imshow size limitations?

Hi! I'm trying to display a 10800 x 8100 pixel image w/ imshow using the following code (adapted from a response to a previous post of mine):

from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure

fig = Figure(figsize=(36,27),
             dpi=300,
             frameon=False)
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111, xticks=[], yticks=[])
cmap = MPL.cm.get_cmap('prism_r')
ax.imshow(result, cmap=cmap)
canvas.print_figure('HiResHex')

I get the following error report:

Traceback (most recent call last):
  File "Hex.py", line 208, in <module>
    canvas.print_figure('HiResHex')
  File "C:\python25\lib\site-packages\matplotlib\backend_bases.py", line 1201, i
n print_figure
    self.figure.canvas.draw()
  File "C:\python25\lib\site-packages\matplotlib\backends\backend_agg.py", line
358, in draw
    self.figure.draw(self.renderer)
  File "C:\python25\lib\site-packages\matplotlib\figure.py", line 624, in draw
    for a in self.axes: a.draw(renderer)
  File "C:\python25\lib\site-packages\matplotlib\axes.py", line 1305, in draw
    for im in self.images if im.get_visible()]
  File "C:\python25\lib\site-packages\matplotlib\image.py", line 131, in make_im
age
    x = self.to_rgba(self._A, self._alpha)
  File "C:\python25\lib\site-packages\matplotlib\cm.py", line 75, in to_rgba
    x = self.norm(x)
  File "C:\python25\lib\site-packages\matplotlib\colors.py", line 593, in __call

···

__
    val = ma.asarray(value).astype(npy.float)
  File "C:\python25\lib\site-packages\numpy\core\ma.py", line 1151, in astype
    d = self._data.astype(tc)
MemoryError

Is there some maximum number of pixels imshow can handle? Any other suggestions?

Platform Details: MPL 0.91.2 (sorry, I didn't realize I was running such an old version, maybe I just need to upgrade?), Python 2.5.2, Windows XP 2002 SP3, 504MB physical RAM, 1294MB VM Page size (1000MB init., 5000MB max)

Thanks!

DG

Oh, forgot to mention: same code works fine on a smaller (fewer pixels) image.

DG

···

--- On Sat, 9/6/08, David Goldsmith <d_l_goldsmith@...9...> wrote:

From: David Goldsmith <d_l_goldsmith@...9...>
Subject: [Matplotlib-users] imshow size limitations?
To: matplotlib-users@lists.sourceforge.net
Date: Saturday, September 6, 2008, 10:46 AM
Hi! I'm trying to display a 10800 x 8100 pixel image w/
imshow using the following code (adapted from a response to
a previous post of mine):

from matplotlib.backends.backend_agg import FigureCanvasAgg
as FigureCanvas
from matplotlib.figure import Figure

fig = Figure(figsize=(36,27),
             dpi=300,
             frameon=False)
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111, xticks=[], yticks=[])
cmap = MPL.cm.get_cmap('prism_r')
ax.imshow(result, cmap=cmap)
canvas.print_figure('HiResHex')

I get the following error report:

Traceback (most recent call last):
  File "Hex.py", line 208, in <module>
    canvas.print_figure('HiResHex')
  File
"C:\python25\lib\site-packages\matplotlib\backend_bases.py",
line 1201, i
n print_figure
    self.figure.canvas.draw()
  File
"C:\python25\lib\site-packages\matplotlib\backends\backend_agg.py",
line
358, in draw
    self.figure.draw(self.renderer)
  File
"C:\python25\lib\site-packages\matplotlib\figure.py",
line 624, in draw
    for a in self.axes: a.draw(renderer)
  File
"C:\python25\lib\site-packages\matplotlib\axes.py",
line 1305, in draw
    for im in self.images if im.get_visible()]
  File
"C:\python25\lib\site-packages\matplotlib\image.py",
line 131, in make_im
age
    x = self.to_rgba(self._A, self._alpha)
  File
"C:\python25\lib\site-packages\matplotlib\cm.py",
line 75, in to_rgba
    x = self.norm(x)
  File
"C:\python25\lib\site-packages\matplotlib\colors.py",
line 593, in __call
__
    val = ma.asarray(value).astype(npy.float)
  File
"C:\python25\lib\site-packages\numpy\core\ma.py",
line 1151, in astype
    d = self._data.astype(tc)
MemoryError

Is there some maximum number of pixels imshow can handle?
Any other suggestions?

