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ENH: box aspect for axes#14917

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@ImportanceOfBeingErnest
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@ImportanceOfBeingErnestImportanceOfBeingErnest commentedJul 30, 2019•
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PR Summary

Matplotlib axes'aspect refers to thedata, i.e. it defines the ratio of vertical vs. horizontal data units. However, it seems people often (mis)use it in order to set the aspect of the axes box, which is of course possible by knowing the limits of the data and usingadjustable="box". But it inevitably leads to problems, e.g.

In short, sometimes people just want to make a square plot.

So it seems useful to introduce abox_aspect parameter, which sets the aspect (i.e. the ratio between height and width) of theaxes box, independent of the data.

Some usecases:

A. A square axes, independent of data

fig1, ax = plt.subplots()ax.set_xlim(300,400)ax.set_box_aspect(1)

fig1

B. Shared square axes

fig2, (ax, ax2) = plt.subplots(ncols=2, sharey=True)ax.plot([0,10])ax2.plot([10,20])ax.set_box_aspect(1)ax2.set_box_aspect(1)

fig2

C. Square twin axes

fig3, ax = plt.subplots()ax2 = ax.twinx()ax.plot([0,10])ax2.plot([12,10])ax.set_box_aspect(1)

fig3

D. Normal plot next to image (works with constrained_layout)

fig4, (ax, ax2) = plt.subplots(ncols=2, constrained_layout=True)im = np.random.rand(16,27)ax.imshow(im)ax2.plot([23,45])ax2.set_box_aspect(im.shape[0]/im.shape[1])

fig4

E. Square joint/marginal plot

fig5, axs = plt.subplots(2,2, sharex="col", sharey="row",                         gridspec_kw = dict(height_ratios=[1,3],                                            width_ratios=[3,1]))axs[0,1].set_visible(False)axs[0,0].set_box_aspect(1/3)axs[1,0].set_box_aspect(1)axs[1,1].set_box_aspect(3/1)x,y = np.random.randn(2,400) * np.array([[.5],[180]])axs[1,0].scatter(x,y)axs[0,0].hist(x)axs[1,1].hist(y, orientation="horizontal")

fig5

F. Equal data aspect, unequal box aspect

fig6, ax = plt.subplots()ax.add_patch(plt.Circle((5,3), 1))ax.set_aspect("equal", adjustable="datalim")ax.set_box_aspect(0.5)ax.autoscale()

fig6

Issues:

  • As@jklymak alreadypointed out, there might be a problem with layout managers, and indeed at least caseE. from above fails with contrained_layout. (fixed byFIX constrained_layout w/ hidden axes #14919)

  • Obviously a fixed box_aspect only makes sense withadjustable="datalim". Since the order in whichbox_aspect andadjustable are set is arbitrary, this still needs to define in which cases to error out, or silently fall back.

PR Checklist

  • Has Pytest style unit tests
  • Code is finallyFlake 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)

@jklymak
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Seems cool. But I think we need to sit down and think about all the axes sizing and sharing options a bit more holistically.

I think if you change the non-original position then you can keep constrained layout working.

@ImportanceOfBeingErnest
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I think if you change the non-original position then you can keep constrained layout working.

I would have thought that is what I'm doing in line 1527. Will need to check this at some later point.

[..] we need to sit down and think about all the axes sizing and sharing options a bit more holistically.

Sure, that's why I put it out in its current state, to see what is possible and to define some working use cases. In general what's still missing is a sharing of geometries (as opposed to sharing of data); or maybe call it a twinning across subplots.

@jklymak
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Great, then I'm surprised there are issues withconstrained_layout since its no different than a data-aspect constrained axes.constrained_layout only cares about the "original" size.

