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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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Dynamic Plots Using an Equation Function

Anonym

This blog post continues to explore the Dynamic Visualizations available using the Equation argument on the PLOT function. In this example, we explore the use of a function name rather than an equation string. See Dynamic Plots Using an Equation String for the first article in this series.

Using an equation string has some limitations:

  • You can only have a single statement
  • You cannot easily change the equation unless you set a new string
  • You cannot pass parameters into your equation

As a different approach, create an IDL function containing your equation and then pass the function name to the Equation argument. This method allows you a bit more flexibility in your input equations.

For example, in the first line of the code above, we could have simply written:

p1= PLOT('LambertW', '2', DIMENSIONS=[400,400],$

  NAME='Upperbranch', $

  TITLE='LambertW Function', XRANGE=[-0.4, 2])

 

Notice that we no longer have an X variable in our equation, we just have the name of the function. We can also create our own routine which accepts our X vector and some optional user data.

First, create a new IDL routine called ex_plot_function and save it in a file ex_plot_function.pro on IDL's path:

FUNCTIONex_plot_function, x, k

COMPILE_OPTIDL2

RETURN,LAMBERTW(DCOMPLEX(x), k)

END

 

Next, we create our plot visualization, passing in the name of our equation along with our user data containing the "branch" parameter k:

 

p1= PLOT('ex_plot_function', '2', DIMENSIONS=[400,400],$

 

NAME='k= 0', EQN_USERDATA=0, $

TITLE='$\Re${LambertW}',XRANGE=[-1, 2])

p2r= PLOT('ex_plot_function', '2r', /OVERPLOT, $

NAME='k= -1', EQN_USERDATA=-1)

p3r= PLOT('ex_plot_function', '2g', /OVERPLOT, $

NAME='k= 1', EQN_USERDATA=1)

p4r= PLOT('ex_plot_function', '2b', /OVERPLOT, $

NAME='k= 2', EQN_USERDATA=2)

lg= LEGEND(/DATA, POSITION=[1.9, -4], LINESTYLE=6, SHADOW=0)

 

Our plot should now look like the following:

Again, we can pan and zoom around the plot, and IDL will automatically update the equations.

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