使用matplotlib進行3D動畫，連接點以創建移動的棒圖

Question

我目前在使用某些時間序列數據進行動畫處理時遇到了一些問題，我無法弄清楚。基本上我有12個標籤，我正在通過時間進行動畫製作。每個標籤都有一個及時的軌跡，以便每個標籤在進行時都可以看到移動路徑（查看附加的圖像）。現在我希望動畫還包括成對標籤之間的線條（即點對 - 例如，如何在黃色和綠色標籤之間添加動畫線），但我不完全確定如何做到這一點。這是從jakevdp.github.io改編的代碼。

這是迄今爲止的代碼。

""" 
Full animation of a walking event (note: a lot of missing data) 
""" 
import numpy as np 
import pandas as pd 
import matplotlib 
matplotlib.use('TkAgg') # Need to use in order to run on mac 
from matplotlib import pyplot as plt 
from mpl_toolkits.mplot3d import Axes3D 
from matplotlib.colors import cnames 
from matplotlib import animation 

#============================================================================================= 

t_start = 1917 # start frame 
t_end = 2130 # end frame 

data = pd.read_csv('~/Smart-first_phase_NaN-zeros.csv') # only coordinate data 
df = data.loc[t_start:t_end,'Shoulder_left_x':'Ankle_right_z'] 

# Find max and min values for animation ranges 
df_minmax = pd.DataFrame(index=list('xyz'),columns=range(2)) 
for i in list('xyz'): 
    c_max = df.filter(regex='_{}'.format(i)).max().max() 
    c_min = df.filter(regex='_{}'.format(i)).min().min() 
    df_minmax.ix[i] = np.array([c_min,c_max]) 

df_minmax = 1.3*df_minmax # increase by 30% to make animation look better 

df.columns = np.repeat(range(12),3) # store cols like this for simplicity 
N_tag = df.shape[1]/3 # nr of tags used (all) 

N_trajectories = N_tag 

t = np.linspace(0,data.Time[t_end],df.shape[0]) # pseudo time-vector for first walking activity 
x_t = np.zeros(shape=(N_tag,df.shape[0],3)) # empty animation array (3D) 

for tag in range(12): 
    # store data in numpy 3D array: (tag,time-stamp,xyz-coordinates) 
    x_t[tag,:,:] = df[tag] 


#===STICK-LINES======================================================================================== 
#xx = [x_t[1,:,0],x_t[2,:,0]] 
#yy = [x_t[1,:,1],x_t[2,:,1]] 
#zz = [x_t[1,:,2],x_t[2,:,2]] 
#====================================================================================================== 

# Set up figure & 3D axis for animation 
fig = plt.figure() 
ax = fig.add_axes([0, 0, 1, 1], projection='3d') 
ax.axis('on') 

# choose a different color for each trajectory 
colors = plt.cm.jet(np.linspace(0, 1, N_trajectories)) 
# set up trajectory lines 
lines = sum([ax.plot([], [], [], '-', c=c) for c in colors], []) 
# set up points 
pts = sum([ax.plot([], [], [], 'o', c=c) for c in colors], []) 
# set up lines which create the stick figures 
stick_lines = sum([ax.plot([], [], [], '-', c=c) for c in colors], []) 

# prepare the axes limits 
ax.set_xlim(df_minmax.ix['x'].values) 
ax.set_ylim(df_minmax.ix['z'].values) # note usage of z coordinate 
ax.set_zlim(df_minmax.ix['y'].values) # note usage of y coordinate 

# set point-of-view: specified by (altitude degrees, azimuth degrees) 
ax.view_init(30, 0) 

# initialization function: plot the background of each frame 
def init(): 
    for line, pt, stick_line in zip(lines, pts, stick_lines): 
     # trajectory lines 
     line.set_data([], []) 
     line.set_3d_properties([]) 
     # points 
     pt.set_data([], []) 
     pt.set_3d_properties([]) 
     # stick lines 
     stick_line.set_data([], []) 
     stick_line.set_3d_properties([]) 
    return lines + pts + stick_lines 

# animation function. This will be called sequentially with the frame number 
def animate(i): 
    # we'll step two time-steps per frame. This leads to nice results. 
    i = (5 * i) % x_t.shape[1] 

    for line, pt, stick_line, xi in zip(lines, pts, stick_lines, x_t): 
     x, z, y = xi[:i].T # note ordering of points to line up with true exogenous registration (x,z,y) 
     # trajectory lines 
     line.set_data(x,y) 
     line.set_3d_properties(z) 
     # points 
     pt.set_data(x[-1:], y[-1:]) 
     pt.set_3d_properties(z[-1:]) 
     # stick lines 
     #stick_line.set_data(xx,zz) 
     #stick_line.set_3d_properties(yy) 
    ax.view_init(30, 0.3 * i) 
    fig.canvas.draw() 
    return lines + pts + stick_lines 

# instantiate the animator. 
anim = animation.FuncAnimation(fig, animate, init_func=init, frames=500, interval=30, blit=True) 

