Travis Oliphant, creator of NumPy the array package for Python, wrote a analog to the Zen of Python for NumPy:

Strided is better than scattered
Contiguous is better than strided
Descriptive is better than imperative (use data-types)
Array-oriented is often better than object-oriented
Broadcasting is a great idea — use where possible
Vectorized is better than an explicit loop
Unless it’s complicated — then use numexpr, weave, or Cython
Think in higher dimensions

I tried something for IDL last year.

I will be posting more about the Great American Eclipse of 2017 this summer. To start off with, NASA has made some great maps of where the total eclipse will be visible from:

On August 21, 2017, the moon will pass between Earth and the sun in a total solar eclipse that will be visible on a path from Oregon to South Carolina across the continental United States. This path of totality will occur in a little over 90 minutes, while observers on the ground will see the eclipse for about two and a half minutes. Standing at the edge of the moon’s shadow, or umbra, the difference between seeing a total eclipse and a partial eclipse comes down to elevation – mountains and valleys both on Earth and on the moon – which affect where the shadow lands. In this visualization, data from NASA’s Lunar Reconnaissance Orbiter account for the moon’s terrain that creates a jagged edge on its shadow. This data is then combined with elevation data on Earth as well as information on the sun angle to create the most accurate map of the eclipse path to date. Watch the video to learn more.

via kottke

Here are some odd, but totally legal, IDL statements. I would suggest staying away from all of them and use more conventional syntax.

Here’s one that looks like a string, but is really specifying an octal value:

IDL> x = "12
IDL> help, x
X               INT       =       10


Back when octal values were much more important than now, I suppose it made some sense to have special syntax for entering them. In modern times, I would suggest x = '12'o.

This also means that if you are specifying a string that begins with digits 0-7 with double quotes, you can generate a bewildering syntax error:

IDL> y = "12 monkeys"
^
% Syntax error.


I recommend using single quotes for all strings, i.e., y = '12 monkeys'.

Next up is a convenience for the truly lazy:

IDL> s = 'some string


You don’t have to put the trailing single or double quote on a string if it is the last character on the line. This will probably make your text editor’s syntax highlighting confused. One character is not too much to type for some clarity.

Finally, I just saw this one last week:

IDL> for i = 0, 4 do begin y = i & print, i
0
1
2
3
4


There are quite a few problems with this:

• There is a begin with no matching end!
• There is not a & after the begin even though you would normally have to start a new line there.
• I would recommend against using &. It can be useful on the command line (it makes it easier to up arrow to a previous set of commands), but don’t do it in a file!

This is the standard syntax for that line (if you really need to put it all on a single line):

IDL> for i = 0, 4 do begin & y = i & print, i & endfor


I might count using parentheses for indexing arrays as a syntax oddity as well, but there are so many IDL programmers still doing it that it counts as commonplace. I still recommend against it.

Excellent rundown of all the horrible rules that organizations impose on your passwords:

• They don’t work.
• They heavily penalize your ideal audience, people that use real random password generators. Hey guess what, that password randomly didn’t have a number or symbol in it. I just double checked my math textbook, and yep, it’s possible. I’m pretty sure.
• They frustrate average users, who then become uncooperative and use “creative” workarounds that make their passwords less secure.
• They are often wrong, in the sense that the rules chosen are grossly incomplete and/or insane, per the many shaming links I’ve shared above.
• Seriously, for the love of God, stop with this arbitrary password rule nonsense already. If you won’t take my word for it, read this 2016 NIST password rules recommendation. It’s right there, “no composition rules”. However, I do see one error, it should have said “no bullshit composition rules”.

My personal pet peeve is forced expiration for no reason. NIST is developing guidelines.

The Mathematics Genealogy Project is an amazing effort to record basic information about every mathematician in the world. We can create a family tree for any mathematician. Here is my tree:

For a description of how to create the graph of another mathematician’s genealogy, see Dana C. Ernst’s article.

Licensing has been the most controversial change in IDL 8.6. The release notes say:

IDL licensing is now managed through a 3rd-party solution from Flexera software. You obtain the license through a portal hosted by Flexera, then you can choose to activate the license on a license server or on an individual node-locked machine.

This seems like a more convenient solution, but there are a lot of other changes in the licensing for IDL 8.6.

