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IDL (programming language)
IDL (programming language)
IDL (programming language)
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IDL (Interactive Data Language)
Paradigmvector-oriented programming
Designed byDavid Stern
DeveloperDavid Stern & ITT Visual Information Solutions (ITT VIS)
First appeared1977
Stable release
IDL 9.2 / July, 2025
Typing disciplineDynamic
Websitehttps://www.nv5geospatialsoftware.com/Products/IDL
Major implementations
IDL, GNU Data Language, Fawlty Language

IDL, short for Interactive Data Language, is a programming language used for data analysis. It is popular in particular areas of science, such as astronomy, atmospheric physics and medical imaging.[citation needed] IDL shares a common syntax with PV-Wave and originated from the same codebase, though the languages have subsequently diverged in detail. There are also free or costless implementations, such as GNU Data Language (GDL) and Fawlty Language (FL).

Overview

[edit]

IDL is vectorized, numerical, and interactive, and is commonly used for interactive processing of large amounts of data (including image processing). The syntax includes many constructs from Fortran and some from C.

IDL originated from early VMS Fortran, and its syntax still shows its heritage: 

 x = findgen(100)/10
 y = sin(x)/x
 plot,x,y

The findgen function in the above example returns a one-dimensional array of floating point numbers, with values equal to a series of integers starting at 0.

Note that the operation in the second line applies in a vectorized manner to the whole 100-element array created in the first line, analogous to the way general-purpose array programming languages (such as APL, J or K) would do it. This example contains a division by zero; IDL will report an arithmetic overflow, and store a NaN value in the corresponding element of the y array (the first one), but the other array elements will be finite. The NaN is excluded from the visualization generated by the plot command.

As with most other array programming languages, IDL is very fast at doing vector operations (sometimes as fast as a well-coded custom loop in Fortran or C) but quite slow if elements need processing individually. Hence part of the art of using IDL (or any other array programming language, for that matter) for numerically heavy computations is to make use of the built-in vector operations.

History

[edit]

The predecessor versions of IDL were developed in the 1970s at the Laboratory for Atmospheric and Space Physics (LASP) at the University of Colorado at Boulder. At LASP, David Stern was involved in efforts to allow scientists to test hypotheses without employing programmers to write or modify individual applications. The first program in the evolutionary chain to IDL that Stern developed was named Rufus; it was a simple vector-oriented calculator that ran on the PDP-12. It accepted two-letter codes that specified an arithmetic operation, the input registers to serve as operands, and the destination register. A version of Rufus developed on the PDP-8 was the Mars Mariner Spectrum Editor (MMED). MMED was used by LASP scientists to interpret data from Mariner 7 and Mariner 9. Later, Stern wrote a program named SOL, which also ran on the PDP-8. Unlike its predecessors, it was a true programming language with a FORTRAN-like syntax. SOL was an array-oriented language with some primitive graphics capabilities.[1]

Stern left LASP to found Research Systems Inc. (RSI) in 1977. The first RSI product was IDL for the PDP-11.[1] In this release, the graphics supported by IDL were primarily Tektronix terminals and raster graphics displays. RSI sold its first IDL licenses to NASA's Goddard Space Flight Center and Ball Aerospace & Technologies Corp. in 1979. Two years later RSI released an initial VAX/VMS version of IDL, which was written in VAX MACRO and FORTRAN. It took advantage of the VAX virtual memory and 32-bit address space.[1] The National Center for Atmospheric Research (NCAR), the University of Michigan, the University of Colorado, and the Naval Research Laboratory started to use IDL with this version.

In 1987 RSI shifted development work of IDL to the Unix environment, which required a complete re-write of the code in C rather than a port of the existing version of VAX IDL. [1] Stern and Ali Bahrami rewrote IDL for Unix on the Sun 3, taking advantage of the re-write to extend and improve the language. Subsequently, IDL was further expanded and ported to several variants of Unix, VMS, Linux, Microsoft Windows (1992), and Mac OS (1994).

Widgets were added to IDL in 1992, providing event-driven programming with graphical user interfaces. In 1997 ION (IDL On the Net), a web server-based system, was commercially released. The first version of ENVI, an application for remote sensing multispectral and hyperspectral image analysis written in IDL, was released in 1994. ENVI was created, developed and owned by Better Solutions Consulting, LLC, until it was purchased from BSC in October 2000 by Eastman Kodak coincident with their purchase of RSI. RSI sold, marketed and supported ENVI under the terms of a license agreement with BSC, LLC from 1994 through October 2000. New object and pointer types, and limited object-oriented programming capabilities, were added to IDL in 1997.

IDL has been applied widely in space science, for example in solar physics. The European Space Agency used IDL to process almost all of the pictures of Halley's Comet taken by the Giotto spacecraft. The team repairing the Hubble Space Telescope used IDL to help them diagnose anomalies in the main mirror. In 1995, astronauts on board a Space Shuttle used IDL loaded on a laptop to study ultraviolet radiation. Currently, amongst other applications, IDL is being used for most of the analysis of the SECCHI part of the STEREO mission at NRL, USA, and at the Rutherford Appleton Laboratory, UK.

