I made some small changes which helped here - eg, deferring
> the initialization of the LUTs until they are actually
> requested. This shaved 0.3 s off startup time on my system.
> With Todd's help, I also made some changes in the core
> "fromarray" in extension code which delivered some speedups,
> and removed some extra checks in the colormapping code which
> are not needed for data that are properly normalized. I
> also think I found and fixed redundant calls to draw in some
> backends due to improper event handling and hold handling
> that crept into 0.65.
Well, Xavier Gnata just pointed out to me off list that almost half
the cost of the default image handling was in the normalization calls
to min and max. After a little poking around, I discovered we were
using python's min and max here, which means sequence API. Ouch!
So we get another 2x speedup on top of the numbers I just posted using
default normalization and colormapping.
# GTKAgg default normalization and colormapping
# 0.65
matplotlib 0.65 figimage : 9.97s
matplotlib 0.65 imshow : 9.91s
# optimization numbers in my last post
matplotlib figimage : 5.23s
matplotlib imshow : 5.18s
# as above but using nxmin and nxmax
matplotlib figimage : 2.21s
matplotlib imshow : 2.24s
So out of the box the next matplotlib will be more than 4x faster than
the last release for images. A long way from MIDAS and IRAF, but
still satisfying for a day's work.
JDH