Python 使用 matplotlib 实时更新
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live updating with matplotlib
提问by Ryan Saxe
So I have some phone accelerometry data and I would like to basically make a video of what the motion of the phone looked like. So I used matplotlib to create a 3D graph of the data:
所以我有一些手机加速度测量数据，我想基本上制作一个关于手机运动的视频。所以我使用 matplotlib 来创建数据的 3D 图：
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas as pd
import pickle
def pickleLoad(pickleFile):
pkl_file = open(pickleFile, 'rb')
data = pickle.load(pkl_file)
pkl_file.close()
return data
data = pickleLoad('/Users/ryansaxe/Desktop/kaggle_parkinsons/accelerometry/LILY_dataframe')
data = data.reset_index(drop=True)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
xs = data['x.mean']
ys = data['y.mean']
zs = data['z.mean']
ax.scatter(xs, ys, zs)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.show()
Now time is important and is actually also a factor that I only see one point at a time because time is also a factor and it lets me watch the progression of the accelerometry data!
现在时间很重要，实际上也是一个因素，我一次只能看到一个点，因为时间也是一个因素，它让我观察加速度数据的进展！
What can I do with this to make it a live updating graph?
我该怎么做才能使它成为实时更新图？
Only thing I can think of is to have a loop that goes through row by row and makes the graph from the row, but that will open so many files that it would be insane because I have millions of rows.
我唯一能想到的是有一个循环，逐行遍历并从行中生成图形，但这将打开太多文件，这将是疯狂的，因为我有数百万行。
So how can I create a live updating graph?
那么如何创建实时更新图呢？
回答by Hooked
Here is a bare-bones example that updates as fast as it can:
这是一个尽可能快地更新的基本示例：
import pylab as plt
import numpy as np
X = np.linspace(0,2,1000)
Y = X**2 + np.random.random(X.shape)
plt.ion()
graph = plt.plot(X,Y)[0]
while True:
Y = X**2 + np.random.random(X.shape)
graph.set_ydata(Y)
plt.draw()
The trick is notto keep creating new graphs as this will continue to eat up memory, but to change the x,y,z-data on an existing plot. Use .ion()and .draw()setup the canvas for updating like this.
诀窍不是继续创建新图，因为这会继续占用内存，而是更改现有图上的 x、y、z 数据。使用.ion()和.draw()设置画布进行更新。
Addendum: A highly ranked comment below by @Kelsey notes that:
附录：@Kelsey 在下面的评论中指出：
You may need a
plt.pause(0.01)after theplt.draw()line to get the refresh to show
您可能需要在行
plt.pause(0.01)之后plt.draw()才能显示刷新
回答by Ryan Saxe
I was able to create live updating with draw()and a while loop here is the code I used:
我能够创建实时更新，draw()这里的 while 循环是我使用的代码：
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from pylab import *
import time
import pandas as pd
import pickle
def pickleLoad(pickleFile):
pkl_file = open(pickleFile, 'rb')
data = pickle.load(pkl_file)
pkl_file.close()
return data
data = pickleLoad('/Users/ryansaxe/Desktop/kaggle_parkinsons/accelerometry/LILY_dataframe')
data = data.reset_index(drop=True)
df = data.ix[0:,['x.mean','y.mean','z.mean','time']]
ion()
fig = figure()
ax = fig.add_subplot(111, projection='3d')
count = 0
plotting = True
while plotting:
df2 = df.ix[count]
count += 1
xs = df2['x.mean']
ys = df2['y.mean']
zs = df2['z.mean']
t = df2['time']
ax.scatter(xs, ys, zs)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
ax.set_title(t)
draw()
pause(0.01)
if count > 50:
plotting = False
ioff()
show()