# Save as mp4. This requires mplayer or ffmpeg to be installed 
#anim.save('lorentz_attractor.mp4', fps=15, extra_args=['-vcodec', 'libx264']) 

plt.show() 

因此，得出結論：我想線與點對（橙色，黃色）和（黃，綠）移動。如果有人能告訴我該怎麼做，我應該能夠將方法外推到動畫的其餘部分。

一如既往，任何幫助非常感謝。

的原始數據可以在這裏找到，如果有人想複製：https://www.dropbox.com/sh/80f8ue4ffa4067t/Pntl5-gUW4

編輯：實施的解決方案

下面是最終的結果，利用所提出的解決方案。

Answer 1

我修改你的代碼中加入棒線，但爲了簡化代碼，我刪除了軌跡線：

import numpy as np 
import pandas as pd 
import matplotlib 
matplotlib.use('TkAgg') # Need to use in order to run on mac 
from matplotlib import pyplot as plt 
from mpl_toolkits.mplot3d import Axes3D 
from matplotlib.colors import cnames 
from matplotlib import animation 

#============================================================================================= 

t_start = 1917 # start frame 
t_end = 2130 # end frame 

data = pd.read_csv('Smart-first_phase_NaN-zeros.csv') # only coordinate data 
df = data.loc[t_start:t_end,'Shoulder_left_x':'Ankle_right_z'] 

# Find max and min values for animation ranges 
df_minmax = pd.DataFrame(index=list('xyz'),columns=range(2)) 
for i in list('xyz'): 
    c_max = df.filter(regex='_{}'.format(i)).max().max() 
    c_min = df.filter(regex='_{}'.format(i)).min().min() 
    df_minmax.ix[i] = np.array([c_min,c_max]) 

df_minmax = 1.3*df_minmax # increase by 30% to make animation look better 

df.columns = np.repeat(range(12),3) # store cols like this for simplicity 
N_tag = df.shape[1]/3 # nr of tags used (all) 

N_trajectories = N_tag 

t = np.linspace(0,data.Time[t_end],df.shape[0]) # pseudo time-vector for first walking activity 
x_t = np.zeros(shape=(N_tag,df.shape[0],3)) # empty animation array (3D) 

for tag in range(12): 
    # store data in numpy 3D array: (tag,time-stamp,xyz-coordinates) 
    x_t[tag,:,:] = df[tag] 

x_t = x_t[:, :, [0, 2, 1]] 

# Set up figure & 3D axis for animation 
fig = plt.figure() 
ax = fig.add_axes([0, 0, 1, 1], projection='3d') 
ax.axis('on') 

# choose a different color for each trajectory 
colors = plt.cm.jet(np.linspace(0, 1, N_trajectories)) 
# set up trajectory lines 
lines = sum([ax.plot([], [], [], '-', c=c) for c in colors], []) 
# set up points 
pts = sum([ax.plot([], [], [], 'o', c=c) for c in colors], []) 
# set up lines which create the stick figures 

stick_defines = [ 
    (0, 1), 
    (1, 2), 
    (3, 4), 
    (4, 5), 
    (6, 7), 
    (7, 8), 
    (9, 10), 
    (10, 11) 
] 

stick_lines = [ax.plot([], [], [], 'k-')[0] for _ in stick_defines] 

# prepare the axes limits 
ax.set_xlim(df_minmax.ix['x'].values) 
ax.set_ylim(df_minmax.ix['z'].values) # note usage of z coordinate 
ax.set_zlim(df_minmax.ix['y'].values) # note usage of y coordinate 

# set point-of-view: specified by (altitude degrees, azimuth degrees) 
ax.view_init(30, 0) 

# initialization function: plot the background of each frame 
def init(): 
    for line, pt in zip(lines, pts): 
     # trajectory lines 
     line.set_data([], []) 
     line.set_3d_properties([]) 
     # points 
     pt.set_data([], []) 
     pt.set_3d_properties([]) 
    return lines + pts + stick_lines 

# animation function. This will be called sequentially with the frame number 
def animate(i): 
    # we'll step two time-steps per frame. This leads to nice results. 
    i = (5 * i) % x_t.shape[1] 

    for line, pt, xi in zip(lines, pts, x_t): 
     x, y, z = xi[:i].T # note ordering of points to line up with true exogenous registration (x,z,y) 
     pt.set_data(x[-1:], y[-1:]) 
     pt.set_3d_properties(z[-1:]) 

    for stick_line, (sp, ep) in zip(stick_lines, stick_defines): 
     stick_line._verts3d = x_t[[sp,ep], i, :].T.tolist() 

    ax.view_init(30, 0.3 * i) 
    fig.canvas.draw() 
    return lines + pts + stick_lines 

# instantiate the animator. 
anim = animation.FuncAnimation(fig, animate, init_func=init, frames=500, interval=30, blit=True) 

plt.show() 

這裏是動畫的一幀：