect('motion_notify_event', motion_notify_callback)

plt.show()

我没有像示例那样将代码封装到对象中，但这应该是一个易于实现的升级

也许您正在寻找？matplotlib页面上也有。我认为最好知道这些问题在多大程度上有帮助或没有帮助，也就是说，应该更具体地说明问题。