Platform Details: MPL 0.91.2 (sorry, I didn't realize I
was running such an old version, maybe I just need to
upgrade?), Python 2.5.2, Windows XP 2002 SP3, 504MB physical
RAM, 1294MB VM Page size (1000MB init., 5000MB max)

Thanks!

DG

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David Goldsmith wrote:

Hi! I'm trying to display a 10800 x 8100 pixel image w/ imshow using the following code (adapted from a response to a previous post of mine):

from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure

fig = Figure(figsize=(36,27), dpi=300, frameon=False)
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111, xticks=[], yticks=[])
cmap = MPL.cm.get_cmap('prism_r')
ax.imshow(result, cmap=cmap)
canvas.print_figure('HiResHex')

I get the following error report:

Traceback (most recent call last):
  File "Hex.py", line 208, in <module>
    canvas.print_figure('HiResHex')
  File "C:\python25\lib\site-packages\matplotlib\backend_bases.py", line 1201, i
n print_figure
    self.figure.canvas.draw()
  File "C:\python25\lib\site-packages\matplotlib\backends\backend_agg.py", line
358, in draw
    self.figure.draw(self.renderer)
  File "C:\python25\lib\site-packages\matplotlib\figure.py", line 624, in draw
    for a in self.axes: a.draw(renderer)
  File "C:\python25\lib\site-packages\matplotlib\axes.py", line 1305, in draw
    for im in self.images if im.get_visible()]
  File "C:\python25\lib\site-packages\matplotlib\image.py", line 131, in make_im
age
    x = self.to_rgba(self._A, self._alpha)
  File "C:\python25\lib\site-packages\matplotlib\cm.py", line 75, in to_rgba
    x = self.norm(x)
  File "C:\python25\lib\site-packages\matplotlib\colors.py", line 593, in __call
__
    val = ma.asarray(value).astype(npy.float)
  File "C:\python25\lib\site-packages\numpy\core\ma.py", line 1151, in astype
    d = self._data.astype(tc)
MemoryError

Is there some maximum number of pixels imshow can handle? Any other suggestions?

David,

It looks to me like you simply ran out of memory--this is 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. Do you really need all that resolution? If you do, you will probably have to get a much more capable machine. Otherwise, you need to knock down the size of that array before trying to plot or otherwise manipulate it.

With respect to imshow, probably you can get it to handle larger images if you feed them in as NxMx4 numpy.uint8 RGBA arrays--but I doubt this is going to be enough, or the right approach, for your present situation.

Eric

···

Platform Details: MPL 0.91.2 (sorry, I didn't realize I was running such an old version, maybe I just need to upgrade?), Python 2.5.2, Windows XP 2002 SP3, 504MB physical RAM, 1294MB VM Page size (1000MB init., 5000MB max)

Thanks!

DG

Thanks, Eric!

-- snip OP --

It looks to me like you simply ran out of memory--this is
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. Do you

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 statement:

canvas.print_figure('HiResHex')

the data being imaged are all numpy.uint8 type.

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 resolution of 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. :slight_smile:

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 to try.

Otherwise, you need to knock down
the size of
that array before trying to plot or otherwise manipulate
it.

Forgive me, but I'd like a more detailed explanation as to why: I have ample (~35 GB, just on my built-in disc, much more than that on external discs) harddisc space - isn't there some way to leverage that?

With respect to imshow, probably you can get it to handle
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 canvas.print_figure, 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 at it.)

if you feed them in as NxMx4 numpy.uint8 RGBA arrays--but I
doubt this
is going to be enough, or the right approach, for your
present situation.

Right: I don't see how that would be better than having a single 8 bit datum at each point w/ color being determined from a color map (which is how I'd prefer to do it anyway).

