I think I have the Agg part of this figured out. See http://github.com/mdboom/aggravate

All that's left, then, is the Python side of things, and #5602 offers some guidance as to where to insert the floating-point interpolation operations. Ideally, the matplotlib._image.Image class on the C++ side should go away. It predates Numpy when we needed a class just to store the image data. Now, a Numpy array will do, and it would be better to just have functions on the C++ side for interpolation and coloring.

"aggravate"? You get a free beer from me at the next SciPy meeting!

On Wed, Dec 2, 2015 at 1:38 PM, Michael Droettboom <notifications@github.com

wrote:

I think I have the Agg part of this figured out. See mdboom/aggravate

All that's left, then, is the Python side of things, and #5602
#5602 offers some guidance
as to where to insert the floating-point interpolation operations. Ideally,
the matplotlib._image.Image class on the C++ side should go away. It
predates Numpy when we needed a class just to store the image data. Now, a
Numpy array will do, and it would be better to just have functions on the
C++ side for interpolation and coloring.

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#5490 (comment)
.

Roget's says it's a synonym for "magnify".

Yes, it works on so many levels. #wordsmith

On Wed, Dec 2, 2015 at 1:43 PM, Michael Droettboom <notifications@github.com

wrote:

Roget's says it's a synonym for "magnify".

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#5490 (comment)
.

shiny!

One advantage of rewriting the image interpolation as a purely functional interface is that it was really easy to add OpenMP support to take advantage of multiple cores.

It is not obviously to me which of those interplotaions is 'average all of the values that fall under this much larger pixel' (ex, coarse graining).

It is not obviously to me which of those interplotaions is 'average all of the values that fall under this much larger pixel' (ex, coarse graining).

The different filter kernels are essentially amount to weighted averages with different curves.