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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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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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