Thanks again,

DG

···

--- On Sat, 9/6/08, Eric Firing <efiring@...202...> wrote:

Eric

>
> Platform Details: MPL 0.91.2 (sorry, I didn't
realize I was running such an old version, maybe I just need
to upgrade?), Python 2.5.2, Windows XP 2002 SP3, 504MB
physical RAM, 1294MB VM Page size (1000MB init., 5000MB max)
>
> Thanks!
>
> DG

David Goldsmith wrote:

Thanks, Eric!

-- snip OP --

It looks to me like you simply ran out of memory--this is
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. Do you

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 statement:

canvas.print_figure('HiResHex')

the data being imaged are all numpy.uint8 type.

Yes, but it looks to me like they are still getting color-mapped, and this requires conversion to numpy.float. This may be a bad aspect of 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 directly, 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 resolution of
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. :slight_smile:

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 to try.

Otherwise, you need to knock down
the size of that array before trying to plot or otherwise manipulate
it.

Forgive me, but I'd like a more detailed explanation as to why: I
have
ample (~35 GB, just on my built-in disc, much more than that on external
discs) harddisc space - isn't there some way to leverage that?

I don't know enough about virtual memory implementations--especially on 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 will thrash the disk until you run out of patience.

With respect to imshow, probably you can get it to handle
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 canvas.print_figure,
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 at it.)

It is imshow in the sense that most of the action in mpl doesn't happen 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 arrays--but I
doubt this is going to be enough, or the right approach, for your
present situation.

Right: I don't see how that would be better than having a single 8
bit
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, including the generation of the double-precision array. The rgba array can go straight to agg.

Eric

···

--- On Sat, 9/6/08, Eric Firing <efiring@...202...> wrote:

Thanks again,

DG

Eric

Platform Details: MPL 0.91.2 (sorry, I didn't

realize I was running such an old version, maybe I just need
to upgrade?), Python 2.5.2, Windows XP 2002 SP3, 504MB
physical RAM, 1294MB VM Page size (1000MB init., 5000MB max)

Thanks!

DG

Ah, Ich verstehe now. I'll try RGBA-ing it; in the meantime, let me know if the colormapping conversion gets changed to 32 bit. Thanks again!

DG

···

--- On Sat, 9/6/08, Eric Firing <efiring@...202...> wrote:

From: Eric Firing <efiring@...202...>
Subject: Re: [Matplotlib-users] imshow size limitations?
To: d_l_goldsmith@...9...
Cc: matplotlib-users@lists.sourceforge.net
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
memory--this is
>> 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.
Do you
>
> 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
statement:
>
> canvas.print_figure('HiResHex')
>
> the data being imaged are all numpy.uint8 type.

Yes, but it looks to me like they are still getting
color-mapped, and
this requires conversion to numpy.float. This may be a bad
aspect of
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
directly,
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
resolution of
> 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. :slight_smile:

>> 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
to try.
>
>> Otherwise, you need to knock down
>> the size of
>> that array before trying to plot or otherwise
manipulate
>> it.
>
> Forgive me, but I'd like a more detailed
explanation as to why: I
> have
> ample (~35 GB, just on my built-in disc, much more
than that on external
> discs) harddisc space - isn't there some way to
leverage that?

I don't know enough about virtual memory
implementations--especially on
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
will thrash
the disk until you run out of patience.

>> With respect to imshow, probably you can get it to
handle
>> 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
canvas.print_figure,
> 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
at it.)

It is imshow in the sense that most of the action in mpl
doesn't happen
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
arrays--but I
>> doubt this
>> is going to be enough, or the right approach, for
your
>> present situation.
>
> Right: I don't see how that would be better than
having a single 8
> bit
> 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,
including the
generation of the double-precision array. The rgba array
can go
straight to agg.

Eric

> Thanks again,
>
> DG
>> Eric
>>
>>> Platform Details: MPL 0.91.2 (sorry, I
didn't
>> realize I was running such an old version, maybe I
just need
>> to upgrade?), Python 2.5.2, Windows XP 2002 SP3,
504MB
>> physical RAM, 1294MB VM Page size (1000MB init.,
5000MB max)
>>> Thanks!
>>>
>>> DG