@jklymak
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Thanks - that was a bug:#14918, and solution:#14919

@jklymakjklymak mentioned this pull requestJul 30, 2019
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@anntzer
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I think it's a great idea :)
I believe this is mutually exclusive with setting the old data aspect? If so, perhaps this should be exposed in the same API, e.g.set_aspect(aspect, "data"/"axes") (the second argument defaulting to "data" which maintains backcompat) and internally stored in the same attribute? The point being that after

ax.set_aspect(a, "data")ax.set_aspect(b, "axes")

it's not too surprising if the second call overwrites the first one, whereas in

ax.set_aspect(a)ax.set_box_aspect(b)

... do we want the second to overwrite the first one? do we want to add a check and error out? or whatnot.

@ImportanceOfBeingErnest
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No, the usual (data-)aspect and the box aspect are not mutually exclusive. In fact using both may make perfect sense, as shown in caseF.. (It's just thatadjustable cannot be "box" in case of using a box aspect.)

@anntzer
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anntzer commentedJul 30, 2019•
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Ah, thanks for the clarification, I missed that; ignore the nonsense I wrote above.

@jklymak
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Some things to consider: What happens if the user manually sets the position and thebox_aspect?

ax.set_position([0.1,0.1,0.9,0.2])ax.set_box_aspect(1.)

For constrained_layout, theset_position method takes the axes out of the automatic layout because we assume the user wants that position set. But I indicate that just by setting the layout boxes associated with the axes toNone inax.set_position, which you probably don't want to depend on here.

I'm not sure which behaviour you want here, but you'll need to decide which call trumps which here. Even more tricky is

ax.set_box_aspect(1.)ax.set_position([0.1,0.1,0.9,0.2])

@ImportanceOfBeingErnest
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The order of setting position and box_aspect isn't relevant. But I added a test to make sure that's the case.

@andrzejnovak
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Just so it doesn't get lost from#15010 , I would really like if it was possible to choose this behaviour as a default setting in rcParams.

@ImportanceOfBeingErnest
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ImportanceOfBeingErnest commentedAug 9, 2019•
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I mentionned my problem with an rc parameter for this in#15001 (comment):

if it's to be set at init time of any axes, it would sure cause problems e.g. with colorbar axes [also: Widget axes, insets etc.] and also the interplay between the adjustable parameter would need to be sorted before the axes is drawn.

I could imagine though to let this be taken as argument by the axes' init, to allow for something like

fig, axs = plt.subplots(2,2, subplot_kw=dict(box_aspect=1))

@andrzejnovak
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I think that's a good compromise. It could be made to work by only applying it to the first axes.append_axes,add_subplot andadd_axes would check if there already is one in the figure and go back to auto, but I could see why that would not be very transparent.

@tacaswell
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Power cycled to try to re-run against master and then belated realized this won't help with circle...

Half of the warnings were from the required target not being hit, pushed a commit making sure they are generated (we do not auto-docAxes).

@tacaswell
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ok, I understand the other half of the errors.

