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!



From Answers to Action: Why ENVI and IDL Agents Go Beyond General AI

From Answers to Action: Why ENVI and IDL Agents Go Beyond General AI

4/20/2026

As generative AI tools like Claude and Gemini continue to gain traction, many organizations are asking the same question: Can general purpose AI actually support real geospatial workflows, or does it stop at surface-level answers? That question was front and center in our recent webinar, Meet Your New Partners in Science: ENVI... Read More >

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 >

1345678910Last
«April 2026»
SunMonTueWedThuFriSat
2930311234
567891011
12131415161718
19202122232425
262728293012
3456789
8686 Rate this article:
3.0

The minimum and maximum operators

Anonym

Have you used the minimum (<) and maximum (>) operators in IDL? They're among my favorites; they're binary operators that return the smaller (minimum) or larger (maximum) of their operands. For example, 5 is larger than 2, so the minimum operator returns 2:

IDL> print, 5 < 2
       2

whereas the maximum operator returns 5:

IDL> print, 5 > 2
       5

Note that these differ from the IDL relational operators LT and GT, which are also binary operators, but instead return a true (1B) or false (0B) value:

IDL> print, 5 lt 2
   0
IDL> print, 5 gt 2
   1

It gets better: like most operators in IDL, the minimum and maximum operators also work on arrays. For example, a sill operation raises the minimum value of an array to the value of the sill. Here, the array new is created by applying the maximum operator to the array test with the value sill:

IDL> test = indgen(5)
IDL> sill = 2
IDL> print, test
       0       1       2       3       4
IDL> new = test > sill
IDL> print, new
       2       2       2       3       4

Note that no value of new is less than sill. The minimum and maximum operators can also be used in masking. Imagine data, stored in an array, that has bad values. I want to mask out and replace the bad values. For this example, I'll use RANDOMN to generate sample data:

IDL> sample = randomn(1, 5, 5)
IDL> print, sample
    -0.836854    -0.172280     0.187117      1.61544    -0.176774
     0.653145    -0.546364     0.194146     0.925709      1.20432
      1.53055     -1.35556    0.0514889      1.02018     -1.22616
     0.708497     0.871673    -0.789721     0.332079     0.205603
    -0.169367    -0.318417    -0.295643     0.522291     -2.23105

Say the bad values are less than zero and I want to replace them with the value zero. You could use the WHERE function to identify the locations of these values in the array:

IDL> i_bad = where(sample lt 0.0, /null)
IDL> help, i_bad
I_BAD           LONG      = Array[11]

and then, by subscripting, replace them with zeros:

IDL> sample[i_bad] = 0.0
IDL> print, sample
     0.000000     0.000000     0.187117      1.61544     0.000000
     0.653145     0.000000     0.194146     0.925709      1.20432
      1.53055     0.000000    0.0514889      1.02018     0.000000
     0.708497     0.871673     0.000000     0.332079     0.205603
     0.000000     0.000000     0.000000     0.522291     0.000000

But the maximum operator, used this time in a compound operator, provides a one-line solution:

IDL> sample = randomn(1, 5, 5) ; reconstitute the sample
IDL> sample >= 0.0
IDL> print, sample
     0.000000     0.000000     0.187117      1.61544     0.000000
     0.653145     0.000000     0.194146     0.925709      1.20432
      1.53055     0.000000    0.0514889      1.02018     0.000000
     0.708497     0.871673     0.000000     0.332079     0.205603
     0.000000     0.000000     0.000000     0.522291     0.000000

Cool! Do you have an example in your work where you use the minimum or maximum operator?

Please login or register to post comments.