Re: [matplotlib-devel] Better defaults all around?

I'd like to propose an update to the default boxplot symbology: all black

Q: How much more black could the boxplots be?
A: None. None more black.

(sorry, ben)

On Fri, Nov 21, 2014 at 7:18 PM, Benjamin Root <ben...@ou...> wrote:

> With regards to defaults for 2.0, I am actually all for breaking them for
> the better. What I find important is giving users an easy mechanism to use
> an older style, if it is important to them. The current behavior isn't
> "buggy" (for the most part) and failing to give users a way to get behavior
> that they found desirable would be alienating. I think this is why projects
> like prettyplotlib and seaborn have been so important to matplotlib. It
> enables those who are in the right position to judge styles to explore the
> possibilities easily without commiting matplotlib to any early decision and
> allowing it to have a level of stability that many users find attractive.
>
> At the moment, the plans for the OO interface changes should not result in
> any (major) API breaks, so I am not concerned about that at the moment.
> Let's keep focused on style related issues in this thread.
>
> Tabbed figures? Intriguing... And I really do need to review that MEP of
> yours...
>
> Cheers!
> Ben Root
>
> On Fri, Nov 21, 2014 at 9:36 PM, Federico Ariza <ari...@gm...>
> wrote:
>
>> I like the idea of aligning a set of changes for 2.0 even if still far
>> away.
>>
>> Regarding to backwards compatibility I think that indeed it is important
>> but when changing mayor version (1.x to 2.0) becomes less important and we
>> must take care of prioritizing evolution.
>> Take for example the  OO interface (not defined yet) this is very
>> probable to break the current pyplot interface but still this is a change
>> that needs to be done.
>>
>> In terms of defaults. I would like to see the new Navigation as default
>> (if it gets merged) and tabbed figures (to come after navigation), having
>> separate figures feel kind of ..."old"
>> On 21 Nov 2014 21:23, "Benjamin Root" <ben...@ou...> wrote:
>>
>>> Some of your wishes are in progress already:
>>> https://github.com/matplotlib/matplotlib/pull/3818
>>> There is also an issue open about scaling the dashes with the line
>>> width, and you are right, the spacing for the dashes are terrible.
>>>
>>> I can definitely see the argument to making a bunch of these visual
>>> changes together. Preferably, I would like to do these changes via style
>>> sheets so that we can provide a "classic" stylesheet for backwards
>>> compatibility.
>>>
>>> I do actually like the autoscaling system as it exists now. The problem
>>> is that the data margins feature is applied haphazardly. The power spectra
>>> example is a good example of where we could "smarten" the system. As for
>>> the ticks... I think that is a very obscure edge-case. I personally prefer
>>> inward.
>>>
>>> It is good to get these grievances enumerated. I am interested in seeing
>>> where this discussion goes.
>>>
>>> Cheers!
>>> Ben Root
>>>
>>> On Fri, Nov 21, 2014 at 6:22 PM, Nathaniel Smith <nj...@po...> wrote:
>>>
>>>> Hi all,
>>>>
>>>> Since we're considering the possibility of making a matplotlib 2.0
>>>> release with a better default colormap, it occurred to me that it
>>>> might make sense to take this opportunity to improve other visual
>>>> defaults.
>>>>
>>>> Defaults are important. Obviously for publication graphs you'll want
>>>> to end up tweaking every detail, but (a) not everyone does but we
>>>> still have to read their graphs, and (b) probably only 1% of the plots
>>>> I make are for publication; the rest are quick one-offs that I make
>>>> on-the-fly to help me understand my own data. For such plots it's
>>>> usually not worth spending much/any time tweaking layout details, I
>>>> just want something usable, quickly. And I think there's a fair amount
>>>> of low-hanging improvements possible.
>>>>
>>>> Batching multiple visual changes like this together seems much better
>>>> than spreading them out over multiple releases. It keeps the messaging
>>>> super easy to understand: "matplotlib 2.0 is just like 1.x, your code
>>>> will still work, the only difference is that your plots will look
>>>> better by default". And grouping these changes together makes it
>>>> easier to provide for users who need to revert back to the old
>>>> defaults -- it's easy to provide simple binary choice between "before
>>>> 2.0" versus "after 2.0", harder to keep track of a bunch of different
>>>> changes spread over multiple releases.
>>>>
>>>> Some particular annoyances I often run into and that might be
>>>> candidates for changing:
>>>>
>>>> - The default method of choosing axis limits is IME really, really
>>>> annoying, because of the way it tries to find "round number"
>>>> boundaries. It's a clever idea, but in practice I've almost never seen
>>>> this pick axis limits that are particularly meaningful for my data,
>>>> and frequently it picks particularly bad ones. For example, suppose
>>>> you want to plot the spectrum of a signal; because of FFT's preference
>>>> for power-of-two sizes works it's natural to end up with samples
>>>> ranging from 0 to 255. If you plot this, matplotlib will give you an
