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x-tick labels using LaTeX are vertically misaligned when you call savefig() with dpi=300. The misalignment occurs when two labels have a superscript and subscript, while the third misaligned label only has a superscript.

Code for reproduction

import sys

import matplotlib
import matplotlib.pyplot as plt

print(matplotlib.__version__)
print(matplotlib.get_backend())
print(sys.version)

idxs = list(range(3))
labels = ["$w^{(2)}_1$", "$w^{(2)}_2$", "$b^{(2)}$"]

fig, ax = plt.subplots(figsize=(3, 3))
ax.set_xlim(-0.5, 2.5)
ax.set_ylim(-3, 3)
ax.set_xticks(idxs, labels)
ax.set_yticks([])
ax.tick_params(which="both", length=0)
fig.tight_layout()
fig.savefig("test.png", dpi=300)

Actual outcome

Here is the image produced by the code. You can see that the $b^{(2)}$ label is vertically misaligned; it's higher than the other two labels.

Expected outcome

Here is the same image if you run the code without the dpi arg in savefig(). You can see that the $b^{(2)}$ label is vertically aligned with the other two labels.

Additional information

[EDIT: slight fix to code] Here's the reproduction code modified to use a workaround:

import sys

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.artist import Artist
from matplotlib.figure import Figure

print(matplotlib.__version__)
print(matplotlib.get_backend())
print(sys.version)


def get_center(fig: Figure, artist: Artist) -> tuple[float, float]:
    renderer = fig.canvas.get_renderer()  # type: ignore  # get_renderer() not in base class
    bbox = artist.get_window_extent(renderer)
    fig_box = bbox.transformed(fig.transFigure.inverted())
    x = (fig_box.xmin + fig_box.xmax) / 2
    y = (fig_box.ymin + fig_box.ymax) / 2
    return x, y


idxs = list(range(3))
labels = ["$w^{(2)}_1$", "$w^{(2)}_2$", "$b^{(2)}_1$"]  # add subscript to b2

fig, ax = plt.subplots(figsize=(3, 3))
ax.set_xlim(-0.5, 2.5)
ax.set_ylim(-3, 3)
ax.set_xticks(idxs, labels)
ax.set_yticks([])
ax.tick_params(which="both", length=0)
fig.tight_layout()  # call the layout function before fixing the label

orig_b2_label = ax.get_xticklabels()[-1]
orig_b2_x, orig_b2_y = get_center(fig, orig_b2_label)  # measure while it's still visible
orig_b2_label.set_visible(False)
b2_label = fig.text(
    orig_b2_x, orig_b2_y, "$b^{(2)}$", ha="center", va="center", transform=fig.transFigure
)
b2_label.set_in_layout(False)

fig.savefig("test-workaround.png", dpi=300)

Here is the image with the workaround:

(Feel free to let me know if there is a better workaround.)

Operating system

Ubuntu 24

Matplotlib Version

3.10.9

Matplotlib Backend

module://backend_interagg

Python version

Python 3.12.3

Jupyter version

No response

Installation

Activity

github-actions
added
first-contribution
on Jun 2, 2026

QuLogic commented on Jun 2, 2026

QuLogic

on Jun 2, 2026

Member

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The problem here is that the x-ticks are aligned to the top, but they are all independent of each other. Ideally, they would be aligned to the baseline, with some adjustment for the height of the largest one. As they do not know about each other, there is no way for this to happen right now.

A simple, but possibly fragile, workaround would be to increase the padding on the xticks to fit the largest text you have, then manually change the text alignment to baseline. However, as ticks can be replaced at any time, it is important that you do this at the last possible moment.

import sys

import matplotlib
import matplotlib.pyplot as plt

idxs = list(range(3))
labels = ["$w^{(2)}_1$", "$w^{(2)}_2$", "$w^{(2)}$"]

plt.rcParams['xtick.major.pad'] = 14  # Make this large enough to fit all your text vertically.

fig, ax = plt.subplots(figsize=(3, 3))
ax.set_xlim(-0.5, 2.5)
ax.set_ylim(-3, 3)
ax.set_xticks(idxs, labels)
ax.set_yticks([])
ax.tick_params(which="both", length=0)
fig.tight_layout()
for tick in ax.xaxis.get_major_ticks():
    tick.label1.set_verticalalignment('baseline')  # Now set alignment at the baseline at that padded position.
fig.savefig("test.png", dpi=300)

scottshambaugh
added
topic: text

topic: text/mathtext
on Jun 2, 2026
QuLogic
added
topic: ticks axis labels
on Jun 3, 2026

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