X

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 >

1345678910Last
7842 Rate this article:
No rating

Data Structure Analysis

Anonym

One of the main questions anybody using a programming language has to ask themselves is "what data structures should I be using?" This can be a complicated and difficult question as there are many trade-offs to consider. If it is desired to have a dynamic data type, IDL provides multiple options. In this analysis we will consider: dynamic arrays, lists, hashes, and ordered hashes and their ability to insert and delete elements. To start, let's review our data structures. Dynamic arrays are based on the ability for IDL arrays to resize themselves. For example:

array = [1,2,3,4]               ; Declaration

array = [array, 5]              ; Insert

array = [array[0:1],array[3:*]] ; Remove

 

Lists use the IDL object LIST:

list = LIST(1,2,3,4)    ; Declaration

list.add,5              ; Insert

list.remove, 2          ; Remove

 

Hashes use the IDL object HASH:

hash = HASH([1,2,3,4],[1,2,3,4]) ; Declaration

hash[5] = 5                      ; Insert

hash.remove, 2                   ; Remove

 

Ordered hashes use the IDL object ORDEREDHASH:

ohash = ORDEREDHASH([1,2,3,4],[1,2,3,4]) ; Declaration

ohash[5] = 5                             ; Insert

ohash.remove, 2                          ; Remove

 

For each data structure we will time how long it takes to insert and remove n elements (Note: the inserts/removals are done inside of a FOR loop, one insert/removal per iteration. This is done to simulate an application which expects a high degree of volatility in the use of their data structures. However, since IDL is a vectorized language, it is always best to try to group multiple operations into a single call). Please see the attached plots for the results of the runs. The results are what we would expect from a simple big-O analysis. Dynamic arrays are comparable for small input sizes, however, as soon as the size of the input grows, it becomes much faster to use a hash (either type) or a list. In terms of pure speed for any arbitrary input size, list  is the fastest. However, if you know your input bounded to a few elements, all of the proposed data structures can offer a similar performance.

Note: For this analysis, all the data structures had similar performance up to 10,000 elements. This is in no way a comprehensive test and the results may differ on your system. However, the rule of thumb I follow is, if your input is less than 10,000 elements choose the data structure which is the easiest for you to work with.

Please login or register to post comments.