Bug summary

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

To disable labeling of minor ticks when 'labelOnlyBase' is False, useminor_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

importmatplotlib.pyplotaspltimportmatplotlib.tickerasmtickerfig,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 whencoeff rounds down to1 in:

I'm notquite sure what the proper solution is, perhaps makingself._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