RSI became a wholly owned subsidiary of ITT Industries in March 2004. As of 15 May 2006, RSI began doing business as ITT Visual Information Solutions. Effective 31 October 2011, as a result of restructuring, that company became Exelis Visual Information Solutions. In 2015, IDL was purchased by Harris Geospatial Solutions which later became L3Harris Geospatial Solutions. In April 2023, IDL was acquired by NV5 Geospatial.

Features

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As a computer language, IDL:

  • is dynamically typed.
  • has separate namespaces for variables, functions and procedures, but no namespace hierarchy.
  • was originally single threaded but now has many multi-threaded functions and procedures.
  • has all function arguments passed by reference; but see "problems", below.
  • has named parameters called keywords which are passed by reference.
  • provides named parameter inheritance in nested routine calls, by reference or value.
  • does not require variables to be predeclared.
  • provides COMMON block declarations and system variables to share global values among routines.
  • provides a basic form of object-oriented programming, somewhat similar to Smalltalk, along with operator overloading.
  • implements a persistent, global heap of pointer and object variables, using reference counting for garbage collection.
  • compiles to an interpreted, stack-based intermediate p-code (à la Java virtual machine).
  • provides a simple and efficient index slice syntax to extract data from large arrays.
  • provides various integer sizes, as well as single and double precision floating point real and complex numbers.
  • provides composite data types such as character strings, homogeneous-type arrays, lists, hash tables, and simple (non-hierarchical) record structures of mixed data types.

Problems

[edit]

Some of these features, which make IDL very simple to use interactively, also cause difficulties when building large programs. The single namespace is particularly problematic; for example, language updates that include new built-in functions have on occasion invalidated large scientific libraries.[2]

Arrays are passed by reference, and this mechanism is an advertised feature of the language to pass data back out of a subroutine – in contrast, array slices are copied before being passed, so that data modifications do not flow back into array ranges (after the subroutine exits), violating the principle of least surprise.

Many historical irregularities survive from the early heritage of the language, requiring individual workarounds by the programmer. As an example:

  • Array indexing and subroutine entry can both be carried out with exactly the same syntax (parentheses); this ambiguity, coupled with the single namespace for all variables and subroutines, can cause code to stop working when newly defined subroutines or language extensions conflict with local variable names. IDL programmers can avoid many of these problems by using square brackets for array indexing, thereby avoiding conflicts with function names which use parentheses.

The preceding issue can be alleviated using this compiler option: 

COMPILE_OPT STRICTARR

ITT Visual Information Solutions (ITT VIS), the developers of IDL, have taken explicit steps to prevent bytecode compatibility with other environments. Files containing compiled routines use a binary tagged-data-structure format that has not been officially published but has been investigated and documented by users[3] but also contain the following notice as ASCII text embedded within each saved file:

IDL Save/Restore files embody unpublished proprietary information about the IDL program. Reverse engineering of this file is therefore forbidden under the terms of the IDL End User License Agreement (IDL EULA). All IDL users are required to read and agree to the terms of the IDL EULA at the time that they install IDL. Software that reads or writes files in the IDL Save/Restore format must have a license from NV5 Geospatial Solutions, Inc. explicitly granting the right to do so. In this case, the license will be included with the software for your inspection. Please report software that does not have such a license to your account manager or sales representative.

As of August 2023, the statement has not been tested in a court of law. Also, that provision of the IDL EULA has no effect in Australia, as a result of sections 47D and 47H of that country's Copyright Act.

Examples

[edit]

The following graphics were created with IDL (source code included):

See also

[edit]
  • List of numerical-analysis software
  • ENVI – an image processing software package built in IDL
  • IRAF – a free, graphical data reduction environment produced by NOAO
  • MATLAB – a technical computing environment providing similar capabilities to IDL
  • NumPy – an extension for Python that gives it array math capabilities similar to those of IDL
  • Perl Data Language (PDL) – An extension to Perl that gives it array math capabilities similar to those of IDL
  • Scilab - a high-level, numerically oriented programming language designed for Scientific computing and interfaces
  • Solarsoft – library for solar data analysis and spacecraft operation activities written predominately in IDL
  • GDL – GNU Data Language, a free implementation similar to IDL.
  • Fawlty Language – Fawlty Language is an IDL8 (Interactive Data Language) compatible compiler.

References

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

IDL (programming language)

