X

NV5 Geospatial Blog

Each month, NV5 Geospatial posts new blog content across a variety of categories. Browse our latest posts below to learn about important geospatial information or use the search bar to find a specific topic or author. Stay informed of the latest blog posts, events, and technologies by joining our email list!



Mapping Earthquake Deformation in Taiwan With ENVI

Mapping Earthquake Deformation in Taiwan With ENVI

12/15/2025

Unlocking Critical Insights With ENVI® Tools Taiwan sits at the junction of major tectonic plates and regularly experiences powerful earthquakes. Understanding how the ground moves during these events is essential for disaster preparedness, public safety, and building community resilience. But traditional approaches like field... Read More >

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

12/3/2025

Large commercial SAR satellite constellations have opened a new era for persistent Earth monitoring, giving analysts the ability to move beyond simple two-image comparisons into robust time series analysis. By acquiring SAR data with near-identical geometry every 24 hours, Ground Track Repeat (GTR) missions minimize geometric decorrelation,... Read More >

Empowering D&I Analysts to Maximize the Value of SAR

Empowering D&I Analysts to Maximize the Value of SAR

12/1/2025

Defense and intelligence (D&I) analysts rely on high-resolution imagery with frequent revisit times to effectively monitor operational areas. While optical imagery is valuable, it faces limitations from cloud cover, smoke, and in some cases, infrequent revisit times. These challenges can hinder timely and accurate data collection and... Read More >

Easily Share Workflows With the Analytics Repository

Easily Share Workflows With the Analytics Repository

10/27/2025

With the recent release of ENVI® 6.2 and the Analytics Repository, it’s now easier than ever to create and share image processing workflows across your organization. With that in mind, we wrote this blog to: Introduce the Analytics Repository Describe how you can use ENVI’s interactive workflows to... Read More >

Deploy, Share, Repeat: AI Meets the Analytics Repository

Deploy, Share, Repeat: AI Meets the Analytics Repository

10/13/2025

The upcoming release of ENVI® Deep Learning 4.0 makes it easier than ever to import, deploy, and share AI models, including industry-standard ONNX models, using the integrated Analytics Repository. Whether you're building deep learning models in PyTorch, TensorFlow, or using ENVI’s native model creation tools, ENVI... Read More >

1345678910Last
6777 Rate this article:
No rating

The merits of an example main program

Anonym

When I write a new routine in IDL—a procedure or a function or a class—I like to include a main program at the bottom of the file. I use the main program to

  • demonstrate the calling syntax of the routine
  • give an example of how the routine is used
  • define a simple unit test (or tests)

I first saw this idea used in Python and I’ve copied it for my work in IDL. For example, here’s the full code listing for a simple function, FLATTEN (which converts a multidimensional array into a vector) along with an example main. The programs are saved in the file flatten.pro, in a directory in IDL’s path.

; docformat = 'rst' 
;+ 
; A convenience routine that flattens/linearizes a 
; multidimensional array. 
; 
; :params: 
;  x : in, required, type=any array 
;   An array of any type to be converted to a vector. 
; 
; :author: 
;  Mark Piper, VIS, 2011 
;-
function flatten, x
   compile_opt idl2

   nx = n_elements(x)
   return, nx gt 0 ? reform(x, nx) : 0
end

; Example
a = indgen(5, 7)
b = flatten(a)
c = reform(a, n_elements(a))
help, a, b, c
print, 'Equivalent results? ', array_equal(b, c) ? 'Y' : 'N'
end 

By examining the main program, you can see how FLATTEN works; here, it’s used to convert a 5 x 7 array into a 35-element vector. To use the main program as an example, I execute it from the command line with the .run executive command:

IDL> .r flatten
% Compiled module: FLATTEN.
% Compiled module: $MAIN$.
A               INT       = Array[5, 7]
B               INT       = Array[35]
C               INT       = Array[35]
Equivalent results? Y

The .run command compiles both routines and executes the main program. We’d get the same behavior from the Run button (or the F8 keyboard shortcut) in the IDL Workbench. Note that—and this is important—the calling mechanism still resolves the FLATTEN function (by itself) correctly:

IDL> .reset
IDL> x = indgen(2,3)
IDL> print, x
      0       1
      2       3
      4       5
IDL> y = flatten(x)
% Compiled module: FLATTEN.
IDL> print, y
      0       1       2       3       4       5

This means that (as intended) FLATTEN can be used as a library routine independent of its example main program. I find this technique of including an example main to be especially useful with functions, which won’t execute with the Run button on the Workbench. (This may be a topic for another post, where I'd like to argue for an implicit redirect to !null for functions; e.g., FLATTEN could be called like this:

IDL> flatten(x)

without throwing a syntax error.) Note: ENVI 5 was released this week. It has a new UI and a new API. The API still uses IDL, but with an object-oriented interface. Though I'm not a heavy ENVI user, I'd like to show some examples of using the new API over the next few weeks and months.

Please login or register to post comments.