IDL (programming language)
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| IDL (programming language) | |
|---|---|
| Name | IDL |
| Paradigm | Array programming, Procedural, Object-oriented |
| Designer | Research Systems, Inc. |
| First appeared | 1970s |
| Typing | Dynamic |
| Influenced by | APL, Fortran, BASIC |
| Influenced | MATLAB, Python (scientific), R (array use) |
| License | Proprietary (original); various commercial |
IDL (programming language) IDL is a proprietary array-oriented programming language and interactive environment widely used for data analysis, visualization, and numerical computation. Developed for scientific and engineering applications, it provides high-level array operations, visualization primitives, and an extensible library ecosystem for domains such as remote sensing, astronomy, and medical imaging. Its toolchain and user community intersect with organizations and projects across industry and academia.
Overview
IDL originated as a high-level interactive environment tailored to numeric arrays and image processing, offering matrix manipulation, plotting, and user interface widgets. It competes and coexists with languages and systems like Fortran, APL, MATLAB, Python (programming language), R (programming language), and environments such as IDL-adjacent toolkits used at institutions like NASA, European Space Agency, NOAA, and Jet Propulsion Laboratory. The language's ecosystem includes proprietary platforms and third-party extensions from companies and research groups, often used alongside software from ESRI, IBM, Microsoft, and Intel Corporation.
History and Development
Developed in the 1970s and 1980s by Research Systems, Inc., IDL evolved through commercial stewardship and acquisitions involving organizations such as ITT Corporation, ITT Visual Information Solutions, and later Exelis Visual Information Solutions. It saw adoption in programs and projects at agencies including NASA Goddard Space Flight Center, European Southern Observatory, and USGS. Academic groups at institutions like Massachusetts Institute of Technology, Stanford University, University of California, Berkeley, and Harvard University used it for image analysis, leading to integrations with toolchains from National Center for Atmospheric Research, CERN, and Max Planck Society. Corporate customers ranged from Lockheed Martin to Raytheon and collaborations with vendors such as Hewlett-Packard, Sun Microsystems, and Dell Technologies influenced platform support.
Language Features and Syntax
The language emphasizes vectorized array operations, broadcasting, and numeric routines influenced by APL and Fortran. IDL provides control constructs, procedural routines, and object-oriented features comparable to constructs found in C++, Java (programming language), and Python (programming language), while retaining unique syntax for array slicing, indexing, and memory view semantics. Built-in data types and structures support complex numbers, structured arrays, and records aligned with formats used by NetCDF and HDF5 standards developed by Unidata and The HDF Group. Interoperability features enable calling external libraries created with C (programming language), Fortran, and interfaces for frameworks such as OpenGL and OpenCL used in visualization and GPU acceleration.
Standard Library and Extensions
IDL's standard library includes image processing, Fourier transforms, statistical routines, and plotting primitives that parallel functionality available in libraries like FFTW, BLAS, and LAPACK used by National Institute of Standards and Technology. Extension ecosystems and commercial toolkits provide domain-specific functionality for remote sensing, spectroscopy, and medical imaging, produced by vendors including Exelis, ITT, and third-party groups associated with NOAA, NASA, and European Space Agency. Integration modules exist for formats and services from MODIS, Landsat, and Hubble Space Telescope data pipelines as managed by organizations like Jet Propulsion Laboratory and Space Telescope Science Institute.
Implementations and Platforms
Canonical implementations have been distributed for operating systems such as Microsoft Windows, macOS, and various distributions of Linux (operating system), with runtime builds supporting architectures promoted by Intel Corporation and AMD. IDL runtime and development environments have been packaged for deployment on enterprise servers and clusters used by National Oceanic and Atmospheric Administration and research facilities at Lawrence Berkeley National Laboratory and Los Alamos National Laboratory. Integration with development tools and version control systems from GitHub, GitLab, and Apache Subversion facilitated collaborative projects in institutions such as California Institute of Technology and Princeton University.
Applications and Use Cases
IDL is widely used in satellite data analysis, astronomical image reduction, geophysical modeling, and medical image processing in projects undertaken by NASA, ESA, NOAA, USGS, and observatories like Keck Observatory and Very Large Telescope. Scientific pipelines for missions such as Voyager, Cassini–Huygens, Hubble Space Telescope, and remote sensing programs including Landsat and MODIS have used IDL-based tools. Research groups at MIT Lincoln Laboratory, Johns Hopkins University, and Columbia University employed it for radar signal processing, spectroscopy, and time-series analysis, often interfacing with datasets from NOAA National Centers for Environmental Information and repositories managed by NASA EarthData.
Criticisms and Limitations
Critics cite the proprietary licensing model and cost barriers compared with open-source alternatives like Python (programming language), R (programming language), and toolchains built around NumPy and SciPy. Concerns about ecosystem openness, extensibility relative to community-led projects at GitHub, and integration with modern software engineering practices championed by organizations such as Apache Software Foundation and Linux Foundation have been raised. Performance limitations on very large-scale parallel clusters operated by Argonne National Laboratory and Oak Ridge National Laboratory motivated migrations to high-performance computing frameworks developed at NVIDIA, AMD, and in projects like MPI and CUDA.