Limits have been placed on the number of instances of IDL running on a machine. For a local (node-locked) license, the number of IDL instances is limited by:

• IDL command line or Workbench – 4
• Execute compiled save code – 4
• IDL Bridge Processes – 16
• IDL Task Engine – 1

• IDL command line or Workbench – 1
• Execute compiled .sav code – 1
• IDL Bridge Processes – 8
• IDL Task Engine – 1

The flexible single user license which allowed people to use IDL at work and at home (or lab) with a single license has also been eliminated in IDL 8.6.

Furthermore, the IDL 8.6 Virtual Machine cannot currently be downloaded from the Harris site. In the past, this has allowed IDL developers to release applications to users who did not need the full IDL distribution, or an IDL license, to run the application.

Complaints resulted in a proposed change for the next release of IDL. IDL Project Lead, Chris Torrence wrote on Feb 1:

Starting with IDL 8.6.1 (hopefully mid-April), we will make the following changes:

• An IDL user will be able to run an unlimited number of sessions on their machine. In IDL 8.6 the IDL license was tied to the MAC address + install location + process ID, so each process ID would consume a separate license. In IDL 8.6.1, the IDL license will be tied to the MAC address + install location + user id, so multiple process ID’s will consume just a single IDL license.
• This change will apply to the IDL command line, the IDL Workbench, and the Python bridge, on all platforms.
• This change will not apply to ENVI or other Harris Geospatial products.
• There is no policy change for “flexible single user” (other than allowing multiple IDL sessions on one machine). If you need to use IDL on two machines, you should contact Tech Support or your sales rep for options.
• IDL Virtual Machine will remain unchanged from pre-IDL 8.6 – we just need to tie up some lose ends and release it.

IDL 8.6 also has an automatic check for updates (you can turn off with the “IDL_UPDATE_CHECK” preference) that will tell you when an update is available.

Harris Geospatial released IDL 8.6 at some point in the last couple months—it’s hard to pick an actual day. I’ve heard the release was rolled out to customers in batches since then and it was finally my turn last Friday!

The release notes list the new features. I am very interested in checking out the IDL Task Engine; I think it will be extremely useful. There are quite a few small features and changes that I think I will regularly use.

I will have more details in the coming weeks as I look at individual new features one by one.

I have been doing some reading about machine learning recently, using Python as an implementation language. I lot of the routines used are fairly easy to implement in IDL, so I have started filling out my library with IDL versions.

I have written a scatter plot matrix routine that takes a collection of vectors and makes all the scatter plots between pairs of them. For example, here’s a scatter plot matrix produced by the routine for the classic iris dataset:

If you want to use the routine, it’s probably easiest to clone my entire library.

FlowingData rounds up his list of best visualization projects of 2016:

Visualization continues its merging into the everyday — less standalone and more of a medium that blends with words. I think this is partially because of a concentration on mobile. There’s simply less visual space on a phone than there is a giant computer screen, so the visualization is stripped or split up into smaller pieces that are more easily digested while scrolling.

For example, the Rhythm of Food shows the popular food by time of year:

And if that is not enough for you, here’s a roundup of 2016 visualization roundups.

A curated list of awesome Python frameworks, libraries, software and resources.

Worth checking out when looking for an existing solution.

• GPULib

GPULib enables IDL developers to access the high-performance capabilities of modern NVIDIA graphics cards without knowledge of CUDA programming.

TaskDL is a task-farming solution for IDL designed for problems with loosely-coupled, parallel applications where no communication between nodes of a cluster is required.

mpiDL

mpiDL is a library of IDL bindings for Message Passing Interface (MPI) used for tightly-coupled parallel applications.

Remote Data Toolkit

The Remote Data Toolkit is a library of IDL routines allowing for easy access to various scientific data in formats such as OPeNDAP, HDF 5, and netCDF.

• Modern IDL

Modern IDL offers IDL programmers one place to look, for beginners and advanced users alike. This book also contains: a thorough tutorial on the core topics of IDL; a comprehensive introduction to the object graphics system; common problems and gotchas with many examples; advanced topics not normally found are discussed throughout the book: regular expressions, object graphics, advanced widget programming, performance, object-oriented programming, etc.

• IDLdoc

IDLdoc is an open source utility for generating documentation from IDL source code and specially formatted comments.

mgunit

mgunit is an open source unit testing framework for IDL.

rIDL

rIDL is an open source IDL command line replacement.

mglib

mglib is an open source library of IDL routines in areas of visualization, application development, command line utilities, analysis, data access, etc.