I tracked this down do line 962 of the _path.cpp.
double* vertex_out = (double*)PyArray_DATA(result);
My guess is that PyArray_SimpleNew at line 957 returns NULL for a
memory error instead of raising an exception, which makes result=NULL
and causes a segfault at line 962.
Since I'm not an c++ expert, I'll leave it to you, Michael.
Regards,
-JJ
On Wed, Jul 1, 2009 at 3:16 PM, Jae-Joon Lee<lee.j.j...@gmail.com> wrote:
> 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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