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from Grokipedia
IDL (Interactive Data Language) is a , scientifically oriented programming language designed for , visualization, and application development, particularly in handling complex numerical datasets across various disciplines. It features an array-oriented syntax that facilitates efficient manipulation of multidimensional data, making it suitable for tasks in image processing, signal analysis, and statistical modeling. Developed initially in 1977 by David Stern through Research Systems, Inc. (RSI), IDL was created to support data visualization needs in scientific research, with early adoption by organizations such as NASA Goddard Space Flight Center and Ball Aerospace in 1979. Over the years, ownership transitioned from RSI, acquired by Eastman Kodak in 1994, to ITT Industries in 2000 (renamed ITT Visual Information Solutions); in 2011, ITT spun off its defense businesses including VIS to form Exelis Inc.; Harris Corporation acquired Exelis in May 2015, forming Harris Geospatial Solutions; Harris merged with L3 Technologies in 2019 to form L3Harris Technologies, and in April 2023, NV5 Global, Inc. acquired L3Harris's geospatial software business, including IDL, integrating it into NV5 Geospatial Software. IDL's core strengths include its dynamically typed nature, intuitive command-line interface for interactive use, and extensive built-in libraries for advanced mathematics, machine learning algorithms (such as neural networks and support vector machines), and graphics rendering with OpenGL acceleration. It supports cross-platform development on Windows, macOS, and Linux, and integrates seamlessly with languages like Python (via an IDL-Python bridge), C, and Java, enabling hybrid workflows for enhanced productivity. Commonly applied in astronomy, earth observation, medical imaging, and environmental science, IDL is valued for transforming raw data into publication-ready visualizations in formats like PDF, GIF, and interactive displays.

Introduction

Overview

IDL (Interactive Data Language) is a proprietary, array-oriented designed primarily for scientific and visualization. Developed as an interpreted environment, it enables users to manipulate and visualize complex numerical datasets through high-level commands and built-in procedures, transforming raw into interactive graphics and insights. IDL finds extensive application in data-intensive fields such as astronomy, , medical imaging, and physics, where it supports tasks like image processing, signal analysis, and statistical modeling. Its emphasis on multidimensional arrays allows efficient handling of large-scale datasets due to optimized internal routines. Key characteristics include dynamic typing, an extensive library of mathematical and statistical functions, and platform independence across Windows, macOS, and systems. As of 2025, IDL is owned and maintained by NV5 Geospatial Software, operating under a commercial licensing model that includes academic and discounts to facilitate educational and use.

History

IDL originated in the 1970s at the Laboratory for Atmospheric and Space Physics (LASP) at the University of Colorado Boulder, where David Stern developed prototype software for analyzing data from NASA missions, including Mariner and the International Ultraviolet Explorer (IUE). These early efforts focused on array manipulation and image processing for scientific computing, drawing inspiration from Fortran's numerical capabilities and APL's array-oriented syntax. In 1977, Stern founded Research Systems, Inc. (RSI) in Boulder, Colorado, to commercialize the technology, releasing the first version of IDL that year as a higher-level language tailored for interactive data analysis. By 1981, RSI had rewritten and released an initial VAX/VMS version of IDL in VAX MACRO and Fortran, leveraging the system's virtual memory for efficient handling of large datasets. Key milestones marked IDL's evolution into a robust tool for scientific visualization and analysis. In 1987, IDL was rewritten for UNIX platforms, introducing foundational support for basic plotting and display of data. In 1997, IDL 5.0 added features, including classes, objects, and pointers, which expanded its applicability for complex applications while maintaining with procedural code. The language saw widespread adoption in academic and space science communities, notably through collaborations; for instance, IDL was used to process and analyze data. Later versions continued to enhance performance and interoperability: IDL 8.0 in 2010 provided full 64-bit support and improved bridging to Python for seamless integration with open-source tools. IDL 9.0, released in November 2023, incorporated capabilities and built-in libraries for AI and workflows. Subsequent releases include IDL 9.1 in October 2024 with library updates and IDL 9.2 in July 2025, which ships with a fully-featured embedded Python distribution including packages like and Pandas for enhanced integration. RSI's ownership underwent several changes that influenced IDL's development and distribution. In September 2000, acquired RSI to bolster its imaging software portfolio. sold its Systems division, including RSI, to ITT Industries in August 2004 for $725 million, rebranding the group as ITT Visual Information Solutions. In October 2011, ITT spun off its defense and information businesses to create , with the visual information solutions operating as . acquired in May 2015, forming . Following the 2019 merger of and into , the geospatial software unit continued under until April 2023, when NV5 Global acquired it, renaming it NV5 Geospatial Software and continuing IDL's maintenance. As of November 2025, NV5 continues to update IDL, emphasizing compatibility with modern hardware such as GPUs for accelerated computing and deeper integration with open-source ecosystems like Python and Jupyter for collaborative scientific workflows; these enhancements build on early foundations to support contemporary data-intensive research.