>>>> xlim of (0, 300), which looks pretty ridiculous. But even worse is the
>>>> way this method of choosing xlims can actually obscure data -- if the
>>>> extreme values in your data set happen to fall exactly on a "round
>>>> number", then this will be used as the axis limits, and you'll end up
>>>> with data plotted directly underneath the axis spine. I frequently
>>>> encounter this when making scatter plots of data in the 0-1 range --
>>>> the points located at exactly 0 and 1 are very important to see, but
>>>> are nearly invisible by default. A similar case I ran into recently
>>>> was when plotting autocorrelation functions for different signals. For
>>>> reference I wanted to include the theoretically ideal ACF for white
>>>> noise, which looks like this:
>>>>     plt.plot(np.arange(1000), [1] + [0] * 999)
>>>> Good luck reading that plot!
>>>>
>>>> R's default rule for deciding axis limits is very simple: extend the
>>>> data range by 4% on each side; those are your limits. IME this rule --
>>>> while obviously not perfect -- always produces something readable and
>>>> unobjectionable.
>>>>
>>>> - Axis tickmarks should point outwards rather than inwards: There's
>>>> really no advantage to making them point inwards, and pointing inwards
>>>> means they can obscure data. My favorite example of this is plotting a
>>>> histogram with 100 bins -- that's an obvious thing to do, right? Check
>>>> it out:
>>>>   plt.hist(np.random.RandomState(0).uniform(size=100000), bins=100)
>>>> This makes me do a double-take every few months until I remember
>>>> what's going on: "WTF why is the bar on the left showing a *stacked*
>>>> barplot...ohhhhh right those are just the ticks, which happen to be
>>>> exactly the same width as the bar." Very confusing.
>>>>
>>>> Seaborn's built-in themes give you the options of (1) no axis ticks at
>>>> all, just a background grid (by default the white-on-light-grey grid
>>>> as popularized by ggplot2), (2) outwards pointing tickmarks. Either
>>>> option seems like a better default to me!
>>>>
>>>> - Default line colors: The rgbcmyk color cycle for line plots doesn't
>>>> appear to be based on any real theory about visualization -- it's just
>>>> the corners of the RGB color cube, which is a highly perceptually
>>>> non-uniform space. The resulting lines aren't terribly high contrast
>>>> against the default white background, and the different colors have
>>>> varying luminance that makes some lines "pop out" more than others.
>>>>
>>>> Seaborn's default is to use a nice isoluminant variant on matplotlib's
>>>> default:
>>>>
>>>> http://web.stanford.edu/~mwaskom/software/seaborn/tutorial/aesthetics.html
>>>> ggplot2 uses isoluminant colors with maximally-separated hues, which
>>>> also works well. E.g.:
>>>>
>>>> http://www.cookbook-r.com/Graphs/Colors_%28ggplot2%29/ggplot2_scale_hue_colors_l45.png
>>>>
>>>> - Line thickness: basically every time I make a line plot I wish the
>>>> lines were thicker. This is another thing that seaborn simply changes
>>>> unconditionally.
>>>>
>>>> In general I guess we could do a lot worse than to simply adopt
>>>> seaborn's defaults as the matplotlib defaults :-) Their full list of
>>>> overrides can be seen here:
>>>>    https://github.com/mwaskom/seaborn/blob/master/seaborn/rcmod.py#L135
>>>>    https://github.com/mwaskom/seaborn/blob/master/seaborn/rcmod.py#L301
>>>>
>>>> - Dash styles: a common recommendation for line plots is to
>>>> simultaneously vary both the color and the dash style of your lines,
>>>> because redundant cues are good and dash styles are more robust than
>>>> color in the face of greyscale printing etc. But every time I try to
>>>> follow this advice I find myself having to define new dashes from
>>>> scratch, because matplotlib's default dash styles ("-", "--", "-.",
>>>> ":") have wildly varying weights; in particular I often find it hard
>>>> to even see the dots in the ":" and "-." styles. Here's someone with a
>>>> similar complaint:
>>>>
>>>> http://philbull.wordpress.com/2012/03/14/custom-dashdot-line-styles-in-matplotlib/
>>>>
>>>> Just as very rough numbers, something along the lines of "--" = [7,
>>>> 4], "-." = [7, 4, 3, 4], ":" = [2, 1.5] looks much better to me.
>>>>
>>>> It might also make sense to consider baking the advice I mentioned
>>>> above into matplotlib directly, and having a non-trivial dash cycle
>>>> enabled by default. (So the first line plotted uses "-", second uses
>>>> "--" or similar, etc.) This would also have the advantage that if we
>>>> make the length of the color cycle and the dash cycle relatively
>>>> prime, then we'll dramatically increase the number of lines that can
>>>> be plotted on the same graph with distinct appearances. (I often run
>>>> into the annoying situation where I throw up a quick-and-dirty plot,
>>>> maybe with something like pandas's dataframe.plot(), and then discover
>>>> that I have multiple indistinguishable lines.)
>>>>
>>>> Obviously one could quibble with my specific proposals here, but does
>>>> in general seem like a useful thing to do?
>>>>
>>>> -n
>>>>
>>>> --
>>>> Nathaniel J. Smith
>>>> Postdoctoral researcher - Informatics - University of Edinburgh
>>>> http://vorpus.org
>>>>
>>>>
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