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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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List extensions, part II

Anonym

(Note: This is the second part of Ron Kneusel’s discussion of his extensions to the IDL 8 list datatype.) At this point, we have extended the List class in several ways. Now, let's take a quick look at using the List class to do some symbolic math in IDL. Download the files dx_plot.pro and dx.pro. Since they use both list_extensions.pro and lisp.pro, make sure you have these, as well. This small application accepts a symbolic expression as an S-expression (the Function) and calculates the first derivative symbolically; it then plots the derivative. Before we start, note that DX_PLOT is an example of how to create an IDL application using objects. Now, let's consider a plot of the derivative of:

y = 3 cos(5x2)

which as an S-expression is written as:

(* 3 (cos (* 5 (^ x 2))))

The derivative is computed in the function DX (in dx.pro) which accepts a list representing the entered S-expression, calls DX_DERIV and then passes the derivative output list to DX_INFIX which converts it to a fully parenthesized infix expression. No simplification of the output of DX_DERIV is performed, but this does not matter since IDL will interpret it properly anyway. (For the moment we are ignoring how this happens, but see the file lambda.pro. More on LAMBDA in a future post.) The derivative is found using the standard rules:

 function dx_deriv, f compile_opt idl2 on_error, 2 case 1 of (~is_list(f)) : ans = (size(f,/type) ne 7) ? '0' : (f eq 'x') ? '1' : '0' (f[0] eq '+') : ans = list('+', dx_deriv(f[1]), dx_deriv(f[2])) (f[0] eq '-') : ans = list('-', dx_deriv(f[1]), dx_deriv(f[2])) (f[0] eq '*') : ans = list('+', list('*', dx_deriv(f[1]), f[2]), list('*', f[1], dx_deriv(f[2]))) (f[0] eq '/') : ans = list('/', list('-', list('*', dx_deriv(f[1]), f[2]), list('*', dx_deriv(f[2]), f[1])), list('*', f[2], f[2])) (f[0] eq 'sin') : ans = list('*', list('cos', f[1]), dx_deriv(f[1])) (f[0] eq 'cos') : ans = list('*', list('_', list('sin', f[1])), dx_deriv(f[1])) (f[0] eq 'tan') : ans = list('*', list('sec^2', f[1]), dx_deriv(f[1])) (f[0] eq 'exp') : ans = list('*', list('exp', f[1]), dx_deriv(f[1])) (f[0] eq 'ln') : ans = list('/', dx_deriv(f[1]), f[1]) (f[0] eq '_') : ans = list('_', dx_deriv(f[1])) (f[0] eq '^') : ans = list('*', list('*', f[2], list('^', f[1], list('-', f[2], 1))), dx_deriv(f[1])) else: ans = 'Syntax error!' endcase return, ans end

Note here how the IDL CASE statement is being used. In this form it operates exactly like the Lisp (cond ...) function which allows us to test conditions sequentially. Note also the recursive nature of the calls which automatically handle evaluating sublists and that we are using '_' (underscore) as negation reserving '-' (minus) exclusively for subtraction. Conversion to infix is done with DX_INFIX:

 function dx_infix, f compile_opt idl2 on_error, 2 case 1 of (~is_list(f)) : ans = f (f[0] eq '+') : ans = list(dx_infix(f[1]), '+', dx_infix(f[2])) (f[0] eq '-') : ans = list(dx_infix(f[1]), '-', dx_infix(f[2])) (f[0] eq '*') : ans = list(dx_infix(f[1]), '*', dx_infix(f[2])) (f[0] eq '/') : ans = list(dx_infix(f[1]), '/', dx_infix(f[2])) (f[0] eq '_') : ans = list('-', dx_infix(f[1])) (f[0] eq 'sin') : ans = list('sin(', dx_infix(f[1]),')') (f[0] eq 'cos') : ans = list('cos(', dx_infix(f[1]),')') (f[0] eq 'tan') : ans = list('tan(', dx_infix(f[1]),')') (f[0] eq 'sec^2') : ans = list('(1.0/cos(', dx_infix(f[1]),'))^2') (f[0] eq 'exp') : ans = list('exp(', dx_infix(f[1]),')') (f[0] eq 'ln') : ans = list('log(', dx_infix(f[1]),')') (f[0] eq '^') : ans = list(dx_infix(f[1]), '^', dx_infix(f[2])) else: ans = 'Syntax error!' endcase return, ans end

This function also makes use of the "cond" CASE statement and recursion. Lastly, the plot of the derivative is shown with the DX_PLOT application: Ron Kneusel's DX_PLOT window Since the derivative of y = 3cos(5x2) is, as an S-expression:

 ( (0 * (cos( (5 * (x ^ 2)) ))) + (3 * ( (- (sin( (5 * (x ^ 2)) ))) * ( (0 * (x ^ 2)) + (5 * ( (2 * (x ^ (2 - 1))) * 1))))))

It is not hard to add easy simplification rules to the output to remove things like multiply by 0 and 1. We leave this as an exercise for the reader. :) We have given here a potpourri of List examples. We hope that they are useful to you and encourage you to explore the power of IDL's List (and Hash!) classes. In a future post we will use List in implementing higher-order functions which brings some of the power of functional programming to IDL.

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