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I like to produce high quality plots and therefore avoid rasterized graphics as much as possible.

I am trying to import an svg file on to a matplotlib figure:

import matplotlib.pyplot as plt
earth   = plt.imread('./gfx/earth.svg')
fig, ax = plt.subplots()
im      = ax.imshow(earth)
plt.show()

This works with png perfectly. Can somebody tell me how to do it with svg or at least point my to proper documentation.

I know that a similar question has been asked (but not answered): here. Has anything changed since?

P.S. I know that I could just export a high resolution png and achieve a similar effect. This is not the solution I am looking for.

Here is the image I would like to import:

Earth_from_above .

William Miller
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Sasha
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  • A quick side note: if you're interested in plotting data on a map there is the [cartopy package](https://scitools.org.uk/cartopy/docs/latest/index.html) that is specialised in just that. – jadsq Mar 08 '19 at 11:02

3 Answers3

31

Maybe what you are looking for is svgutils 

import svgutils.compose as sc
from IPython.display import SVG # /!\ note the 'SVG' function also in svgutils.compose
import numpy as np

# drawing a random figure on top of your SVG
fig, ax = plt.subplots(1, figsize=(4,4))
ax.plot(np.sin(np.linspace(0,2.*np.pi)), np.cos(np.linspace(0,2.*np.pi)), 'k--', lw=2.)
ax.plot(np.random.randn(20)*.3, np.random.randn(20)*.3, 'ro', label='random sampling')
ax.legend()
ax2 = plt.axes([.2, .2, .2, .2])
ax2.bar([0,1], [70,30])
plt.xticks([0.5,1.5], ['water  ', ' ground'])
plt.yticks([0,50])
plt.title('ratio (%)')
fig.savefig('cover.svg', transparent=True)
# here starts the assembling using svgutils 
sc.Figure("8cm", "8cm", 
    sc.Panel(sc.SVG("./Worldmap_northern.svg").scale(0.405).move(36,29)),
    sc.Panel(sc.SVG("cover.svg"))
    ).save("compose.svg")
SVG('compose.svg')

Output:

enter image description here

Joooeey
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yann zerlaut
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2

to anyone ending up here in 2021...

I'd suggest having a look at the cairosvg package
(conda install -c conda-forge cairosvg)

https://cairosvg.org/

import cairosvg
import matplotlib.pyplot as plt
from PIL import Image
from io import BytesIO

img = cairosvg.svg2png("... the content of the svg file ...")
img = Image.open(BytesIO(img_png))
plt.imshow(img)
raphael
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  • Thank you, this worked very well! – Andreas Dec 12 '21 at 15:57
  • I couldn't import cairosvg. Error message: OSError: no library called "cairo-2" was found no library called "cairo" was found no library called "libcairo-2" was found cannot load library 'libcairo.so.2': error 0x7e cannot load library 'libcairo.2.dylib': error 0x7e cannot load library 'libcairo-2.dll': error 0x7e – Martin Gardfjell May 12 '22 at 07:44
-11

SVG (Scalable Vector Graphics) is a vectorial format, which means the image is not composed of pixels, but instead of relative paths that can be scaled arbitrarily.

NumPy/Matplotlib, as numerics software, only really works with pixel graphics and cannot handle svg. I would suggest first converting the svg file to e.g. a png file by opening and saving it in software such as Inkscape (which is free). Then, open the exported png in Python.

Alternatively, use the wikimedia provided versions of the file in png format on the picture's information page (click on the download button to the right of the picture).

If you really believe you need the vectorial form, well, there is no way to do that. You can always superpose the matplotlib figure on to the figure manually (using the matplotlib Artist to draw on the plot canvas), or through some pycairo magic, and save that. But Matplotlib cannot work directly with svg content.

rubenvb
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    Thank you for the reply. However, from my question, you could have seen that I have an understanding of the characteristics of the two formats. I also explicitly stated that everything works fine with a png. Furthermore, matplotlib works with paths. Every time you save an image as eps or svg, you are saving vector graphics. Most plots, before you save them, are vector graphics which can be scaled arbitrarily. – Sasha Jul 16 '15 at 11:35
  • Matplotlib might work with paths internally just like `svg`, but two things working with paths does not imply those two things working together. Note I also suggested generating the matplotlib figure separately and merging them in a second manual or scripted step. Unless you dive into the Matplotlib artists and are prepared to parse the `svg` file manually, I strongly doubt you'll be able to let Matplotlib use the file as a background. – rubenvb Jul 16 '15 at 11:43
  • I did not think it is that complicated, will try to see how the parsing thing works. Could you edit your answer so that I can up-vote it? – Sasha Jul 16 '15 at 12:08
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    @Sasha there, done. Please refrain from downvoting too soon to prevent this kind of situation. – rubenvb Jul 16 '15 at 12:09
  • I am confused, since I can export from Matplotlib as svg, but I can't read back – Alexandre Strube Feb 04 '20 at 17:11