James Hague [writes]:

>Though my fascination with Forth is long behind me, I still tend toward minimalist programming, but not in the same, extreme, way. I've adopted a more modern approach to minimalism:

>Use the highest-level language that's a viable option.

>Lean on the built-in features that do the most work.

>Write as little code as possible.

I think this is good advance, but I would add one more point about having as few third party dependencies as possible that tries to balance the last point to write as little code as possible.

[writes]: http://prog21.dadgum.com/223.html "The New Minimalism"

[Colorgorical] is an alternative to [ColorBrewer] with a few different options for creating color tables. For example, you can add a couple specific colors that should be in the color table and let Colorgorical figure out the others which maximizes the perceptual difference between the colors. Colorgorical seems particularly well suited to generating qualitative color tables, e.g., to find sufficiently different colors for each line in a plot.

via [FlowingData].

[Colorgorical]: http://vrl.cs.brown.edu/color "Colorgorical"
[ColorBrewer]: http://colorbrewer2.org "ColorBrewer"
[FlowingData]: http://flowingdata.com/2016/08/22/colorgorical-generates-color-schemes-for-you/ "Colorgorical generates color schemes for you"

Motivated by the below chart of the age distribution of Olympic athletes, Junk Charts presents several techniques to [visualize multiple distributions]:

Age distribution of Olympic athletes

Candidates include the traditional boxplots used by statisticians as well variations and a stack of histograms. I think violin plots, suggested by a commenter, are a nice compromise showing the full distribution.

[visualize multiple distributions]: http://junkcharts.typepad.com/junk_charts/2016/08/various-ways-of-showing-distributions.html "Various ways of showing distributions"

It is often useful to display a progress bar showing the state of a task. `MG_Progress` can easily be used to display a progress bar, percent completion, and estimated time to completion. As a simple example, let's pretend to load 100 items (while actually just waiting a bit):

foreach i, mg_progress(indgen(100), title='Loading') do wait, 0.1

The above line produces the following output:

Code for [mg_progress__define] is on GitHub (you will need [mg_statusline] also). See the code docs for the many other options that can be used with `MG_Progress` like dealing with a list of items that don't all take equal time and customizing the display.

[mg_progress__define]: https://github.com/mgalloy/mglib/blob/master/src/cmdline_tools/mg_progress__define.pro "mglib/mg_progress__define.pro"

[mg_statusline]: https://github.com/mgalloy/mglib/blob/master/src/cmdline_tools/mg_statusline.pro "mglib/mg_statusline.pro"

[John Nelson] produced this beautiful map of how the boundaries of US droughts have changed over the last five years with data from the [US Drought Monitor]:

Link via [FlowingData].

[John Nelson]: https://twitter.com/John_M_Nelson "@John_M_Nelson"
[US Drought Monitor]: http://droughtmonitor.unl.edu "United States Drought Monitor"
[FlowingData]: http://flowingdata.com/2016/07/07/moving-drought-boundaries/ "Moving drought boundaries"

[Part 2] (of what promises to be a four part series) of the great comparison of Google Maps and Apple Maps by Justin O'Beirne. See [part 1] before starting with part 2.

[Part 2]: http://www.justinobeirne.com/essay/cartography-comparison-2 "Cartography Comparison: Google Maps & Apple Maps"

[part 1]: http://michaelgalloy.com/2016/05/12/google-maps-missing-cities.html "Google Maps' missing cities"

This is an example of using a [clever color key] that doubles as a histogram showing the distribution of the corresponding areas.

By the way, this post is from a great [series] about small ways to make better visualizations.

[clever color key]: http://www.visualisingdata.com/2016/04/little-visualisation-design-part-14/ "Little of Visualization of Design: Part 14"

[series]: http://www.visualisingdata.com/2016/03/little-visualisation-design/ "All the 'Little of Visualisation of Design'"

Great post examining some of the reasons why the [FFT algorithm] is so fast compared to a naive implementation:

> The goal of this post is to dive into the Cooley-Tukey FFT algorithm, explaining the symmetries that lead to it, and to show some straightforward Python implementations putting the theory into practice. My hope is that this exploration will give data scientists like myself a more complete picture of what's going on in the background of the algorithms we use.

[FFT algorithm]: http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/ "Understanding the FFT Algorithm"

A nice list of [resources] for doing remote sensing in Python, especially if you already know IDL.

[resources]: http://blog.rtwilson.com/resources-for-learning-python-for-remote-sensing-or-switching-from-idl/ "Resources for learning Python for Remote Sensing – or switching from IDL"

Nice example of [why rainbow color tables can be misleading]:

> Regular readers will be aware of the #endrainbow campaign to reduce the use of rainbow colour palettes in scientific figures. At the recent EGU conference, I gave a talk on ‘making better figures’, which included an example of a published conclusion which was incorrect due to the use of a rainbow colour scheme.

via [@asoconnor] via [@rsimmon]

[why rainbow color tables can be misleading]: http://www.climate-lab-book.ac.uk/2016/why-rainbow-colour-scales-can-be-misleading/ "Why rainbow colour scales can be misleading"
[@rsimmon]: https://twitter.com/rsimmon/status/725683215587926016 "Rob Simmon tweet"
[@asoconnor]: https://twitter.com/asoconnor/status/725708638497677312 "Amanda O'Connor tweet"

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