matplotlib

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#spline-interpolation-in-1-d-procedural-interpolate-splxxx

Here is a quick example that might help you:

http://www.loria.fr/~rougier/coding/gallery/showcase/showcase-10-large.png
http://www.loria.fr/~rougier/coding/gallery/showcase/showcase-10.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#spline-interpolation-in-1-d-procedural-interpolate-splxxx
> 
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