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!



Monitor, Measure & Mitigate: Integrated Solutions for Geohazard Risk

Monitor, Measure & Mitigate: Integrated Solutions for Geohazard Risk

9/8/2025

Geohazards such as slope instability, erosion, settlement, or seepage pose ongoing risks to critical infrastructure. Roads, railways, pipelines, and utility corridors are especially vulnerable to these natural and human-influenced processes, which can evolve silently until sudden failure occurs. Traditional ground surveys provide only periodic... Read More >

Geo Sessions 2025: Geospatial Vision Beyond the Map

Geo Sessions 2025: Geospatial Vision Beyond the Map

8/5/2025

Lidar, SAR, and Spectral: Geospatial Innovation on the Horizon Last year, Geo Sessions brought together over 5,300 registrants from 159 countries, with attendees representing education, government agencies, consulting, and top geospatial companies like Esri, NOAA, Airbus, Planet, and USGS. At this year's Geo Sessions, NV5 is... Read More >

Not All Supernovae Are Created Equal: Rethinking the Universe’s Measuring Tools

Not All Supernovae Are Created Equal: Rethinking the Universe’s Measuring Tools

6/3/2025

Rethinking the Reliability of Type 1a Supernovae   How do astronomers measure the universe? It all starts with distance. From gauging the size of a galaxy to calculating how fast the universe is expanding, measuring cosmic distances is essential to understanding everything in the sky. For nearby stars, astronomers use... Read More >

Using LLMs To Research Remote Sensing Software: Helpful, but Incomplete

Using LLMs To Research Remote Sensing Software: Helpful, but Incomplete

5/26/2025

Whether you’re new to remote sensing or a seasoned expert, there is no doubt that large language models (LLMs) like OpenAI’s ChatGPT or Google’s Gemini can be incredibly useful in many aspects of research. From exploring the electromagnetic spectrum to creating object detection models using the latest deep learning... Read More >

From Image to Insight: How GEOINT Automation Is Changing the Speed of Decision-Making

From Image to Insight: How GEOINT Automation Is Changing the Speed of Decision-Making

4/28/2025

When every second counts, the ability to process geospatial data rapidly and accurately isn’t just helpful, it’s critical. Geospatial Intelligence (GEOINT) has always played a pivotal role in defense, security, and disaster response. But in high-tempo operations, traditional workflows are no longer fast enough. Analysts are... Read More >

1345678910Last
«September 2025»
SunMonTueWedThuFriSat
31123456
78910111213
14151617181920
21222324252627
2829301234
567891011
6352 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.