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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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Order of operations in an expression

Anonym
Given a floating-point array x, which of these two statements:
IDL> y1 = 2.0 * x / 3.0
IDL> y2 = x * (2.0 / 3.0)
executes faster, the first or the second? Note that both statements produce the same result to within floating-point precision. We can test the execution time of IDL code with the built-in SYSTIME function. Here’s an example program that demonstrates a technique for doing so:
pro test_orderofoperations
   compile_opt idl2

   n_iter = 1e2
   x = findgen(1e6)

   t0 = systime(/seconds)
   for i=1, n_iter do y1 = 2.0 * x / 3.0
   t1 = systime(/seconds) - t0
   print, t1, format='("Execution time for [2.0 * x / 3.0]:   ",f5.2," s")'

   t0 = systime(/seconds)
   for i=1, n_iter do y2 = x * (2.0 / 3.0)
   t2 = systime(/seconds) - t0
   print, t2, format='("Execution time for [x * (2.0 / 3.0)]: ",f5.2," s")'

   print, t1/t2, format='("Ratio : ",f5.2)'
end 
Note that I chose a smallish array size to avoid memory issues (this is another performance topic) and I looped over the statements many times to attempt to average out any transient effects. Here’s the result from running this program on my laptop (YMMV):
IDL> test_orderofoperations
Execution time for [2.0 * x / 3.0]:    0.77 s
Execution time for [x * (2.0 / 3.0)]:  0.30 s
Ratio :  2.58
Why does the execution time of these statements differ? In the first statement, there’s a multiplication and a division. Since both are at the same level in the operator hierarchy, IDL works from left to right, calculating (2.0 * x), then dividing the result by the value 3.0. The key here is that the expression (2.0 * x) is an array operation, so under the hood, at the C level of IDL, every element of the array x is multiplied by the value 2.0. The result is a new array, held temporarily in memory. Every element of this array is then divided by the scalar value 3.0. Compare this order of operation with that in the second statement. Here, because of the parentheses, the scalar operation (2.0 / 3.0) is performed first, with another scalar as a result. This scalar is then multiplied, element-by-element, with the array x. So, the difference between these two statements is that the first uses two array operations, whereas the second uses only one. The lesson is then: group scalar operations in an expression. This is a simple performance tweak that will help your IDL code run faster. For more information on code performance, including demonstrations of techniques similar to this, see Mike Galloy's book, Modern IDL. We also experiment with several performance techniques in our Scientific Programming with IDL course.
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