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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!



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 >

Thermal Infrared Echoes: Illuminating the Last Gasp of a Dying Star

Thermal Infrared Echoes: Illuminating the Last Gasp of a Dying Star

4/24/2025

This blog was written by Eli Dwek, Emeritus, NASA Goddard Space Flight Center, Greenbelt, MD and Research Fellow, Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA. It is the fifth blog in a series showcasing our IDL® Fellows program which supports passionate retired IDL users who may need support to continue their work... Read More >

A New Era of Hyperspectral Imaging with ENVI® and Wyvern’s Open Data Program

A New Era of Hyperspectral Imaging with ENVI® and Wyvern’s Open Data Program

2/25/2025

This blog was written in collaboration with Adam O’Connor from Wyvern.   As hyperspectral imaging (HSI) continues to grow in importance, access to high-quality satellite data is key to unlocking new insights in environmental monitoring, agriculture, forestry, mining, security, energy infrastructure management, and more.... Read More >

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What the *bleep* is IDL doing? Creating variables

Anonym

The IDL language has many features which allow for quick and simple programming.  For example, say you have an INT array and you need to append a value to it:

intarray = [intarray,42]

 

Quick.  Simple.  However, just because you can do this doesn't mean you should.  Let's examine what is happening in the statement:

intarray = [intarray,42]

 

First, IDL has to create a temporary array to hold intarray and the new value.  Then, IDL copies intarray into the temporary and adds 42 to the end of it.  Finally, it has to free the old intarray variable.  Creating a temporary variable is an expensive operation.  As such, you should always be mindful when operations will create temporary variables.  Take for example creating an integer array of fixed length with some initial value using array appending.

print, 'Creating array by appending'

tic

length=100000l

a=[]

for i=0l,length-1 do a=[a,42]

toc

 

Which completes in:

% Time elapsed: 1.4720001 seconds.

While the append functionality gives the flexibility to add an element to an array, the time to create the temporary makes this approach extremely impractical.  However, if we need a dynamic array which elements and be added and removed as needed, consider using LIST. 

print, 'Creating array by list'

tic

length=100000l

l=list()

for i=0l, length-1 do l.add, 42

a=l.toarray()

toc

 

Which completes in:

% Time elapsed: 0.15200019 seconds.

LIST's avoid duplicating the entire array every time you add an element.  This is especially important when you are dealing with dynamic data.  However, if we know how long our array should be we can use IDL's array creation functions to drastically increase performance. 

print, 'Creating array by preallocate'

tic

length=100000l

a=intarr(length,/nozero)

for i=0l, length-1 do a[i]=42

toc

 

Which completes in:

% Time elapsed: 0.0060000420 seconds.

In this case we can speed up our processing even more.  Since we are initializing each element of our array to the same value, we can improve our performance even more by using the MAKE_ARRAY function.

print, 'Creating array by vectorized solution'

tic

length=100000l

a=make_array(length,value=42)

toc

 

Which completes in:

% Time elapsed: 0.00000000 seconds.

There are many way to accomplish any given task in IDL.  However, each method has its unique advantages and disadvantages.  By understanding what you are trying to accomplish and what IDL has to do under the hood to perform an operation, you can dramatically decrease the time it takes your code to run.

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