<br><br><div class="gmail_quote">On Tue, Jun 26, 2012 at 12:43 AM, Zoltán Vörös <span dir="ltr"><<a href="mailto:zvoros@gmail.com" target="_blank">zvoros@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div bgcolor="#FFFFFF" text="#000000">
Dear Fernando, and Bob,<br>
<br>
I didn't want to bring up this issue before the new release, given
that that has the higher priority, but since a new thread was opened
along the same lines, I would like to respond to a couple of points
raised here, and here:
<a href="http://mail.scipy.org/pipermail/ipython-dev/2012-June/009476.html" target="_blank">http://mail.scipy.org/pipermail/ipython-dev/2012-June/009476.html</a>
which I will quote here.<br>
<br>
Based on these two threads, I have the feeling that the views on
what an interactive plot should do are somewhat diverging, and we
should first come to some consensus as to what we exactly want. <br>
<br>
First, here are a couple of use cases for the notebook (From
Fernando's comments)<br>
<br>
<blockquote type="cite">
<pre>On Fri, Jun 8, 2012 at 8:49 AM, Bob McElrath <<a href="http://mail.scipy.org/mailman/listinfo/ipython-dev" target="_blank">bob+ipython@mcelrath.org</a>> wrote:
><i> Well I'll just say that IPython and matplotlib have quickly become my primary
</i>><i> computational tool. So I would not simply call it an "exploratory" tool. I
</i>><i> could already do exploratory computing with the (i)python command line. The
</i>><i> notebook allows me to save, repeat/reproduce, and communicate a computation. In
</i>><i> particular it allows me to record how I produced the final version of a plot.
</i>
1. Individual exploratory work: play with some code, some data, ...
2. Collaboration with colleagues. ...
3. Parallel production work: ...
... if your results do pan out, you'll want to publish them!...
4. The same notebook can then be exported to latex or html for
publication. We're not really at the point of the notebook being what
you send to a journal, but the notebooks right now are perfect as
supplementary materials that remain *executable*.
</pre>
</blockquote>
<br>
I think, implementing all of these in a single tool is too
ambitious, and unrealistic, and the requirements are even
conflicting. This would mean, e.g., that the notebook should be
aware of LaTeX packages. But since at this point, it uses MathJaX,
and since MathJaX does not implement the full LaTeX command set, I
don't see how this could be pulled off. Even worse, many journals
require the use of a specific stylesheet, and figures have to be
submitted as separate files. So, one would have to implement at
least two separate backends, one for interactive work, and one for
PDFs...<br></div></blockquote><div><br></div><div>I don't see any need for tex in the browser to be aware of packages, nor for MathJax to be any limitation on latex *export* of notebooks. MathJax renders snippets for interactive use, but when you export a notebook to LaTeX,(or PDF via latex) MathJax is not involved at all. Further, you can use raw cells to add perfectly arbitrary LaTeX to the notebook (stylesheets, packages, etc.), which will be included at export. People are already doing this today.</div>
<div><br></div><div>-MinRK</div><div> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div bgcolor="#FFFFFF" text="#000000">
<br>
Also, if executability is a requirement (I think, it is a reasonable
one), then for plots we are left with js or SVG, that can work as a
stand-alone figure, without relying on the ipython kernel. Another
reason for decoupling the plot from the kernel is that in this way,
one could explore plots in the notebook, while another calculation
is running in the kernel. <br>
<br>
I would like to quote Brian's comments "I would love to see that
emerge out of matplotlib, rather than YAPL
(yet another plotting library).". I couldn't agree more. But if this
is the case, then there are some major modifications to be
implemented in matplotlib itself. I think, we have already realised
that we would have to have access to not only the canvas elements,
but the underlying data. At this point, there is no easy way of
exposing those in matplotlib. So, the bottom line is that, if this
plotting tool is to emerge from matplotlib, then we have to work
with the matplotlib developers, and we can't just piggyback on what
is already there. In such a case, however, we would have to have a
very clear idea as to what is needed, for otherwise, it will be
quite hard to convince the matplotlib developers.<br>
<br>
As I said earlier, after having played with the javascript backend,
I could implement coordinate reporting, cross-hairs, and grid
toggling in a standalone file, and more would most probably be not
possible. If this is enough, then we should just go with that. If
not, then the javascript backend is not an option, and SVG would not
offer significantly more.<br>
<blockquote type="cite">
<pre>5. ... With the traditional approaches we've mostly used so far, each of the
steps above typically required shifting tools completely. That hurts
productivity, kills reproducibility, and fundamentally impairs your
ability to *iterate* on an idea...</pre>
</blockquote>
<pre>In my view, this point is perhaps the most problematic: if the user
is capable of changing a figure significantly, then there's got to be
a mechanism for tracing those changes, otherwise, reproducibility will
be severely compromised, if at a different level. However, implementing
such a tracking mechanism is most certainly a huge undertaking.
</pre>
On 06/26/2012 06:26 AM, Fernando Perez wrote:
<blockquote type="cite">
<pre>Hi Bob,
In this spirit, have you had a chance to play with tools like this?
<div class="im"><a href="http://paperjs.org/about/" target="_blank">http://paperjs.org/about/</a>
Since it goes directly to the canvas element and is not SVG, I wonder
if interactive figures built upon it would fare any better than the
SVG ones.
</div></pre>
</blockquote>
I have looked at quite a few of the examples, and it seems to me
that this library is rather CPU-hungry. But it might just be an
issue with firefox... However, this might be a problem, even if we
start from matplotlib, and implement a javascript backend from
scratch. Static plots certainly have the advantage that once
produced, they don't require CPU resources anymore... <br>
Cheers,<br>
Zoltán<br>
<br>
</div>
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