加速Matplotlib散点图 [英] Speeding up matplotlib scatter plots
问题描述
我正在尝试制作一个交互式程序，该程序主要使用 matplotlib 来制作相当多的点(10k-100k 左右)的散点图.现在它可以工作，但更改需要很长时间才能呈现.少量的点数是可以的，但是一旦数量增加，事情就会变得匆忙.因此，我正在研究加速分散的方法，但运气不高
I'm trying to make an interactive program which primarily uses matplotlib to make scatter plots of rather a lot of points (10k-100k or so). Right now it works, but changes take too long to render. Small numbers of points are ok, but once the number rises things get frustrating in a hurry. So, I'm working on ways to speed up scatter, but I'm not having much luck
有明显的做事方式(现在实施的方式)(我意识到情节在没有更新的情况下重绘.我不想通过大量随机调用来改变 fps 结果).
There's the obvious way to do thing (the way it's implemented now) (I realize the plot redraws without updating. I didn't want to alter the fps result with large calls to random).
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
import time
X = np.random.randn(10000) #x pos
Y = np.random.randn(10000) #y pos
C = np.random.random(10000) #will be color
S = (1+np.random.randn(10000)**2)*3 #size
#build the colors from a color map
colors = mpl.cm.jet(C)
#there are easier ways to do static alpha, but this allows
#per point alpha later on.
colors[:,3] = 0.1
fig, ax = plt.subplots()
fig.show()
background = fig.canvas.copy_from_bbox(ax.bbox)
#this makes the base collection
coll = ax.scatter(X,Y,facecolor=colors, s=S, edgecolor='None',marker='D')
fig.canvas.draw()
sTime = time.time()
for i in range(10):
print i
#don't change anything, but redraw the plot
ax.cla()
coll = ax.scatter(X,Y,facecolor=colors, s=S, edgecolor='None',marker='D')
fig.canvas.draw()
print '%2.1f FPS'%( (time.time()-sTime)/10 )
提供快速的 0.7 fps
Which gives a speedy 0.7 fps
或者，我可以编辑 scatter 返回的集合.为此，我可以更改颜色和位置，但不知道如何更改每个点的大小.我会认为看起来像这样
Alternatively, I can edit the collection returned by scatter. For that, I can change color and position, but don't know how to change the size of each point. That would I think look something like this
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
import time
X = np.random.randn(10000) #x pos
Y = np.random.randn(10000) #y pos
C = np.random.random(10000) #will be color
S = (1+np.random.randn(10000)**2)*3 #size
#build the colors from a color map
colors = mpl.cm.jet(C)
#there are easier ways to do static alpha, but this allows
#per point alpha later on.
colors[:,3] = 0.1
fig, ax = plt.subplots()
fig.show()
background = fig.canvas.copy_from_bbox(ax.bbox)
#this makes the base collection
coll = ax.scatter(X,Y,facecolor=colors, s=S, edgecolor='None', marker='D')
fig.canvas.draw()
sTime = time.time()
for i in range(10):
print i
#don't change anything, but redraw the plot
coll.set_facecolors(colors)
coll.set_offsets( np.array([X,Y]).T )
#for starters lets not change anything!
fig.canvas.restore_region(background)
ax.draw_artist(coll)
fig.canvas.blit(ax.bbox)
print '%2.1f FPS'%( (time.time()-sTime)/10 )
这会导致速度降低至0.7 fps.我想尝试使用 CircleCollection 或 RegularPolygonCollection，因为这可以让我轻松更改大小，而且我不关心更改标记.但是，我不能画画，所以我不知道他们是否会更快.所以，此时我正在寻找想法.
This results in a slower 0.7 fps. I wanted to try using CircleCollection or RegularPolygonCollection, as this would allow me to change the sizes easily, and I don't care about changing the marker. But, I can't get either to draw so I have no idea if they'd be faster. So, at this point I'm looking for ideas.
推荐答案
我们正在积极研究大型matplotlib散点图的性能.我鼓励您参与对话(http://matplotlib.1069221.n5.nabble.com/mpl-1-2-1-Speedup-code-by-removing-startswith-calls-and-some-for-loops-td41767.html )，甚至更好地测试已提交的请求请求，以使类似情况的生活变得更好(https://github.com/matplotlib/matplotlib/pull/2156).
We are actively working on performance for large matplotlib scatter plots. I'd encourage you to get involved in the conversation (http://matplotlib.1069221.n5.nabble.com/mpl-1-2-1-Speedup-code-by-removing-startswith-calls-and-some-for-loops-td41767.html) and, even better, test out the pull request that has been submitted to make life much better for a similar case (https://github.com/matplotlib/matplotlib/pull/2156).
HTH
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