减小矢量化等高线图的大小 [英] Reducing size of vectorized contourplot

问题描述

我想在 pdf 文档(例如 TeX 文档)中包含一个填充的等高线图.目前我正在使用 pyplots contourf，并使用 pyplots savefig.这样做的问题是，与高分辨率 png 相比，图的大小变得相当大.

I would like to include a filled contour plot to a pdf document (for example a TeX document). Currently I am using pyplots contourf, and saving to pdf with pyplots savefig. The problem with this is that the size of the plots becomes rather big as compared to a high resolution png.

缩小尺寸的一种方法当然是减少绘图中的级别数，但级别太少会导致绘图效果不佳.我正在寻找一种简单的方法，例如将绘图的颜色另存为 png，将轴、刻度等保存为矢量化.

One way to reduce the size is of course to reduce the number of levels in the plot, but too few levels gives a poor plot. I'm searching for a simple way to for example let the colors of the plot be saved as a png, with the axes, ticks etc. to be saved vectorized.

推荐答案

您可以使用 Axes 选项 set_rasterization_zorder.

You can do this using the Axes option set_rasterization_zorder.

zorder 小于您设置的任何内容都将保存为光栅化图形，即使保存为像 pdf 这样的矢量格式.

Anything with a zorder less than what you set that to be will be saved as rasterized graphics, even when saving to a vector format like pdf.

例如:

import matplotlib.pyplot as plt
import numpy as np

data = np.random.rand(500,500)

# fig1 will save the contourf as a vector
fig1,ax1 = plt.subplots(1)
ax1.contourf(data)
fig1.savefig('vector.pdf')

# fig2 will save the contourf as a raster
fig2,ax2 = plt.subplots(1)
ax2.contourf(data,zorder=-20)
ax2.set_rasterization_zorder(-10)
fig2.savefig('raster.pdf')

# Show the difference in file size. "os.stat().st_size" gives the file size in bytes.
print os.stat('vector.pdf').st_size
# 15998481
print os.stat('raster.pdf').st_size
# 1186334

您可以查看这个 matplotlib 示例了解更多背景信息.

You can see this matplotlib example for more background info.

正如@tcaswell 所指出的，要在不影响其 zorder 的情况下只对一位艺术家进行光栅化，您可以使用 .set_rasterized.但是，这似乎不是 contourf 的选项，因此您需要遍历 contourf 和 创建的 PathCollections>set_rasterized 在他们每个人上.像这样:

As pointed out by @tcaswell, to rasterise just one artist without having to affect its zorder, you can use .set_rasterized. However, this doesn't appear to be an option with contourf, so you would need to loop over the PathCollections created by contourf and set_rasterized on each of them. Something like this:

contours = ax.contourf(data)
for pathcoll in contours.collections:
    pathcoll.set_rasterized(True)

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