Bug summary

In investigating #25894 I discovered that despite being documented as:

To disable labeling of minor ticks when 'labelOnlyBase' is False, use minor_thresholds=(0, 0). This is the default for the "classic" style.

It actually only removes some minor tick labels (e.g. with base of 10: 11-14 will get labels, but 16-19 (15 is edge case, dependent on floating point rounding, 1.4, or 140 also similar))

Code for reproduction

import matplotlib.pyplot as plt
import matplotlib.ticker as mticker

fig, ax = plt.subplots()
ax.set_yscale("log")

formatter = mticker.LogFormatter(minor_thresholds=(0,0))
ax.yaxis.set_minor_formatter(formatter)

ax.set_ylim(10, 20)
plt.show()

Actual outcome

formatter(10) # '10'
formatter(11) # '11'
formatter(12) # '12'
formatter(13) # '13'
formatter(14) # '14'
formatter(15) # '15'
formatter(16) # ''

Expected outcome

formatter(10) # ''
formatter(11) # ''
formatter(12) # ''
formatter(13) # ''
formatter(14) # ''
formatter(15) # ''
formatter(16) # ''

Additional information

Only really affects plots that are zoomed in to much less than one decade, which arguably doesn't make sense to use a log scale, but still seems incongruous with the documented "this turns off minor ticks".

Has to do with when coeff rounds down to 1 in:

I'm not quite sure what the proper solution is, perhaps making self._sublabels=set() instead?

Operating system

Linux

Matplotlib Version

3.8 (main)

Matplotlib Backend

No response

Python version

3.11

Jupyter version

No response

Installation

git checkout