Category "IDL"


IDL 8.7.1 adds the IDL Package Manager (IPM), a package manager for IDL libraries. You can install libraries from the Internet with a single command in IDL, even installing dependencies for the library if needed.

Continue reading “IDL Package Manager.”

IDL 8.7.1 was released today. Check the What’s New on the Docs Center for a comprehensive list of the new features.

Along with some performance improvements and library updates, there are two big new features:

  • an IDL package manager
  • machine learning classes (including a feed forward neural network class)

I will publish an article about the package manager shortly; I made IDLdoc, mgunit, and my personal library available through the package manager. I think this is now the best way to distribute IDL packages.

I am also excited to explore the machine learning classes. I will write more as soon we get 8.7.1 installed on our machines.

With these two new features, this is a powerful “bug fix” release.

When working with data files at my day job, I often come across directories containing a large number of files of several distinct types. It would be useful to produce a listing of the files clustered into these types. I wrote cls (Clustered ls) to find patterns in filenames for display.

Continue reading “Clustered ls.”

Circular law states the eigenvalues of a matrix with random entries of mean 0 and variance 1/n are approximately uniformly distributed in the unit disk of the complex plane. To see this, create a random matrix:

n = 1000
x = randomu(seed, n, n) - 0.5
x *= sqrt(12.0 / n)

Find the eigenvalues:

eigenvalues = la_eigenproblem(x, eigenvectors=eigenvectors)

Plot them:

plot, real_part(eigenvalues), imaginary(eigenvalues), $
      psym=3, $
      xstyle=1, xrange=[-1.5, 1.5], $
      ystyle=1, yrange=[-1.5, 1.5]

This gives a plot like below:

Via the excellent John D. Cook blog. I recommend reading his site if you are interested in a combination of mathematics and Python.

IDLdoc 3.6.2 has been released! New features include:

  • Bug fix for image directive for image files in other directories specified with a relative path (fix by Dave Gellman).

  • Only copying MathJax for LaTeX-style equations if not already present.

  • Fixed crash when invalid format/markup was specified on the docformat line of a .pro file.

mgunit 1.6 has been released! New features include:

  • Fix for bug when no filename with jUnit output format

  • Recursively search directories below test suite home directory for *_ut.pro and *_uts.pro files

  • Fixed for bug in mguttestsuite_define::addTestingFolder that did not add absolute paths correctly

  • Add superclasses of test classes recursively

You can download a distribution with a .sav file and documentation, or just access the repo as needed.

IDL 8.7 was released recently. The listed features are:

  • ROUTINE_DIR function that returns the directory for the file containing the calling routine
  • asynchronous job classes
  • a few miscellaneous other updates

The asynchronous job classes look interesting:

The IDLAsync classes allow you to specify units of work to execute asynchronously outside the main IDL session. To do this, create an IDLAsyncQueue and one or more IDLAsyncJob objects that encapsulate the work to be performed. As jobs are added to the queue, they will be executed at some point in the future as resources are available. When a job is complete, you can retrieve its results for further use. You can also construct an IDLAsyncJoin object and pass it into the jobs on creation. If you do this, you can wait on the join object for all of the jobs it observes to be finished before continuing.

Check the release notes for more details.

The IDL Usenet newsgroup has moved to a Google Group:

This Google Group is a continuation of the Usenet group comp.lang.idl-pvwave, but allows for better spam filtering. It is for discussion of the Interactive Data Language (IDL), developed by Harris Geospatial Corporation. Questions about ENVI, a geospatial analytics software written in IDL are welcome. Discussion of the similar PV-WAVE language is also allowed.

The big question now is how to save the posts from the old newsgroup.

I presented a poster at a Space Weather workshop at the Lorentz Center in Leiden, Netherlands last week:

Real-time automated detection of coronal mass ejections using ground-based coronagraph instruments

Coronal mass ejections (CMEs) are dynamic events that eject magnetized plasma from the Sun’s corona into interplanetary space. CMEs are a major driver of solar energetic particle (SEP) events and geomagnetic storms. SEP events and geomagnetic storms pose hazards to astronauts, satellites, communication systems, and power grids. Understanding CME formation and predicting their impacts at Earth are primary goals of the National Space Weather program. St. Cyr et al. (2017) reported on the use of near real-time white light observations of the low corona from the COSMO K-Coronagraph (K- Cor) to provide an early warning of possible SEP events driven by fast CMEs. Following that work, one of us (Thompson) created a new CME detection algorithm adapted from the Solar Eruptive Event Detection System (SEEDS) code for use with K-Cor observations from the Mauna Loa Solar Observatory (MLSO) in Hawaii. We develop performance metrics and report on the success of the algorithm to detect CMEs in the 2017 K-Cor observations. Measures of success include the ability of the algorithm to detect an event and the amount of time between the event onset and its detection. The algorithm successfully detected 20 of the 35 CMEs identified between 1 Jan and 31 August, 2017 in the K-Cor data. There were 10 false positive events during this time period. The threshold for CME detection is discussed as a function of CME visibility, instrument background, and sky noise. The code has been modified to run in an automated mode and is in the process of being integrated into the real-time data processing pipeline at Mauna Loa. We report on current status, real-time alerts, and future upgrades.

Here’s a tutorial of how to make an animation of the moon’s shadow with GOES imagery during The Great American Eclipse of 2017:

Here is one of the coolest examples that I have created using IDL in a while. For this blog post, I’m going to walk through how I created an animation of the Moon’s shadow during the Great American Total Solar Eclipse using several different technologies for accessing, downloading, and visualizing the data.

The video is on Harris Geospatial Solutions’ Facebook page.

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