Why Does Plt Subplots Figsize Ignore Dpi With Tight_layout?

2025-09-04 09:44:41 365
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3 Answers

Jolene
Jolene
2025-09-05 09:03:26
Okay, here’s the practical angle I lean on after fixing a dozen plotting headaches: tight_layout rearranges axes inside the existing figure rectangle, it doesn’t rewrite the physical size in inches or the DPI value. Where the confusion starts is when cropping or saving happens.

For example, savefig has its own dpi argument and bbox handling. If you do fig.savefig('out.png', bbox_inches='tight') the saved image may be cropped to the tight bounding box of artists, and then the final pixel size is influenced by the dpi you passed to savefig (or figure.dpi if you didn’t pass one). In interactive contexts (like Jupyter), the notebook renderer might also scale the image for retina displays, effectively multiplying the pixels you expect. So the perceived size can differ from figsize*dpi even though matplotlib's internal size remains consistent.

So my recommended fixes: explicitly give dpi in savefig if you need exact pixel output; call fig.tight_layout() then fig.canvas.draw() before saving; if cropping is the issue, use bbox_inches=None or tweak pad parameters, or force the figure size after tight_layout with fig.set_size_inches(..., forward=True). That last bit forces the backend to respect the new size for rendering. Once you separate layout, cropping, and saving DPI in your head, the 'ignored' DPI stops being mysterious and becomes just a pipeline of steps that you can control.
Brooke
Brooke
2025-09-08 12:52:21
Funny little quirk, right? I used to be bamboozled by this until I dug in: figsize is measured in inches, dpi is dots-per-inch, and tight_layout is purely a layout algorithm — they all live in the same universe but play different roles.

Figsize = (width, height) in inches. DPI = pixels per inch. So the expected pixel dimensions are figsize * dpi. tight_layout, though, doesn't change those inches or DPI. What tight_layout actually does is adjust subplot parameters (left/right/top/bottom, spacing) so axes, labels and titles fit inside the figure canvas. It can shrink the effective axes area or add space around them, but it doesn't rewrite fig.get_size_inches() or fig.dpi. Where people see 'ignored DPI' is usually later: when you call savefig with bbox_inches='tight' or display in a notebook, Matplotlib crops or rescales the image bounding box, and savefig has its own dpi parameter that can override or interact with figure.dpi.

Practical checklist that helped me: check fig.get_size_inches() and fig.dpi before and after tight_layout; call fig.canvas.draw() to ensure layouts are computed; if saving, use savefig(dpi=...) explicitly and be careful with bbox_inches='tight' because it crops and may change pixel dimensions; if you’re in a high-DPI (retina) display, the display backend can scale the figure differently. If you want absolute control, set figsize and dpi, call fig.set_dpi(...), avoid bbox_inches='tight' or compute the bounding box yourself, or use plt.subplots_adjust to lock margins. Once I started thinking in inches + dpi + cropping as three separate steps, things clicked.
Declan
Declan
2025-09-10 08:37:12
Short and practical: figsize sets inches, dpi sets pixels-per-inch, and tight_layout only rearranges axes inside the figure — it doesn't change those core properties. Most of the time people see DPI 'ignored' because of one of these follow-up steps: (1) savefig with bbox_inches='tight' crops and can rescale when you give savefig a dpi, (2) interactive/retina backends rescale display DPI for higher-resolution screens, or (3) you called tight_layout before the canvas was rendered so layout calculations weren't finalized.

Quick debug steps I use: call fig.get_size_inches() and fig.dpi to confirm values, run fig.canvas.draw() then fig.tight_layout(), and when saving be explicit: fig.savefig('file.png', dpi=300, bbox_inches=None) if you want predictable pixels. If you need no cropping, avoid bbox_inches='tight' or control the pad. Once you treat figsize, layout, and saving DPI as separate knobs, the behavior becomes consistent and controllable.
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