Skip to content

Fix nonsensical transform in mixed-mode axes aspect computation. #14990

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Aug 7, 2019

Conversation

anntzer
Copy link
Contributor

@anntzer anntzer commented Aug 6, 2019

PR Summary

The first commit fixes the nonsensical transform mentioned in #14899, while changing the test introduced in #14727 to match the behavior of autoscale which we decided in #14899 to keep at least for now (as refactoring it is going to require more work).

The second commit fixes the fact that I missed, in #14727, that I could just write trf.transform(array) rather than map(trf.transform, array), i.e. Transform.transform works on arrays (and is documented to do so).

attn @efiring

PR Checklist

  • Has Pytest style unit tests
  • Code is Flake 8 compliant
  • New features are documented, with examples if plot related
  • Documentation is sphinx and numpydoc compliant
  • Added an entry to doc/users/next_whats_new/ if major new feature (follow instructions in README.rst there)
  • Documented in doc/api/api_changes.rst if API changed in a backward-incompatible way

@anntzer anntzer added the Release critical For bugs that make the library unusable (segfaults, incorrect plots, etc) and major regressions. label Aug 6, 2019
@anntzer anntzer added this to the v3.2.0 milestone Aug 6, 2019
Copy link
Member

@efiring efiring left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

You might want to either remove the comment in the test, or modify the test so that it tests the zoomed-in case where the requested view limits are within larger data limits. This need not block the PR, though.

aspect=1, adjustable="datalim")
ax.margins(0)
ax.apply_aspect()
assert ax.get_xlim() == pytest.approx(np.array([1/10, 10]) * np.sqrt(10))
# Currently the autoscaler chooses to reduce the x-limits by half a decade
# on each end, but this may change later.
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I don't know why it would, unless the test itself is changed. I think the point of confusion is that in this test you are plotting only one point, so apply_aspect is understandably and correctly shrinking an axis instead of expanding one. xlim and ylim pertain to view limits, not data limits. It looks like I made a big mistake, long ago, in calling the adjustable option "datalim", because it is not actually adjusting data limits--they remain determined by the actual plot calls and Artist additions--but rather, view limits. Maybe we should change the option name (with deprecation, in a separate PR) to "viewlim" or "viewlimits"?

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I just removed the comment and let's punt the discussion of what the ideal behavior should be to, well, not this PR :)

@timhoffm timhoffm merged commit d95e3f8 into matplotlib:master Aug 7, 2019
@anntzer anntzer deleted the mixedscaleaspect branch August 7, 2019 19:14
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
Release critical For bugs that make the library unusable (segfaults, incorrect plots, etc) and major regressions.
Projects
None yet
Development

Successfully merging this pull request may close these issues.

3 participants