Paul Smith wrote:
> Hi,
> I can't find a way to plot marker symbols that have a transparent face
> colour,
> so I can see the contour plot that's under them. Setting alpha affects the
> edge colour as well.
> Any suggestions?
Paul,
If you want the symbol faces to be completely transparent, set
Hi,
I can't find a way to plot marker symbols that have a transparent face colour,
so I can see the contour plot that's under them. Setting alpha affects the
edge colour as well.
Any suggestions?
Paul
-
This SF.net email is
> I agree that exploration of large data sets is an important application,
> and that we need to speed it up. A couple days ago I added automatic
> subsetting (but not decimation--although this could be added easily) to
> image drawing, and that made a big difference for panning and zooming
> u
I just tried reverting to some older revs. r4802 (0.91.2 release) builds fine.
r4817, where the transforms branch was merged in, doesn't, and gives what looks
like the same error (see below) as the current revision (see previous post). I
think r4817 is also the first time the file _path.cpp show
thewtex wrote:
>>> Main issue is Matplotlib's performance. I'm trying to plot a current
>>> trace from a physics experiment, containing about 300,000 data points.
>>> In LabVIEW, one can easily browse through a data set like this, but I
>>> haven't been able yet to get such a good performance w
> > Main issue is Matplotlib's performance. I'm trying to plot a current
> > trace from a physics experiment, containing about 300,000 data points.
> > In LabVIEW, one can easily browse through a data set like this, but I
> > haven't been able yet to get such a good performance with
> > IPytho
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
Hello Benjamin,
I think you should use an other kind of an axes. Instead of pylba.subplot(111)
(or fig.add_subplot(111)), you could use pylab.axes([0.0, 0.0, 1.0, 1.0])
with the values (left, bottom, width, height) to avoid the gray border.
regards Matthias
On Wednesday 09 April 2008 20:37:08
Hello Adrian,
I'm not sure I understood well, but if you want a histogram with logarithmic
yscale, you may want to use something like:
import pylab
pylab.subplot(111, yscale='log')
pylab.histogram(some_nice_data)
pylab.show()
regrads Matthias
On Monday 14 April 2008 22:28:49 Adrian Price-Whel
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