Language Design

Syntax and Structure

IDL employs a procedure-based structure for organizing code, where programs are typically stored in files with the .pro extension. Procedures are defined using the PRO keyword followed by the procedure name and optional parameters, with the block terminated by END; for example, a simple procedure might begin with PRO example_proc and include statements for execution. Functions, which return values, are similarly defined using the FUNCTION keyword, followed by a to specify the output, such as FUNCTION example_func, input with RETURN, result. Main program blocks consist of executable statements outside of procedures or functions, often used for top-level scripts in .pro files. Control structures in IDL facilitate conditional and iterative execution, with support for array-oriented operations. The statement evaluates a condition and executes statements accordingly, using syntax like IF condition THEN statement [ELSE statement] or block forms with BEGIN and ENDIF for multiple statements. Looping is handled by FOR loops (FOR var = init, limit [, step] DO statement), which iterate over a range with optional increments; WHILE loops (WHILE condition DO statement) repeat while a condition holds true; and REPEAT-UNTIL for post-condition checks. The CASE statement provides multi-way branching (CASE expr OF expr1: statements ... [ELSE: statements] ENDCASE), evaluating the first matching case. Additionally, the FOREACH loop enables iteration over arrays or collections (FOREACH element, array DO statement), promoting efficient without explicit indexing. Variables in IDL are dynamically typed, requiring no explicit ; they are created implicitly upon assignment, such as var = value, with the type inferred from the right-hand side and potentially changing during operations. Local variables are confined to the scope of their defining procedure or function and are automatically destroyed upon exit, while global variables can be shared across routines using the COMMON block (COMMON blockname, var1, var2), which maintains values persistently. This scoping promotes modular code while allowing when needed. Error handling in IDL utilizes mechanisms to manage runtime exceptions gracefully. The ON_ERROR statement sets the response to errors within a procedure, such as ON_ERROR, 2 to return control to the caller upon failure, with options for stopping execution or printing stack traces. For more robust handling, the CATCH procedure establishes an error handler (CATCH, error_status), intercepting exceptions and allowing custom recovery code based on the state in the !ERROR_STATE system variable. These tools enable programs to continue execution or log issues without abrupt termination. IDL operates as an interpreted , executing code directly without prior compilation in interactive mode, though .pro files are automatically compiled upon invocation if located in the search path defined by !PATH. Programs can be run from the command line by invoking the IDL executable with batch files or scripts, supporting both interactive sessions and non-interactive . The IDL Workbench, an based on , provides tools for editing, debugging, and execution within a graphical interface, often integrated with ENVI for geospatial workflows. For performance optimization, compiled routines and data can be saved in binary .sav files using the SAVE procedure, which load faster via RESTORE without recompiling . Unlike C-like languages, IDL evaluates expressions strictly from left to right for operators of equal precedence, avoiding from side effects, and lacks raw pointers with arithmetic, instead using managed pointers via PTR_NEW and PTR_FREE for heap access with automatic . The language emphasizes vectorized operations, where functions and operators apply element-wise to arrays by default, reducing the need for explicit loops and enhancing performance for scientific computing tasks.

Data Types and Operations

IDL supports a variety of core data types, emphasizing its array-centric design for scientific computing. Scalar types include BYTE for 8-bit unsigned integers (0 to 255), INT for 16-bit signed integers (-32,768 to 32,767), UINT for 16-bit unsigned integers (0 to 65,535), LONG for 32-bit signed integers (-2,147,483,648 to 2,147,483,647), ULONG for 32-bit unsigned integers (0 to 4,294,967,295), LONG64 for 64-bit signed integers (-9,223,372,036,854,775,808 to 9,223,372,036,854,775,807), ULONG64 for 64-bit unsigned integers (0 to 18,446,744,073,709,551,615), FLOAT for 32-bit single-precision floating-point numbers (approximately 6-7 decimal digits of precision), DOUBLE for 64-bit double-precision floating-point (approximately 15-16 digits), COMPLEX for single-precision complex numbers, DCOMPLEX for double-precision complex numbers, and for variable-length text sequences (up to 2,147,483,647 characters). The data type of a variable is determined by the literal syntax used in assignment, such as a = 5B for BYTE or b = 0.0D for DOUBLE, or by the result of operations and functions. Beyond scalars, IDL's fundamental structures are arrays, which can be one- to eight-dimensional and are the primary means of data storage and manipulation. Arrays are created using type-specific functions like BYTARR(n1, n2, ...) for byte arrays or the more flexible MAKE_ARRAY(dim1, dim2, ..., /TYPE=type) for runtime-specified dimensions and types. User-defined structures provide compound data types with named tags, defined via {tag1: value1, tag2: value2}, where tags are immutable after creation and can hold mixed types. Other compound types include LIST for ordered collections of values, HASH for unordered key-value pairs with any scalar keys, DICTIONARY for case-insensitive string-keyed hashes with dot notation access, and ORDEREDHASH for ordered key-value pairs preserving insertion order. Pointers enable dynamic memory allocation by referencing heap variables, created with PTR_NEW(value) and freed manually with PTR_FREE. Objects, introduced in IDL version 5.0, allow object-oriented programming through class instances created via OBJ_NEW('classname'), with methods for initialization, cleanup, and property access; their data types are defined by the class and remain fixed post-creation. Array indexing in IDL is zero-based, using square brackets for access, such as arr[0] for the first element or arr[0:5, 2] for slicing a subarray from index 0 to 5 in the first dimension and index 2 in the second. This supports efficient subsetting without explicit loops. Mathematical operations are vectorized, applying element-wise to arrays of compatible shapes via automatic broadcasting, where smaller arrays are replicated to match dimensions (e.g., a scalar added to an array adds the scalar to each element). Arithmetic operators include +, -, *, /, and ^ for exponentiation, with intrinsic functions like SIN, EXP, and SQRT also operating element-wise. Type promotion occurs automatically during operations; for instance, adding an INT and a FLOAT yields a FLOAT result to preserve precision. Logical operations in IDL, using AND (&&), OR (||), and NOT (~), extend to arrays and produce byte arrays (0 or 1 masks) for conditional processing, such as filtering elements where (arr GE 0) AND (arr LT 10). These masks enable vectorized selection, like result = arr[WHERE(mask)]. Memory management in IDL relies on automatic garbage collection via for all data types, reclaiming memory when variables go out of scope or reference counts reach zero; however, cyclic references require manual intervention with functions like HEAP_GC. Pointers and objects need explicit freeing with PTR_FREE or OBJ_DESTROY to avoid leaks. The SIZE function queries array properties, returning a vector with the number of dimensions, sizes, type code, and total elements (e.g., SIZE(arr) for a 3x4 FLOAT array yields [2, 3, 4, 4, 12]). Type conversion is handled explicitly through functions that operate on scalars and arrays alike. Examples include BYTE(expr) to unsigned 8-bit integers (truncating decimals), FIX(expr) to 16-bit integers, FLOAT(expr) to single-precision floats, DOUBLE(expr) to double-precision, LONG(expr) to 32-bit integers, and STRING(expr) to text (e.g., converting a byte array to a character string). These ensure compatibility in expressions or data export, with warnings issued for invalid conversions like non-numeric strings to numbers.

