On Thu, Jan 28, 2016 at 3:23 PM, Andreas Mueller <t3k...@gmail.com> wrote:
> Hi all.
>
> This is about a joint jupyter-notebook / matplotlib problem I've been
> thinking about.
> So I'm writing a book using jupyter-notebook, and all my figures are
> generated using matplotlib.
>
> In books, there is usually a figure caption with a running number and
> some description.
> From what I read, the best way to add captions is just using plt.text.
> However, the caption should probably be in the markup,
> not in a rendered PNG. I'm not sure if changing the backend might help,
> but that probably doesn't make the notebook happy?
>
> The other problem is that I want to have running numbers that I can
> refer to by a tag (as you would in latex).
> That is more of a notebook problem, though.
>
> Any feedback would be very welcome
>
I've been wanting to do something about this problem for a while, but
haven't had the cycles to work on it... Here's my current idea, perhaps I
can goad you into implementing it :)
I think that IPython.display should provide a Figure object, capable of
wrapping any input image (with nice code to automatically swallow a
matplotlib figure without asking the user to convert it to an image first),
and taking an optional caption.
Figure() would then produce as output the displayed image but with a bit of
nice CSS to center it on the page, along with the caption.
The trick is to send the entire data bundle correctly structured so that,
at the other end, nbconvert could recognize these figures as such, and not
only produce nice HTML, but more importantly, push them into the LaTeX
output with the correct call to \figure, including \caption as well as size
and placement specifiers.
The signature of Figure() might be something like
def Figure(fig, caption=None, width=None, height=None,
latex_placement=None):
I would try implementing this first as a standalone tool, and once it's
been tested enough in real-world usage with both HTML and LaTeX output from
nbconvert, it could be merged in. I suspect it's going to take a few
iterations to get it right.
But it's not particularly hard, and someone working on a book would be the
perfect candidate to have enough test cases to be able to iterate until
happy ;)
If you think you want to take a stab at this, don't hesitate to ping us on
the jupyter list. We can help with some of the more obscure parts of
getting this to work on nbconvert (and there may be things I've overlooked
in the sketch above).
Cheers,
f
--
Fernando Perez (@fperez_org; http://fperez.org)
fperez.net-at-gmail: mailing lists only (I ignore this when swamped!)
fernando.perez-at-berkeley: contact me here for any direct mail
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