<div><div dir="auto">Doing #2 properly requires passing depth information fully down into the rasterizer (e.g. Agg), and having it compute z for each pixel (fragment). That’s a ton of work, and GPUs already do this.</div><div dir="auto"><br></div><div dir="auto">Ryan</div><br><div class="gmail_quote"><div>On Tue, Nov 28, 2017 at 3:02 AM Antony Lee <<a href="mailto:antony.lee@berkeley.edu">antony.lee@berkeley.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div>A bit late to the party, but here are some (not very deep) thoughts regarding 3D support.</div><div><br></div><div>1. I believe that writing a new 3D projection class from scratch, properly integrated with the transforms system (so with support for log-scale, etc.), should be a "relatively easy" (if somewhat tedious and lengthy...) task, for someone who knows Matplotlib's internals well (probably easier than trying to forcefully modernize mplot3d). All the machinery is already there. Given the way projections work, this can be started as a third-party project (requiring explicit registration by import, just like mplot3d does now) and later be integrated into matplotlib's core.</div><div><br></div><div>2. More difficult to resolve is z-buffering (i.e., support for overlapping polygons that cannot be sorted unambiguously using a single z-value). As far as I can tell, this would require adding a new method on the renderer classes (because there is simply no way to pass z-information to the renderer right now -- z-sorting occurs before data reaches the renderer), and, well, implementing it.</div></div><div><div><br></div><div>Antony</div><div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">2017-11-20 12:25 GMT-08:00 <a href="mailto:vincent.adrien@gmail.com" target="_blank">vincent.adrien@gmail.com</a> <span><<a href="mailto:vincent.adrien@gmail.com" target="_blank">vincent.adrien@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Thank you Tom for the summary (a well as Ryan and Eric for the meeting of course).<br>
<br>
Just being curious, does the following point (at the very end of your email) mean that there is some plan to perform kind of a big overhaul of mplot3d?<span><br>
<br>
> ** major funding<br>
> - get mplot3D 'right'<br>
> - same interface<br>
> - uses real 3D tools<br>
<br></span>
I am asking mainly because I cannot find a MEP about that in the documentation, even though it seems like a rather major goal/news to me :). But I may have misunderstood something about what is stated above...<br>
<br>
Best,<br>
Adrien<div><div class="m_2263510692038078490h5"><br>
<br>
<br>
On 11/20/2017 12:06 PM, Thomas Caswell wrote:<br>
</div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div class="m_2263510692038078490h5">
Folks,<br>
<br>
Notes from today's phone call. There are 3 things left for 2.1.1: categorical changes, 'fuzzy' images when mostly invalid, and the appevyor cleanup<br>
<br>
Tom<br>
<br>
Ryan May, Eric Firing, Thomas Caswell<br>
<br>
** categorical<br>
<br>
- everyone on board with not sorting categorical values<br>
- everyone on board with only accepting strings as categories<br>
- some concern about supporting `np.nan`<br>
- if the first entry in `nan`, will miss units<br>
- do not want a python loop that chceks until it finds a not-nan<br>
- defer nan handling<br>
<br>
Tom's job to get all of the PRs collected an into one<br>
<br>
Plan going forward to support mixed types, missing data, and explicit<br>
ordering between categories:<br>
- write a category class<br>
- write a handler for pandas categorical<br>
<br>
** Tom's pre-meeting notes<br>
<br>
*** do not sort values<br>
<br>
On one hand, sorting the values make sense as<br>
<br>
#+BEGIN_SRC python<br>
fig, (ax1, ax2) = plt.subplots(2, 1)<br>
ax1.scatter([1, 2], [1, 2])<br>
ax2.scatter([2, 1], [2, 1])<br>
<br>
#+END_SRC<br>
<br>
should produce visually identical plots so Tom thinks<br>
<br>
<br>
#+BEGIN_SRC python<br>
fig, (ax1, ax2) = plt.subplots(2, 1)<br>
ax1.scatter(['a', 'b'], [1, 2])<br>
ax2.scatter(['b', 'a'], [2, 1])<br>
<br>
#+END_SRC<br>
<br>
should as well (but Tom is wrong).<br>
<br>
On the other hand, user may expect that there is some semantics in the<br>
order they pass the data in in<br>
<br>
#+BEGIN_SRC python<br>
plt.bar(['first', 'second', 'third'], [1, 2, 3])<br>
<br>
#+END_SRC<br>
<br>
and blindly sorting alphabetically gives them no escape hatch.<br>
<br>
practicality over purity, drop the sorting.<br>
<br>
*** supporting non-string values<br>
<br>
In the original implementation a cast through numpy was used which<br>
converted all non-string values to strings so things like<br>
<br>
#+BEGIN_SRC python<br>
plt.bar([1, 2, 'apple'], [1, 2, 3])<br>
<br>
#+END_SRC<br>
<br>
would work. However this lead to the =2= and ='2'= being treated as<br>
the same (which seems less than great). Supporting them as different<br>
is possible, but is a fair bit of work because a number of places the<br>
unit framework assumes that 'plain' numbers will pass through<br>
un-changed.<br>
<br>
A more worrying concern is that<br>
<br>
#+BEGIN_SRC python<br>
x = [52, 32, 'a', 'b']<br>
y = [0, 1, 2, 3]<br>
<br>
fig, (ax1, ax2) = plt.subplots()<br>
ax1.plot(x, y, 'o')<br>
ax2.plot(x[:2], y[:2], 'x')<br>
<br>
#+END_SRC<br>
<br>
in the first case the ints are treated as categoricals and in the<br>
second they are not. If we want to support mixed types like this then<br>
we need to make a special class (or use pandas categorical) which does<br>
not have to guess the type on the fly.<br>
<br>
requiring if the categorical unit handling is triggered, then all of the values<br>
must be string-like seems like the safest approach.<br>
<br>
*** support for nan<br>
<br>
Most of matptollib accepts `np.nan` as 'missing' data which is then<br>
dropped as part of the draw process. This makes less sense with `bar` but makes<br>
lots of sense with `scatter`.<br>
<br>
We should special-case allowing `np.nan` in as a 'string' and map it<br>
map it to it's self.<br>
<br>
*** special containers<br>
<br>
It was proposed to look for objects arrays as a marker for catagorical<br>
instead of the type of the data. Do not think we should do this as we try to<br>
be as agnostic about the container as possible everywhere else.<br>
<br>
<br>
** appveyor<br>
- drop building conda package<br>
- remove conda recipe from the repo<br>
<br>
Ryan is taking care of this<br>
<br>
** set_theta_grid(frac)<br>
- merged, improvement over current behavior, raising seems too<br>
aggressive<br>
<br>
** #8947<br>
- ringing with lots of nans<br>
<br>
this is Tom's job to investigate<br>
<br>
** talked about traits / traitlets and friends<br>
<br>
** major funding<br>
- get mplot3D 'right'<br>
- same interface<br>
- uses real 3D tools<br>
<br>
<br></div></div>
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</blockquote>
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</blockquote></div></div><div dir="ltr">-- <br></div><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div>Ryan May<br><br></div></div></div>