This probably should be made into an issue on github as this is clearly a
bug.
On further consideration, the fact that in my example the bad pixels show
up only on the edge and are not symmetric makes me think that my original
suggestion is wrong. Does `interpoltation='nearest'` work any better?
I also am not sure that the rasterzation is happening so this may be an
issue on the renderer end.
This needs more investigation that I have time for today.
Tom
On Tue, Apr 7, 2015 at 11:02 AM Steven Boada <bo...@physics.tamu.edu> wrote:
> Thomas,
>
> Thanks for the smaller example. I would have come up with one, but I
> wasn't sure what was causing it to begin with.
>
> Is there anything to be done to prevent this? Just use another backend?
>
> Steven
>
> On 4/6/15 8:47 PM, Thomas Caswell wrote:
> > This is probaly due to issues with not all of the vector backends
> > supporting alpha gracefully.
> >
> > This can be reproduced more simply by
> >
> > x, y = np.ogrid[-5:5:.1, -5:5:.1]
> > dd = np.exp(-(x**2 + y**2))
> > dd[dd < .1] = np.nan
> >
> > fig, ax = plt.subplots()
> > ax.imshow(dd, interpolation='none', cmap='gray_r')
> > plt.savefig('test.pdf')
> >
> > @steven In the future it is best to report bugs with minimal
> > copy-paste able examples.
> >
> > On Mon, Apr 6, 2015 at 5:41 PM Steven Boada <bo...@physics.tamu.edu
> > <mailto:bo...@physics.tamu.edu>> wrote:
> >
> > Getting some strange artifacts when I save a figure as a PDF in
> > matplotlib. Here are some screen shots. PDF
> > <http://imgur.com/oQDXkWn> and PNG <http://imgur.com/bCw3Fn4>. Any
> > idea why that is happening?
> >
> > Here is (most of) the source code that makes the plot. I stripped
> > out the data generation, because it is long and involved, and
> > doesn't really matter. Basically what the script is supposed to do
> > is make a scatter plot where the density is below some threshold,
> > and a 2d histogram when it is above that threshold. The code seems
> > to work fine, but when I save the figure (using savefig in
> > Ipython) it shows up funny.
> >
> > Thanks.
> >
> > import pylab as pyl
> >
> > bins = [50,50]
> > thresh = 3
> >
> > xdat = #generate or load some data
> > ydat = #generate or load some data
> >
> > hh, locx, locy = pyl.histogram2d(xdat, ydat,
> > range=[[-1,4],[-26,-10]], bins=bins)
> > posx = pyl.digitize(xdat, locx)
> > posy = pyl.digitize(ydat, locy)
> >
> > # finds the bins which contain points. posx = 0 for points
> > outside "range"
> > ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <=
> > bins[1])
> > # values of histogram with points in the bins.
> > hhsub = hh[posx[ind] - 1, posy[ind] - 1]
> >
> > xdat1 = xdat[ind][hhsub < thresh] # low density points
> > ydat1 = ydat[ind][hhsub < thresh]
> > hh[hh < thresh] = pyl.nan # fill the areas with low density by
> > NaNs
> >
> > pyl.scatter(xdat1, ydat1, s=20, c='0.8')
> > pyl.imshow(pyl.log10(hh.T), cmap='gray_r',
> > extent=pyl.array([[-1,4],[-26,-10]]).flatten(),
> > interpolation='none')
> >
> > pyl.show()
> >
> > --
> >
> > Steven Boada
> >
> > Doctoral Student
> > Dept of Physics and Astronomy
> > Texas A&M University
> > bo...@physics.tamu.edu <mailto:bo...@physics.tamu.edu>
> >
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>
> --
>
> Steven Boada
>
> Doctoral Student
> Dept of Physics and Astronomy
> Texas A&M University
> bo...@physics.tamu.edu
>
>
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> Develop your own process in accordance with the BPMN 2 standard
> Learn Process modeling best practices with Bonita BPM through live
> exercises
> http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual-
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