How to define the aspect ratio of a matplotlib figure?

Question

Python produces an aspect ratio that is suitable for its content eg, respects the structure of the font of each label, axis title, etc. This is the basic code using Jupyter Notebook:

fig, ax = plt.subplots()
ax.boxplot(dataLipid)
ax.set_title("Lipid contact analysis")
plt.xticks([1,2,3,4,5],["x4 Monomers","x2 Monomers\nDimer","x2 Dimers","Monomer\nTrimer", "x4mer"])
plt.show()

However, I want to save the image as a tiff, with a dpi of 600, and a width of 8.3cm (maximum height is an A4 page, but the nature of my question will make that irrelevant).

I'm using the code:

fig.savefig("bar.tiff", dpi=600, format="tiff", pil_kwards = {"compression":"tiff_lzm"})

This produces the following:

All good so far. Next, the Royal Soc. of Chemistry expect a single column image to be 8.3 cm in width (height, no more than the page).

My question: Is there any way for Python to calculate the height of the figure given only the wdith, whilst maintaining the correct aspect ratio for the fonts, titles and ticks etc.? If I specify width=height, the image looks terrible:

fig.set_size_inches(3.26,3.26)
fig.savefig("bar.tiff", dpi=600, format="tiff", pil_kwards = {"compression":"tiff_lzm"})

Or is this a case where I define the size of the figure first, then adjust the font sizes as a separate step? I'm looking more for a one-fix solution as I have multiple figures of different size requirements (all being dpi=600 though) to produce. Thanks.

Answer 1

Here you go:

dataLipid = np.random.uniform(0,1,(100,5)) * 90000

fig, ax = plt.subplots()
ax.boxplot(dataLipid)

ax.set_title("Lipid contact analysis")
plt.xticks([1,2,3,4,5],["x4 Monomers","x2 Monomers\nDimer","x2 Dimers","Monomer\nTrimer", "x4mer"])
fig.set_size_inches(3.26,3.26)

# rotate ticks
plt.xticks(rotation=45)
# set bottom margin
plt.subplots_adjust(left=0.2, bottom=0.3)

fig.savefig("bar.tiff", dpi=600, format="tiff", pil_kwards = {"compression":"tiff_lzm"})

There is no general solution as far as I know. So setting the correct margin depends on your content and your data. Rotating the ticks is always a good option to make them readable in case of close spacing.

Answer 2

You can use the Axes.set_aspect method.

# square plot
ax.set_aspect(1)

Also have a look at the tight_layout method to ensure everything is redrawn to fit in the figure.