Core Features

Array and Image Processing

IDL's array processing capabilities are designed for efficient manipulation of multidimensional data, particularly in scientific computing contexts such as astronomy and earth sciences. Arrays in IDL serve as the primary data structure, enabling operations that leverage the language's array-oriented paradigm to handle large datasets without explicit looping. This approach facilitates rapid reshaping, statistical analysis, and transformation of arrays, making it suitable for preprocessing data in image analysis workflows. Key functions for array reshaping and resampling include , which alters the dimensions of an array while preserving the total number of elements, allowing removal of degenerate dimensions or reconfiguration for compatibility with other routines. returns the transpose of an array and can rearrange dimensions according to a specified permutation vector, useful for aligning data with row-major or column-major storage conventions. performs linear, quadratic, or spline interpolation on vectors with regular or irregular grids, enabling resampling of data to new coordinate points without loss of continuity. For statistical analysis, computes the density function of an array, producing a vector of bin counts that can reveal distribution characteristics, while MOMENT calculates the mean, variance, skewness, and kurtosis of a sample, providing central tendency and shape metrics essential for data quality assessment. Image processing in IDL builds on these array functions with specialized routines for enhancement and feature extraction. CONGRID resizes one-, two-, or three-dimensional arrays by arbitrary factors using methods like nearest neighbor, bilinear, or bicubic, which is particularly effective for scaling images while minimizing artifacts. FILTER_IMAGE applies smoothing or filtering over a moving window, optionally using a (PSF) as a convolution kernel to compute the output value; for a operation, the result at position (x, y) is given by: output[x,y]=∑i∑jkernel[i,j]⋅input[x+i,y+j]\text{output}[x,y] = \sum_{i} \sum_{j} \text{kernel}[i,j] \cdot \text{input}[x+i, y+j] where the sums are over the kernel dimensions, effectively blurring or sharpening the image depending on the kernel design. For edge detection, the SOBEL function approximates the , emphasizing regions of high by computing magnitudes in horizontal and vertical directions, though IDL does not natively implement the operator. These routines support byte, integer, and floating-point images, with edge detection outputs highlighting boundaries in or binary form. IDL supports multidimensional arrays up to eight dimensions, accommodating complex datasets like hyperspectral imagery where spectral bands form additional axes alongside spatial dimensions, allowing seamless handling of datacubes with millions of elements. Efficient for large files is provided by SHMMAP, which maps disk files or into the IDL address space, enabling access to datasets exceeding available RAM without full loading, thus supporting out-of-core processing for terabyte-scale arrays. Performance in and operations is optimized through vectorization, where built-in functions process entire in parallel using compiled , avoiding the overhead of explicit loops and achieving speeds comparable to low-level languages for bulk operations. Later versions of IDL incorporate multi-threading via an internal , automatically parallelizing eligible computations like convolutions and statistical moments across multiple cores for up to linear on multicore systems. File input/output for arrays emphasizes scientific formats, with READ_BINARY reading unstructured into arrays using templates for type and structure specification. SAVE and RESTORE handle of variables, including arrays, into portable .sav files for session persistence, while built-in support for and HDF allows direct reading and writing of multidimensional arrays in these standards-compliant formats, facilitating interoperability with tools like or Python in collaborative environments.

