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

For a served (floating) license:

  • 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.

Awesome Python:

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

Worth checking out when looking for an existing solution.

National Geographic collects the best maps of 2016:

It’s been a good year for map lovers. Whether you’re into old maps, new maps, or new ways of interacting with old maps, there was much to cheer about in 2016.

via kottke.org

The NSF has opened up voting for the People’s Choice for visualizations in the Photo, Illustration, Poster/Graphic, Interactive, and Video categories.

Voting closes Sunday December 4 at 11:59 p.m. PST.

Upgrading from XQuartz from 2.7.9 to 2.7.11 on macOS Sierra broke IDL widgets for me:

~$ idl
IDL Version 8.5.1, Mac OS X (darwin x86_64 m64).
(c) 2015, Exelis Visual Information Solutions, Inc., a subsidiary of Harris
Corporation.

IDL> xloadct
Error: attempt to add non-widget child "dsm" to parent "idl" which supports
only widgets

The fix that worked for me were the following two commands:

sudo mv /opt/X11/lib/libXt.6.dylib{,.bak}
sudo cp /opt/X11/lib{/flat_namespace,}/libXt.6.dylib

Downgrading to 2.7.9 (but not 2.7.10) also worked for me.

D3 in Depth:

D3 in Depth aims to bridge the gap between introductory tutorials/books and the official documentation.

I have found D3 extremely useful for creating dynamic plots on dashboard style websites for monitoring data pipelines. This looks an excellent resource for learning it.

via FlowingData

I plot a lot of data on daily cycles, where there is no data collected at night. Let’s mock up some sample data with the following simple code:

IDL> x = [findgen(10), findgen(10) + 25, findgen(10) + 50]
IDL> seed = 0L
IDL> y = randomu(seed, 30)
IDL> plot, x, y

Then I get a plot like this:

This plot doesn’t show the nightly breaks in data well. Connecting the last data point collected from a day to the first data point collected the next day emphasizes the trend between these points, which may not be appropriate.

I have been using a fairly simple routine to insert NaNs into the data to break the plot into disconnected sections. For example, modify the above data for plotting with:

IDL> new_y = mg_insert_nan(x, y, [10.0, 35.0], new_x=new_x)
IDL> plot, new_x, new_y

The new plot shows the gaps between the “days” in the data:

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