we auto-generate and insert

    ..table:::class: property-table       =======================================================================================   =====================================================================================================       Property                                                                                  Description       =======================================================================================   =====================================================================================================:meth:`adjustable <matplotlib.axes._base._AxesBase.set_adjustable>`                       {'box', 'datalim'}:meth:`agg_filter <matplotlib.artist.Artist.set_agg_filter>`                              a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array:meth:`alpha <matplotlib.artist.Artist.set_alpha>`                                        float or None:meth:`anchor <matplotlib.axes._base._AxesBase.set_anchor>`                               2-tuple of floats or {'C', 'SW', 'S', 'SE', ...}:meth:`animated <matplotlib.artist.Artist.set_animated>`                                  bool:meth:`aspect <matplotlib.axes._base._AxesBase.set_aspect>`                               {'auto', 'equal'} or num:meth:`autoscale_on <matplotlib.axes._base._AxesBase.set_autoscale_on>`                   bool:meth:`autoscalex_on <matplotlib.axes._base._AxesBase.set_autoscalex_on>`                 bool:meth:`autoscaley_on <matplotlib.axes._base._AxesBase.set_autoscaley_on>`                 bool:meth:`axes_locator <matplotlib.axes._base._AxesBase.set_axes_locator>`                   Callable[[Axes, Renderer], Bbox]:meth:`axisbelow <matplotlib.axes._base._AxesBase.set_axisbelow>`                         bool or 'line':meth:`box_aspect <matplotlib.axes._base._AxesBase.set_box_aspect>`                       None, or a number:meth:`clip_box <matplotlib.artist.Artist.set_clip_box>`                                  `.Bbox`:meth:`clip_on <matplotlib.artist.Artist.set_clip_on>`                                    bool:meth:`clip_path <matplotlib.artist.Artist.set_clip_path>`                                [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None]:meth:`contains <matplotlib.artist.Artist.set_contains>`                                  callable:meth:`facecolor <matplotlib.axes._base._AxesBase.set_facecolor>`                         color:meth:`fc <matplotlib.axes._base._AxesBase.set_facecolor>`                                color:meth:`figure <matplotlib.axes._base._AxesBase.set_figure>`                               `.Figure`:meth:`frame_on <matplotlib.axes._base._AxesBase.set_frame_on>`                           bool:meth:`gid <matplotlib.artist.Artist.set_gid>`                                            str:meth:`in_layout <matplotlib.artist.Artist.set_in_layout>`                                bool:meth:`label <matplotlib.artist.Artist.set_label>`                                        object:meth:`navigate <matplotlib.axes._base._AxesBase.set_navigate>`                           bool:meth:`navigate_mode <matplotlib.axes._base._AxesBase.set_navigate_mode>`                 unknown:meth:`path_effects <matplotlib.artist.Artist.set_path_effects>`                          `.AbstractPathEffect`:meth:`picker <matplotlib.artist.Artist.set_picker>`                                      None or bool or float or callable:meth:`position <matplotlib.axes._base._AxesBase.set_position>`                           [left, bottom, width, height] or `~matplotlib.transforms.Bbox`:meth:`prop_cycle <matplotlib.axes._base._AxesBase.set_prop_cycle>`                       unknown:meth:`rasterization_zorder <matplotlib.axes._base._AxesBase.set_rasterization_zorder>`   float or None:meth:`rasterized <matplotlib.artist.Artist.set_rasterized>`                              bool or None:meth:`sketch_params <matplotlib.artist.Artist.set_sketch_params>`                        (scale: float, length: float, randomness: float):meth:`snap <matplotlib.artist.Artist.set_snap>`                                          bool or None:meth:`title <matplotlib.axes._axes.Axes.set_title>`                                      str:meth:`transform <matplotlib.artist.Artist.set_transform>`                                `.Transform`:meth:`url <matplotlib.artist.Artist.set_url>`                                            str:meth:`visible <matplotlib.artist.Artist.set_visible>`                                    bool:meth:`xbound <matplotlib.axes._base._AxesBase.set_xbound>`                               unknown:meth:`xlabel <matplotlib.axes._axes.Axes.set_xlabel>`                                    str:meth:`xlim <matplotlib.axes._base._AxesBase.set_xlim>`                                   (left: float, right: float):meth:`xmargin <matplotlib.axes._base._AxesBase.set_xmargin>`                             float greater than -0.5:meth:`xscale <matplotlib.axes._base._AxesBase.set_xscale>`                               {"linear", "log", "symlog", "logit", ...}:meth:`xticklabels <matplotlib.axes._base._AxesBase.set_xticklabels>`                     List[str]:meth:`xticks <matplotlib.axes._base._AxesBase.set_xticks>`                               unknown:meth:`ybound <matplotlib.axes._base._AxesBase.set_ybound>`                               unknown:meth:`ylabel <matplotlib.axes._axes.Axes.set_ylabel>`                                    str:meth:`ylim <matplotlib.axes._base._AxesBase.set_ylim>`                                   (bottom: float, top: float):meth:`ymargin <matplotlib.axes._base._AxesBase.set_ymargin>`                             float greater than -0.5:meth:`yscale <matplotlib.axes._base._AxesBase.set_yscale>`                               {"linear", "log", "symlog", "logit", ...}:meth:`yticklabels <matplotlib.axes._base._AxesBase.set_yticklabels>`                     List[str]:meth:`yticks <matplotlib.axes._base._AxesBase.set_yticks>`                               unknown:meth:`zorder <matplotlib.artist.Artist.set_zorder>`                                      float       =======================================================================================   =====================================================================================================

into a bunch of docstrings which points to the base class (because it asks the methods what class they belong to). Will push a commit 'fixing' this soon...

