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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!



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 >

Deploy, Share, Repeat: AI Meets the Analytics Repository

Deploy, Share, Repeat: AI Meets the Analytics Repository

10/13/2025

The upcoming release of ENVI® Deep Learning 4.0 makes it easier than ever to import, deploy, and share AI models, including industry-standard ONNX models, using the integrated Analytics Repository. Whether you're building deep learning models in PyTorch, TensorFlow, or using ENVI’s native model creation tools, ENVI... 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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