IDL
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| IDL | |
|---|---|
| Name | IDL |
| Developer | Research Systems, Inc.; Exelis Visual Information Solutions; Harris Corporation; L3Harris Technologies |
| Released | 1977 |
| Latest release | 8.8 (example) |
| Programming language | C++; FORTRAN |
| Operating system | Microsoft Windows; macOS; Linux (kernel); Solaris |
| License | Proprietary |
IDL
IDL is a proprietary array-oriented programming language and interactive environment originally developed for scientific visualization, image analysis, and data processing. It provides tools for numerical computation, graphical display, and interactive exploration used in environments that include remote sensing, astronomy, medical imaging, and atmospheric science. The environment has been employed by researchers at institutions such as NASA, European Space Agency, NOAA, Harvard University, and Stanford University.
Definition and Overview
IDL is an interpreted language and integrated development environment produced for numerical analysis, visualization, and application development. Users exploit IDL for manipulating arrays, producing publication-quality graphics, and implementing algorithms for datasets from missions like Hubble Space Telescope, Landsat, Nimbus-7, and Terra (satellite). The platform integrates with toolchains used at organizations such as Jet Propulsion Laboratory, MIT, Caltech, and Smithsonian Institution.
History and Development
IDL originated in 1977 at Research Systems, Inc. (RSI) and evolved through corporate transitions involving ITT Visual Information Solutions, ITT Corporation, and acquisitions by Exelis, later absorbed into Harris Corporation and then L3Harris Technologies. It grew in parallel with visualization needs at research centers including Los Alamos National Laboratory, Lawrence Livermore National Laboratory, Argonne National Laboratory, Brookhaven National Laboratory, and Jet Propulsion Laboratory. IDL’s development tracks advances in hardware from DEC VAX systems to UNIX workstations from Sun Microsystems and Silicon Graphics, and later to Microsoft Windows and Linux (kernel). Notable milestones include adoption in missions like Voyager program, Cassini–Huygens, and Mars Reconnaissance Orbiter for data processing pipelines.
Variants and Standards
Vendor releases and forks of the environment produced variant packages, extensions, and language bindings tailored for domains at institutions such as NOAA National Centers for Environmental Information, European Centre for Medium-Range Weather Forecasts, US Geological Survey, National Oceanic and Atmospheric Administration, and National Aeronautics and Space Administration. Add-on libraries and standards-compliant toolboxes offer functionality interoperable with formats and protocols from OGC standards implementations, HDF5, NetCDF, FITS (file format), and middleware stacks used at CERN and European Southern Observatory. Third-party projects developed bridges to languages and systems at MathWorks (for MATLAB interoperability), Python (programming language) ecosystems used at NumPy, SciPy, and Matplotlib, and platforms like IDL-to-Python wrappers maintained by community groups at GitHub.
Syntax and Language Features
The language emphasizes vectorized operations, array slicing, and built-in visualization primitives used in workflows at Harvard–Smithsonian Center for Astrophysics, Space Telescope Science Institute, Max Planck Institute for Astronomy, and California Institute of Technology. IDL offers structured programming constructs similar to those in FORTRAN and control flow patterns used in codebases at Lawrence Berkeley National Laboratory and Sandia National Laboratories. Key features include graphics windows with event handling used by instrument teams from European Space Agency missions, widget toolkits for GUIs built by research groups at University of Cambridge, and procedures/modules for image processing applied by groups at Johns Hopkins University and Massachusetts General Hospital.
Implementations and Tooling
Official implementations of the environment are distributed by corporate entities such as Research Systems, Inc. and successors; third-party tools and integration kits have been produced by vendors and laboratories including ITTVIS, ENVI (Environment for Visualizing Images), and commercial partners supplying toolboxes for PCI Geomatics, ERDAS Imagine, and Hexagon AB. Community tooling for conversion, interfacing, and batch processing is developed by teams at GitHub, SourceForge, and academic consortia at European Centre for Medium-Range Weather Forecasts and National Center for Atmospheric Research. Build and deployment automation in high-performance computing centers at Oak Ridge National Laboratory, Argonne National Laboratory, and NERSC leverage wrappers and compiled modules to integrate IDL with cluster schedulers from SLURM and resource managers used at XSEDE.
Use Cases and Applications
IDL has been widely used for remote sensing analysis for missions like MODIS, ASTER, SPOT (satellite), and Sentinel-2, for astronomical data reduction in projects at Keck Observatory, Palomar Observatory, and European Southern Observatory, and for medical imaging workflows at Mayo Clinic, Cleveland Clinic, and Johns Hopkins Hospital. The language supports geospatial analysis tasks demanded by agencies like USGS and applied research in climate science at NOAA and NASA Goddard Space Flight Center. Image processing pipelines built in IDL were instrumental in data products from Mars Reconnaissance Orbiter and calibration routines for instruments on Cassini–Huygens and Voyager program payloads.
Criticisms and Limitations
Critics in communities at Stack Overflow and academic departments including University of Oxford and Imperial College London cite proprietary licensing from vendors such as Exelis Visual Information Solutions and L3Harris Technologies as barriers to reproducibility compared with open-source ecosystems like Python (programming language), R (programming language), and Julia (programming language). Performance and integration concerns arise in large-scale computing centers at Lawrence Livermore National Laboratory and NERSC where alternatives such as MPI-based C++ pipelines and TensorFlow or PyTorch workflows are favored. The ecosystem’s reliance on vendor toolboxes contrasts with toolchains adopted at GitHub-hosted projects and consortia at Open Source Geospatial Foundation.