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Fix image interpolation#8966
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In rotate_image.png, the edge of the image seems to follow the middle of the dashes more closely in the original version. |
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👍 on the approach--interpolating before doing any other transforms makes sense to me. I'm also a little concerned about the change torotate_image, but no clue what's going wrong.
Caught a couple typos in the comments.
lib/matplotlib/image.py Outdated
| # we are done with A_scaled now, remove from namespace to be sure! | ||
| del A_scaled | ||
| # un-scale the resampled data to approximatly the |
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approximatly -> approximate
lib/matplotlib/image.py Outdated
| self.get_filterrad() or 0.0) | ||
| # we are done with the mask, delete from namespace to be sure! | ||
| del mask | ||
| # Agg tells us a pixel has no value not setting a value into it |
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I think there's a word missing in this comment.
a883cd2 to2db0d6aCompareI anticipate re-writing history here before it gets merged. |
I see some failures locally that I do not understand, let see if we see them on travis. Still have to sort out those edge effects. History will get re-written at least once more before this is ready to merge. |
I am actually comfertable that the edges are the correct behavior. If you look at the zoomed in bottom 3x3 area
you can see that the out of range area is against the edge, the bottom right is the highest value which is equal to vmax of the norm. It is correct, from the interpolations point of view, that the pixel past the last point (which it view as being at the center of that pixel) are of greater value (which are then correctly marked as out of range). |
c511460 todb19f9fCompare1962d84 to1e160aaComparetacaswell commentedAug 7, 2017 • edited
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edited
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Todos:
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- interpolate raw, not normed, data - should reduce memory footprint, only 1 or 2 copies of input data - this will change many tests in small ways - down-casting input data before interpolation was causing issues with numerical precision so work in no less than the input float type - Numpy 1.7 does not support `np.nanmin` and `np.nanmax` on masked arrays (instead of returning a number it returns a MaskedIterator).
- update to mpl20 style - re-generate as needed - bump threshold on pngsuite test
- bump to mpl20 style
1e160aa toec43d0eComparefromcopyimportcopyimportmatplotlib.imageasmimagedefinterpolation_grid(data,vmin=None,vmax=None):cm=copy(plt.get_cmap('viridis'))cm.set_over('r')cm.set_under('b')cm.set_bad('k')fig,ax_grid=plt.subplots(3,6,sharex=True,sharey=True)ret= []forinterp,axinzip(sorted(mimage._interpd_),ax_grid.ravel()):ax.set_title(interp)im=ax.imshow(data,cmap=cm,interpolation=interp)ax.axis('off')im.set_clim(vmin,vmax)ret.append(im)returnret ims_a=interpolation_grid([[0,1], [1,0]],0,1)ims_b=interpolation_grid(np.tile([[0,1], [1,0]], (10,10)),0,1) I am satisfied that the speckles are edge effects on the higher-order interpolators That is the smallest upper limit that shows the speckles on my system which gives us an idea of the level of the numeric instability. I don't think that it is worth playing games to stay just under it as I suspect it will vary between systems. If we do anything about this we should bump the Autoscales out a bit. |
Push the stale flag down into the ScalarMappable so that it getsapplied to all ScalarMappable sub-classes (ex Collections).
8186243 to346bb1aCompare
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This is an attempt at#8631 and#6952
I expect this to fail. Already updated a bunch of images that had minor changes. There are a few with big changes that I still need to look into.
look into scaling to a bitless than full range to prevent speckles near vmin/vmax