From: Eric Firing <efiring@...202...>
Subject: Re: [Matplotlib-users] imshow size limitations?
Date: Saturday, September 6, 2008, 3:13 PM
David Goldsmith wrote:
> Thanks, Eric!
> --- On Sat, 9/6/08, Eric Firing > <efiring@...202...> wrote:
> -- snip OP --
>> It looks to me like you simply ran out of
>> not an imshow
>> problem as such. Your array is about 1e8
elements, and as
>> floats that
>> would be close to a GB--just for that array alone.
> Well, I anticipated that, so I do initialize the
storage for the numpy array as numpy.uint8 and have
confirmed that the data in the array returned by the
function which creates it remains numpy.uint8, so it should
"only" be ~100MB (indeed, the .na file into which
I tofile it is 85,430 KB, just as it should be for a 10800 x
8100 array of uint8 elements). And the ax.imshow statement
doesn't (directly) cause the crash (but I don't know
that it isn't making either a float copy or an in-place
conversion of the array). So, AFAIK, right up until the
> the data being imaged are all numpy.uint8 type.
Yes, but it looks to me like they are still getting
this requires conversion to numpy.float. This may be a bad
the mpl design, but it is quite deeply embedded. I suspect
the best we
could do would be to use float32 instead of float64;
certainly for color
mapping one does not need 64 bits.
Using numpy.uint8 helps only if you are specifying RGBA
bypassing the colormapping.
>> really need
>> all that resolution?
> Well, there's the rub: I fancy myself a fractal
"artist" and I have
> access to an HP DesignJet 500ps plotter with a maximum
> 1200 x 600 dpi. For the size images I'm trying to
make (I'm hoping to go
> even bigger than 36" x 27", but I figured
that as a good starting point)
> even I regard _that_ resolution as too much - I was
thinking of 300 x
> 300 dpi (which is its "normal" resolution)
as certainly worthy of giving
> a try.
>> If you do, you will probably have to
>> get a much
>> more capable machine.
> Possible, but I was hoping to generate at least one
"proof" first to determine how hard I'd need
>> Otherwise, you need to knock down
>> the size of
>> that array before trying to plot or otherwise
> Forgive me, but I'd like a more detailed
explanation as to why: I
> ample (~35 GB, just on my built-in disc, much more
than that on external
> discs) harddisc space - isn't there some way to
I don't know enough about virtual memory
Win or Mac--to say. In practice, I suspect you would find
that as soon
as you are doing major swapping during a calculation, you
the disk until you run out of patience.
>> With respect to imshow, probably you can get it to
>> larger images
> Again, imshow doesn't appear to be the culprit
(contrary to my
> original subject line), rather it would appear to be
> canvas.print_figure. (While I'm on the subject of
> isn't there some way for MPL to "splash"
the image directly to the
> screen, without first having to write to a file? I
didn't ask this
> before because I did eventually want to write the
image to a file, but I
> would prefer to do so only after I've had a look
It is imshow in the sense that most of the action in mpl
when you call imshow or plot or whatever--they just set
things up. The
real work is done in the backend when you display with
show() or write
to a file.
>> if you feed them in as NxMx4 numpy.uint8 RGBA
>> doubt this
>> is going to be enough, or the right approach, for
>> present situation.
> Right: I don't see how that would be better than
having a single 8
> datum at each point w/ color being determined from a
color map (which is
> how I'd prefer to do it anyway).
The way it is better is that it avoids a major operation,
generation of the double-precision array. The rgba array
straight to agg.
> Thanks again,
>>> Platform Details: MPL 0.91.2 (sorry, I
>> realize I was running such an old version, maybe I
>> to upgrade?), Python 2.5.2, Windows XP 2002 SP3,
>> physical RAM, 1294MB VM Page size (1000MB init.,