Visualization and Graphics

IDL provides robust built-in capabilities for visualization and , enabling users to render data as plots, surfaces, and interactive displays directly within the language. The system supports both Direct Graphics, the traditional immediate-mode approach for static visualizations, and Object Graphics, an object-oriented framework introduced in IDL 5.0 (1997) for more flexible, modifiable . Direct Graphics uses procedural commands to draw directly to devices, while Object Graphics returns graphic objects that can be manipulated post-creation, such as adjusting properties like colors or positions. These features allow seamless transition from data arrays to visual representations, emphasizing in scientific computing. Core plotting commands facilitate 2D and 3D visualizations. The PLOT procedure creates 2D line or scatter plots from vector data, supporting options for axes scaling, symbols, and lines; for example, PLOT, x, y renders basic line graphs. CONTOUR generates 2D contour plots for level sets of 2D arrays, useful for depicting scalar fields like elevation or density. For 3D representations, SURFACE produces mesh or wireframe surfaces from 2D arrays, with parameters for hidden-line removal and lighting. IMAGELINE overlays lines on raster images, aiding in annotated displays of image data. Device and window management ensures control over output destinations. The WINDOW procedure opens new graphics windows for displaying plots, with options for size and position. DEVICE handles output to various formats, including PostScript for vector printing and PNG for raster images, via commands like SET_PLOT, 'PS' followed by DEVICE settings. WIDGET creates GUI elements, such as buttons or sliders, integrated into graphics windows for user interaction. Advanced graphics extend to specialized domains. MAP supports geospatial projections, plotting continents, grids, and overlays for cartographic visualizations. SHADE_SURF renders shaded relief surfaces, enhancing 3D plots with Gouraud or Phong shading for realistic terrain depictions. TV displays raster images scaled to the current window, fundamental for image-based graphics. Customization options allow fine-tuning of appearances. The system variable !P.MULTI configures multi-panel layouts, enabling arrangements like 2x2 grids for comparative plots via settings such as [0,2,2]. Color tables are loaded with LOADCT, selecting predefined palettes (e.g., 0 for grayscale, 39 for rainbow) to map data values to colors. Annotations are added using XYOUTS for text placement at specified coordinates and ARROW for directional lines, both supporting fonts, sizes, and styles. Interactive features support dynamic applications. XMANAGER handles for widget-based GUIs, registering callbacks for mouse or keyboard events. ZOOMBOX enables interactive scaling, allowing users to select regions for magnified views within plots. Export capabilities facilitate sharing visuals. SAVEIMAGE outputs to formats like or TIFF, preserving quality for publications; in Object Graphics, methods like graphic.Save, 'file.png' achieve similar results. This integration between Direct and Object Graphics systems ensures compatibility, with Object Graphics preferred for modern 3D and interactive tasks due to its extensibility.

Integration Capabilities

IDL provides robust foreign function interfaces through the CALL_EXTERNAL function, which enables seamless invocation of routines written in C or from dynamically linked libraries (DLLs on Windows or shared objects on systems). This interface supports argument passing by reference using pointers, allowing efficient data exchange between IDL arrays and external code without unnecessary copying, and adheres to the IDL Portable Convention for cross-platform compatibility. Language bridging in IDL facilitates interoperability with other programming environments, notably through the bidirectional Python bridge introduced in IDL 8.5. This bridge, comprising the IDL-to-Python and Python-to-IDL components, permits calling Python code from IDL and vice versa, enabling IDL objects to be directly manipulated within Python libraries such as and for enhanced numerical computing workflows. As of IDL 9.0 (2023), the bridge supports Python 3.11, and IDL 9.2 (August 2025) introduced embedded Python, allowing Python code to run directly within the IDL environment without external Python installations. Database connectivity is supported via the IDL DataMiner, an (ODBC) interface that allows users to query and manipulate data from various SQL-compliant databases, including connection establishment, SQL execution, and result retrieval into IDL structures. For web interactions, the IDLnetURL object class handles HTTP and FTP protocols, supporting operations like GET, PUT, and directory listing for remote file transfers and server communication. Additionally, built-in routines such as JSON_PARSE and XML_PARSE provide native support for parsing and XML data formats, converting them into IDL variables for further processing. For with cameras and s, IDL supports file-based ingestion of data through its processing routines, often extended via external libraries callable through CALL_EXTERNAL for direct hardware access. IDL maintains tight integration with the ENVI software suite, where it serves as the primary scripting engine, allowing users to extend ENVI's analysis capabilities with custom IDL procedures and access ENVI's for tasks like multispectral directly within IDL environments.

Usage and Implementation

Programming Examples

IDL's syntax supports concise array manipulations, as demonstrated in this basic example that generates a array, plots it, and computes its value. The FINDGEN function creates an array of sequential floating-point numbers starting from 0, while SIN applies the sine function element-wise. The PLOT procedure displays the data graphically, and MEAN calculates the . This showcases IDL's array-oriented nature, where operations apply broadcast to entire arrays without explicit loops.

a = FINDGEN(100) * !PI / 50.0 PLOT, a, [SIN](/page/Sin)(a) PRINT, [MEAN](/page/Mean)([SIN](/page/Sin)(a))

a = FINDGEN(100) * !PI / 50.0 PLOT, a, [SIN](/page/Sin)(a) PRINT, [MEAN](/page/Mean)([SIN](/page/Sin)(a))

Executing this code produces a plot of the over the generated x-values and outputs a value close to 0 to the console, highlighting IDL's built-in mathematical functions and visualization integration. For image processing, IDL provides routines to load and filter images efficiently. The READ_IMAGE function imports an image file into a byte array, and FILTER_IMAGE applies a filter, such as averaging pixels in a moving window to smooth the image. The TV procedure then displays the result in the current graphics window. This example uses the /AVERAGE keyword for mean filtering with a default 3x3 kernel, demonstrating IDL's specialized tools for two-dimensional data.

