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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!



NV5 at ESA’s Living Planet Symposium 2025

NV5 at ESA’s Living Planet Symposium 2025

9/16/2025

We recently presented three cutting-edge research posters at the ESA Living Planet Symposium 2025 in Vienna, showcasing how NV5 technology and the ENVI® Ecosystem support innovation across ocean monitoring, mineral exploration, and disaster management. Explore each topic below and access the full posters to learn... Read More >

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 >

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Array Uniqueness in IDL

Anonym

 

When a person finds oneself in pursuit of retrieving information from data, it is often imperative to inspect every unique element - but why waste time on inspecting EVERY element when you can weed out the duplicates?

IDL has a uniq function built for just such a purpose. It goes in to an array, and removes any duplicates adjacent to one another.

Let's take an array:

IDL> array = [1,2,2,5,1,4,4,2]

When we run uniq on this array, it returns the indices that are NOT duplicates of an adjacent entry. This prints out:

IDL> print, uniq(array)

          0        2       3        4        6        7

In order to get back the original array with the elements removed, we can subset the array with these indices:

IDL> print, array[uniq(array)]

       1       2      5       1       4      2

The duplicate 2 and 4 have been removed, however there are still more duplicates in the array. To get just one of each unique element, you first have to use the sort function. This function also returns indices; in this case the indices that put the array in ascending order:

IDL> print, sort(array)

          4        0        7       2        1        6       5        3

Just like with uniq, these can be used to re-order the original array to get the sorted array:

IDL> print, array[sort(array)]

       1       1      2       2       2      4       4       5

Now for the final step - since this sorted array has all ofthe similar elements adjacent to each other, we can now use the uniq function to pull out all of the unique elements of the array.

IDL> s = array[sort(array)]

IDL> print, s[uniq(s)]

       1       2      4       5

Or for those that like to do it in one line:

IDL> print, (array[sort(array)])[uniq(array[sort(array)])]

       1       2      4       5

Now instead of looping over and entire array to check every element, IDL will be able to look through and array that is half the size of its original.

2 comments on article "Array Uniqueness in IDL"

Avatar image

Michael Galloy

A bit shorter for the one-line is to use the optional second argument to UNIQ:

IDL> print, array[uniq(array, sort(array))]

1 2 4 5


Avatar image

Jim P

In IDL 8.4, an even shorter way...

IDL> array.uniq()

1 2 4 5

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