Eric Firing wrote:
> Glenn,
>
> The slowness is almost entirely in the line
>
> rgba = lut[xa]
>
> where lut is a 2-D uint8 table and xa is an array of indices.
>
> I have replaced that in svn with
>
> rgba = lut.take(xa, axis=0)
>
> which cuts the time in half!
This should be reduce
Glenn,
The slowness is almost entirely in the line
rgba = lut[xa]
where lut is a 2-D uint8 table and xa is an array of indices.
I have replaced that in svn with
rgba = lut.take(xa, axis=0)
which cuts the time in half!
That is still not nearly as fast as the solution you have found.
Numpy 1.0.3 and MPL 0.91.2. The image array is 256 x 1024. I found I
could speed things up a lot (~15ms update time) by setting my data to
be a 256 x 1024 x 4 array of uint8, so I guess the solution is to
handle color mapping myself. I appreciate any other suggestions.
Glenn
On 4/15/08, Eric Firin
Glenn,
What version of numpy are you using? What version of matplotlib? And
what are the dimensions of your image array?
Eric
G Jones wrote:
> Thank you for the suggestion.
> I now have the update time down to about 70 ms.
> When I run the code through the profiler, I see that each plot update
Thank you for the suggestion.
I now have the update time down to about 70 ms.
When I run the code through the profiler, I see that each plot update
requires a call to matplotlib.colors.Colormap.__call__, and each of
these calls takes 52 ms, 48 ms of which is spent inside the function
itself. This l
I just use blit on imshow map, and work properly. Maybe the following code
will help you.
def ontimer()
canvas.restore_region(background)
im.set_array(Z)
ax.draw_artist(self.imList[i])
canvas.blit(ax.bbox)
canvas.gui_repaint()
--
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Hello,
I want to use imshow to make a real time waterfall plot. The attached
code is the core of my application, and it works, but it is quite
slow, around 200ms to update the plot. Is there a way to accelerate
this? I have seen the blitting demos, and they work well for the line
plots, but I could