img = READ_IMAGE('example.tif') FILTER_IMAGE, img, smoothed, /AVERAGE TV, smoothed

img = READ_IMAGE('example.tif') FILTER_IMAGE, img, smoothed, /AVERAGE TV, smoothed

Assuming 'example.tif' is a valid image file, this code loads the image, applies to reduce , and renders the filtered version, preserving the original dimensions while altering intensities based on neighborhood averages. Control structures in IDL include FOR loops for and IF statements for conditionals, often combined with CATCH for error handling to prevent program termination. In this example, the code iterates over a data , attempting division on all elements while catching potential division-by-zero errors to demonstrate handling within the loop. N_ELEMENTS returns the size, and the RESULT is pre-allocated for efficiency. The CATCH is set inside the loop to intercept errors per , allowing graceful continuation.

data = [1.0, 2.0, 0.0, 3.0] result = DBLARR(N_ELEMENTS(data)) FOR i = 0, N_ELEMENTS(data)-1 DO BEGIN CATCH, error_id IF error_id NE 0 THEN BEGIN PRINT, 'Error caught: ', !ERROR_STATE.MSG CATCH, /CANCEL result[i] = 0.0 CONTINUE ENDIF result[i] = 10.0 / data[i] CATCH, /CANCEL ENDFOR PRINT, result

data = [1.0, 2.0, 0.0, 3.0] result = DBLARR(N_ELEMENTS(data)) FOR i = 0, N_ELEMENTS(data)-1 DO BEGIN CATCH, error_id IF error_id NE 0 THEN BEGIN PRINT, 'Error caught: ', !ERROR_STATE.MSG CATCH, /CANCEL result[i] = 0.0 CONTINUE ENDIF result[i] = 10.0 / data[i] CATCH, /CANCEL ENDFOR PRINT, result

This outputs approximately [10.0000, 5.00000, 0.00000, 3.33333] (with an error message for the zero element), illustrating robust and conditional logic with exception management in IDL. IDL procedures and functions enable modular code, such as this procedure for linear curve fitting using the built-in CURVEFIT function with polynomial mode. The procedure takes input arrays x and y, computes fitted parameters (slope and intercept) weighted inversely by y-squared for error handling, and stores them in an output variable. The /POLY keyword specifies a linear fit (degree 1), bypassing the need for a custom model function. This example highlights IDL's support for non-linear least-squares optimization in a reusable procedure.

PRO fit_data, x, y, params weights = 1.0 / (y^2 + 1e-6) ; Avoid [division by zero](/page/Division_by_zero) params = CURVEFIT(x, y, weights, [1.0, 0.0], 1, /POLY) END

PRO fit_data, x, y, params weights = 1.0 / (y^2 + 1e-6) ; Avoid [division by zero](/page/Division_by_zero) params = CURVEFIT(x, y, weights, [1.0, 0.0], 1, /POLY) END

To use: x = FINDGEN(10); y = 2*x + 1 + RANDOMU(seed,10); fit_data, x, y, p; PRINT, p yields approximately [2.0, 1.0], demonstrating parameter estimation for linear models with weighted fitting.

Applications in Science

IDL has been extensively applied in astronomy for processing observational data from major telescopes. The IDL Astronomy User's Library, developed and maintained by , provides a suite of routines for handling astronomical data formats like files, enabling tasks such as analysis and mosaic imaging of celestial objects. For instance, the SMART software package, implemented in IDL, facilitates the reduction and analysis of infrared spectra from the Spitzer Space Telescope's Infrared Spectrograph, allowing astronomers to extract and interpret emission lines from distant galaxies and star-forming regions. Similarly, tools like IrsLow use IDL to coadd low-resolution spectra from Spitzer observations, supporting studies of planetary atmospheres and debris disks. In Earth sciences, IDL powers geospatial analysis through integration with ENVI software, particularly for processing . ENVI-IDL routines enable calculations of vegetation indices, such as the (NDVI) from Landsat data, which quantify plant health and changes for . This capability is crucial for applications like tracking and agricultural productivity. Additionally, IDL supports modeling visualizations by processing large datasets from models like the UVic Earth System , generating plots of circulation and temperature anomalies to aid in understanding global dynamics. In weather forecasting, IDL has been employed to analyze numerical prediction models, as seen in enhancements to Japanese Meteorological Agency simulations for improved accuracy in and tracking. Medical imaging benefits from IDL's array operations for tasks like segmentation and of scans. The IDL DICOM modules allow reading and manipulating medical image files, supporting the processing of MRI and CT data to isolate anatomical structures or visualize tissue densities. For example, IDL routines can warp and register MRI volumes to CT scans, facilitating multimodal analysis in diagnostics and surgical planning. This has been applied in research environments, such as the MEGSIM MRIView tool, which uses IDL for interactive display and analysis of data. Beyond these domains, IDL finds use in physics for particle tracking simulations, where its image processing capabilities help analyze trajectories of microscopic particles in imaging experiments. In engineering, it supports signal processing for vibration analysis and sensor data from aerospace systems. NASA's broader adoption includes IDL for planetary imagery processing, such as in the analysis of Mars rover Pancam images through custom routines for color calibration and feature extraction. Academic research reflects this impact, with IDL cited in thousands of peer-reviewed publications annually across scientific fields, remaining the second most used language in astronomy as of recent surveys. IDL's scripting features enable seamless workflow integration, including on clusters for large-scale . Users can submit IDL jobs via batch scripts on systems like those at the University of Utah's Center for High Performance Computing, allowing parallel execution of simulations and visualizations on multi-node environments. This supports efficient handling of terabyte-scale datasets in scientific pipelines, from initial data ingestion to final reporting.

