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From: Benjamin Jonen <bjonen@gm...>  20120716 09:09:48
Attachments:
ColBorrowingForDifferentMRates.pdf

Hey everybody, Before I made the change to matplotlib I used to do my charts in MS Excel 2007. I have been using matplotlib for a while now but haven't been able to replicate my graphs in the same beauty (for an example see the attachment). 1) Can I generate additional line styles beyond [ ‘‘  ‘–’  ‘.’  ‘:’  ‘steps’  ...]? For example the empty line for value 9 in the chart would be very nice to have. 2) The coloring and the way the lines curve around looks very nice to me. I remember that the Excel charts did not have this nice look before Excel 2007. Can I achieve similar effects with matplotlib? I'm not really sure what creates this nice look, so this question is of course a little fuzzy. These questions are not meant as a criticism. I enjoy using matplotlib and I am very grateful this amazing tool has been developed. The latex integration is a clear advantage over excel. If matplotlib becomes equal or better in look to excel charts the incentives for excel users to convert will be even stronger. Thanks for any answers/thoughts, Benjamin 
From: Daπid <davidmenhur@gm...>  20120716 09:27:58

On Mon, Jul 16, 2012 at 11:09 AM, Benjamin Jonen <bjonen@...> wrote: > 2) The coloring and the way the lines curve around looks very nice to > me. I remember that the Excel charts did not have this nice look > before Excel 2007. Can I achieve similar effects with matplotlib? I'm > not really sure what creates this nice look, so this question is of > course a little fuzzy. Maybe you are thinking about the smoothness of the curves. Even you have spaced points, they don't do sharp edges. In my opinion, for scientific research, they shouldn't be concealed in the general case, and this is, I think, the main target of MPL. Nevertheless, if in your case it makes sense and you want them to be smooth, you can do it through SciPy, applying a interpolation scheme. tck=scipy.interpolate(datax, datay) datax_n=np.arange(datax.min(), datax.max(), len(datax)*20) datay_n=sicpy.interpolate(splev(datax_n,tck,der=0) And then you plot datax_n and datay_n. http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html#splineinterpolationin1dproceduralinterpolatesplxxx 
From: Nicolas Rougier <Nicolas.R<ougier@in...>  20120716 12:12:10

Here is a quick example that might help you: http://www.loria.fr/~rougier/coding/gallery/showcase/showcase10large.png http://www.loria.fr/~rougier/coding/gallery/showcase/showcase10.py Nicolas On Jul 16, 2012, at 11:27 , Daπid wrote: > On Mon, Jul 16, 2012 at 11:09 AM, Benjamin Jonen <bjonen@...> wrote: >> 2) The coloring and the way the lines curve around looks very nice to >> me. I remember that the Excel charts did not have this nice look >> before Excel 2007. Can I achieve similar effects with matplotlib? I'm >> not really sure what creates this nice look, so this question is of >> course a little fuzzy. > > Maybe you are thinking about the smoothness of the curves. Even you > have spaced points, they don't do sharp edges. In my opinion, for > scientific research, they shouldn't be concealed in the general case, > and this is, I think, the main target of MPL. > > Nevertheless, if in your case it makes sense and you want them to be > smooth, you can do it through SciPy, applying a interpolation scheme. > > tck=scipy.interpolate(datax, datay) > datax_n=np.arange(datax.min(), datax.max(), len(datax)*20) > datay_n=sicpy.interpolate(splev(datax_n,tck,der=0) > > And then you plot datax_n and datay_n. > > http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html#splineinterpolationin1dproceduralinterpolatesplxxx > >  > Live Security Virtual Conference > Exclusive live event will cover all the ways today's security and > threat landscape has changed and how IT managers can respond. Discussions > will include endpoint security, mobile security and the latest in malware > threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ > _______________________________________________ > Matplotlibusers mailing list > Matplotlibusers@... > https://lists.sourceforge.net/lists/listinfo/matplotlibusers 