@anntzer
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I think this is basically good to go.
Minor nit: perhaps name this "axes_aspect"? consistently with transAxes, with the places that accept "axes"/"data" as transform specifiers, etc. Obviously it's a bit(...) late to rename the classic "aspect" to "data_aspect", though...

@ImportanceOfBeingErnest
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I would agree that if we had.set_data_aspect, naming this.set_axes_aspect would be ideal. But given that the data aspect is by now commonly known as"the aspect", it is important to distinguish clearly.

In general we have the semantics ofobject.set_property(...) throughout the code, which reads as"set the object's property to ...". In the case of the aspect,axes.set_aspect, needs to read"set the axes' aspect to ...". Following this,axes.set_axes_aspect would read"set the axes' axes aspect to" in which case there is little indication for that to be different from"set the axes' aspect to"; and that would be the data aspect.

I therefore find.set_box_aspect much clearer: The axes has an aspect (which is related to data), and it has a box-aspect (which is related to its box).

@jklymak
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OK, I'll check later, but what if you set two axes to have different aspect ratio, and use a layout manager? I'm just a little concerned there are all sorts of edge cases here that will cause problems, but I may be completely incorrect.

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Actually because this works on theactive position of the axes, it works fine w/ the layout managers. Or at least I couldn't easily break it.

@jklymak
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I'll merge tomorrow, if no one else does, or@ImportanceOfBeingErnest you can self-merge if we give this a day or so to make sure there are no further objections.

@jklymakjklymak merged commit805cb72 intomatplotlib:masterOct 1, 2019
@jklymak
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Thanks@ImportanceOfBeingErnest I think this will be a pretty valuable tool in the axes shaping toolbox!

@ImportanceOfBeingErnestImportanceOfBeingErnest deleted the box-aspect branchOctober 3, 2019 01:25
tacaswell added a commit to tacaswell/matplotlib that referenced this pull requestMay 30, 2020
The way that bbox_inches='tight' is implemented we need to ensure thatwe do not try to adjust the aspect during the draw (because we havetemporarily de-coupled the reported figure size from the transformswhich results in the being distorted).  Previously we did not have away to fix the aspect ratio in screen space of the Axes (only theaspect ratio in dataspace) however in 3.3 we gained this ability forboth Axes (matplotlib#14917) and Axes3D (matplotlib#8896 /matplotlib#16472).Rather than add an aspect value to `set_aspect` to handle this case,in the tight_bbox code we monkey-patch the `apply_aspect` method witha no-op function and then restore it when we are done.  Previously wewould set the aspect to "auto" and restore it in the same places.closesmatplotlib#16463.
tacaswell added a commit to tacaswell/matplotlib that referenced this pull requestJun 4, 2020
The way that bbox_inches='tight' is implemented we need to ensure thatwe do not try to adjust the aspect during the draw (because we havetemporarily de-coupled the reported figure size from the transformswhich results in the being distorted).  Previously we did not have away to fix the aspect ratio in screen space of the Axes (only theaspect ratio in dataspace) however in 3.3 we gained this ability forboth Axes (matplotlib#14917) and Axes3D (matplotlib#8896 /matplotlib#16472).Rather than add an aspect value to `set_aspect` to handle this case,in the tight_bbox code we monkey-patch the `apply_aspect` method witha no-op function and then restore it when we are done.  Previously wewould set the aspect to "auto" and restore it in the same places.closesmatplotlib#16463.
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