Limitations and Evolution

Known Problems

One notable performance bottleneck in IDL arises during the processing of very large arrays, where execution can slow significantly without manual optimizations, even with its built-in vectorization capabilities. For instance, operations on arrays exceeding several gigabytes in size often require explicit techniques like using TEMPORARY for or accessing data in order to avoid degradation, as scalar operations or loops exacerbate the issue. This limitation stems from IDL's interpreted nature, which, while efficient for array-based computations, struggles with heavy individual element access or unoptimized code on massive datasets. IDL's licensing model presents accessibility challenges due to its high commercial , which restricts by individual researchers or small teams outside academic or institutional settings. Additionally, while the IDL allows free runtime distribution of compiled applications, more advanced runtime licensing is required for enhanced flexibility in deployment, imposing further costs and restrictions on non-licensed users. Compatibility issues frequently arise from version-specific changes in IDL, which can break legacy code; for example, the transition to full 64-bit support in IDL 8.3 (released in ) introduced crashes in certain GUI elements like DIALOG_PRINTERSETUP when called from the , and overall for widgets has been inconsistent across updates. Release notes consistently document such changes, requiring users to review migration guides to adapt older scripts, particularly those relying on deprecated features or platform-specific behaviors. Documentation for IDL exhibits gaps, particularly in outdated manuals covering legacy features, contributing to a steep for users unfamiliar with its Fortran-inspired syntax and array-centric paradigm. While online resources provide comprehensive coverage for core routines, obsolete functions documented in older guides (e.g., pre-6.x versions) often lack clear notices, complicating maintenance of historical codebases. Recent versions, particularly IDL 9.x, have addressed some persistent issues through targeted patches, including fixes for memory leaks in IDL_IDLBridge and object execution commands, as well as enhancements to GPU support via improved compatibility with modern hardware like for accelerated computations. IDL 9.2, released in August 2025, introduces further improvements such as native integration with for enhanced code editing, debugging, and auto-completion. These updates aim to mitigate earlier performance and stability problems without altering core syntax.

Alternatives and Successors

One prominent open-source alternative to IDL is the GNU Data Language (GDL), a free incremental designed as a that achieves full compatibility with IDL version 7 and partial compatibility with later versions, supporting approximately 90% of IDL's syntax for numerical calculations, data visualization, and image processing. GDL facilitates migration of legacy IDL code by handling array-oriented operations, file formats like and HDF, and graphical outputs, though it has limitations in advanced features documented in its known-issues list. It is particularly useful for researchers seeking cost-free maintenance of older scripts without proprietary licensing. The Python ecosystem serves as a versatile modern alternative, leveraging libraries such as for array manipulation, for scientific computing, and for visualization to replicate IDL's core functionalities in an open-source environment with a larger community and greater extensibility. Tools like pyIDL automate initial code conversion by translating IDL constructs—such as loops, operators, and vector indexing—into Python equivalents, jump-starting migrations for projects while requiring manual adjustments for unsupported elements like function headers. Community efforts, including wrappers via the IDL to Python Bridge, enable hybrid workflows where IDL calls Python modules like , easing transitions; benchmarks indicate Python can be 2-5 times faster for large datasets in I/O-intensive tasks due to optimized libraries. IDL offers faster prototyping for quick scripts, whereas Python provides superior scalability for and integration with frameworks. Other options include Julia, which excels in high-performance scientific computing with for tasks like numerical simulations, often outperforming IDL in loop-heavy operations while maintaining readable syntax. R focuses on statistical analysis and visualization, serving as a niche alternative for data-heavy workflows but differing significantly in syntax from IDL's array focus. remains a direct proprietary competitor with similar licensing and vectorized operations for engineering applications, though it shares IDL's cost barriers. As a successor and extension, the ENVI+IDL bundle from NV5 Geospatial Software continues to target and geospatial analysis, integrating IDL's visualization with ENVI's image processing tools under NV5's ownership since 2023, with ongoing support including Python bridges for hybrid environments. NV5 emphasizes IDL's role in local data analytics without a full pivot to cloud-based systems, though the Python Bridge supports evolution toward scalable, open ecosystems. Official migration aids like RSI's (now NV5's) IDL-to-Python translator further this shift, allowing direct calls to Python libraries from IDL for gradual code modernization.

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