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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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Positioning plots

Anonym

Last week, I gave an example of creating a multi-panel plot (or multiplot) using the LAYOUT keyword. Today (with a nod to a comment from Paul Young), I'll show an alternate technique using the POSITION keyword. The Solar Physics Group at NASA's Marshall Space Flight Center provides information on the sunspot cycle. You can download the group's monthly sunspot number data—dating back to 1749!—here (TXT). I'd like to display, side-by-side, a plot of the sunspot series since 1970 and a histogram of the series. Start by reading the data from the file, assuming it's included in your IDL path. I prefer the astrolib READCOL procedure for reading text files.

 file = file_which('spot_num.txt') readcol, file, year, month, sunspots

Next, create a time vector and use it to restrict our analysis to sunspot activity since 1970:

 time = year + (month-1.0)/12.0 i_ge1970 = where(time ge 1970.0, /null) time_recent = time[i_ge1970] sunspots_recent = sunspots[i_ge1970]

Now use HISTOGRAM to calculate a discrete frequency distribution of the sunspot numbers since 1970:

 sunspot_histogram = histogram(sunspots_recent, $ binsize=10, $ locations=sunspot_bins)

I've empirically chosen a bin size of 10, and returned the locations of the bins into the variable sunspot_bins. The first plot, positioned on the left, is of the sunspot series:

 xr = minmax(time_recent) yr = minmax(sunspots_recent) series = plot(time_recent, sunspots_recent, $ dimensions=[800,600], $ position=[0.10, 0.15, 0.75, 0.90], $ xrange=xr, yrange=yr, $ xtitle='Year', ytitle='Sunspots', $ title='Sunspot Activity (1970-present)')

The key in this call to PLOT is the POSITION keyword: this four-element array describes the lower left [0.10, 0.15] and upper right [0.75,0.90] corners of the bounding box of the plot, in normalized coordinates. The plot fits within this box. I've also used the convenient astrolib MINMAX function to set up axis ranges for the plot, and the DIMENSIONS property to set the size of the plot window, in pixels. The second plot, positioned to the right of the first, displays the histogram of the sunspot series:

 histoplot = plot(sunspot_histogram, sunspot_bins, $ position=[0.80, 0.15, 0.95, 0.90], /current, $ /histogram, $              ; IDL 8.2.1 yrange=series.yrange, $    ; match yrange of first plot ymajor=0, $                ; no axis text /fill_background, fill_color='light gray', $ xtitle='Frequency', title='Histogram')

Swapping the order of the parameters to PLOT transposes the histogram. POSITION is used again to position the plot. CURRENT ensures this plot appears in the same window as series. The HISTOGRAM property tells IDL to draw discrete blocks instead of point-to-point lines (hi, Haje!). Here's my result: Sunspot series and histogram

I like this pair of plots because scanning horizontally across the time series gives a visual estimate of the histogram displayed on the right. Next week, I'll perform some simple time series analyses on these data.

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