On 08/27/2013 09:49 AM, Chris Beaumont wrote:
I've been burned by this before as well. MPL stores some intermediate
data products (for example, scaled RGB copies) at full resolution,
even though the final rendered image is downsampled depending on
screen resolution.
I've used some hacky tricks to get around this, which mostly involve
downsampling the image on the fly based on screen resolution. One such
effort is at https://github.com/ChrisBeaumont/mpl-modest-image.
It looks like this wouldn't be too hard to include in matplotlib. I
don't think we'd want to change the current behavior, because sometimes
its tradeoff curve makes sense, but in other cases, the "modest image"
approach also makes sense. It's just a matter of coming up with an API
to switch between the two behaviors. Pull request?
Cheers,
Mike
If you are loading your arrays from disk, you can also use
memory-mapped arrays -- this prevents you from loading all the data
into RAM, and further cuts down on the footprint.
cheers,
chris
On Tue, Aug 27, 2013 at 6:49 AM, S(te(pán Turek
<stepan.tu...@seznam.cz <mailto:stepan.tu...@seznam.cz>> wrote:
You could look at whether or not you actually need 64-bit
precision. Often times, 8-bit precision per color channel is
justifiable, even in grayscale. My advice is to play with the
dtype of your array or, as you mentioned, resample.
thanks, this helped me significantly, uint8 precision is enough.
Also, is it needed to keep all images? It sounds to me like
your application will become very resource hungry if you're
going to be displaying several of these 2D images over each
other (and if you don't use transparency, you won't get any
benefit at all from plotting them together).
Yes, I need them all .
To avoid it I am thinking about merging them into one image and
then plot it.
Stepan
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