On Wed, Jul 1, 2009 at 2:34 PM, Michael Droettboom<md...@stsci.edu> wrote:
> I agree with Jae-Joon here -- try to reduce the number of points before
> passing it to matplotlib.
>
> However, I'm a little concerned about the segfault -- I'd rather matplotlib
> give a MemoryError exception if that's in fact what is happening. Jae-Joon
> -- can you share your test that causes the segfault?
>
> The snippet below completely hogs my machine for a few minutes, but then,
> correctly, aborts with a MemoryError.
>
> This is on FC11 i586, Python 2.6, Numpy 1.3.
>
> ====
>
> from matplotlib.pyplot import *
> import numpy as np
>
> points = np.random.random((50000000, 2))
> plot(points)
> show()
>
Yes, I also got MemoryError in this case during the plot() call.
But I got segfault for the code below.
x=random(50e6)
y=random(50e6)
plt.plot(x, y)
plt.show()
In this case, plot() runs fine, but segfault during show().
The segfault happens in the _path_module::affine_transform method of
src/_path.cpp.
I wonder if you can reproduce this.
-JJ
> ====
>
> Mike
>
> On 07/01/2009 01:34 PM, Jae-Joon Lee wrote:
>
> A snippet of code does not help much.
> Please try to post a small concise standalone example that we can run and
> test.
>
> A general advise is to try to reduce the number of plot call, i.e.,
> plot as may points as possible with a single plot call.
>
> However, 50million points seems to be awful a lot.
> 6 inch x 6 inch figure with dpi=100 has 0.36 million number of pixels.
> My guess is that it makes little sense to plot 50 million points here.
>
> Anyhow, plotting 50million points with a single plot call dies with
> some segfault error in my machine. So, I feel that matplotlib may not
> be suitable for your task. But, John or others may have some insight
> how to deal with.
>
> Regards,
>
> -JJ
>
>
>
> On Tue, Jun 30, 2009 at 1:22 PM, Markus Feldmann<feldmann_mar...@gmx.de>
> wrote:
>
>
> Hi All,
>
> my program lets slow down my cpu. This only appears if i plot to much
> points. I am not sure how many point i need to get this, normally i plot
> 3*14e6 + 8e3, that is round about 50million points. My system is a
> dual core 2GHz cpu with 2Gbyte Ram.
>
> Here is my method to plot,
> def drawtransientall(self,min):
> self.subplot = self.figure.add_subplot(111)
> self.subplot.grid(True)
> list_t1,list_peaks,t2,list_samples =
> self.computetransientall(min,min+self.maxitems)
> offset = 0
> color = ['green','red','blue','magenta','cyan']
> markerPeaks = ['v','<','1','3','s']
> markerSamples = ['^','>','2','4','p']
> self.plots=[[],[]]
> for i in range(len(self.showBands)):
> self.plots[0] +=
> self.subplot.plot(list_t1[i],list_peaks[i],color=color[i],marker=markerPeaks[i],
> linestyle='None')
> self.plots[1] +=
> self.subplot.plot(t2,list_samples[i]+offset,color=color[i],
>
> marker=markerSamples[i],linestyle='None')
> offset +=1
>
> self.subplot.set_xlim(t2[0]-np.abs(t2[-1]-t2[0])/100,t2[-1]+np.abs(t2[-1]-t2[0])/100)
> ymax = np.amax(list_samples)
> ymin = np.amin(list_samples)
> self.subplot.set_ylim([ymin-np.abs(ymin)*0.1, ymax*1.2 + 2])
> self.subplot.set_ylabel("abs(Sample(t)) und
> abs(Peak(t)+Offset)-->",fontsize = 12)
> self.subplot.set_xlabel("Zeit in Sek. -->",fontsize = 12)
>
> Any ideas how to avoid the slow down of my cpu ?
>
> regards Markus
>
>
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