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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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Gum Drop Plot

Anonym

On our IDL page, there is an image that displays a plot made out of a series of colorful spheres. Based on my research, this plot was generated a long time ago most likely using IDL Object Graphics.

I wanted to generate a similar plot using the IDL 8 Graphics Functions. To do this, I used the SCATTERPLOT3D routine, the ORB object (provided with the IDL distribution but not documented), and the POLYLINE routine.  An example of the output is shown below:

 

 

The data used to produce this plot is randomly generated. Therefore, if you run the code, the output will look different each time. The code used to generate this plot is shown below:

pro gum_drop

  compile_opt idl2
 
  ;Generate some random data to
  ;plot
  x = RANDOMU(seed, 10)
  y = RANDOMU(seed, 10)
  z = RANDOMU(seed, 10)
 
  ;Draw an initail scatter
  ;plot with symbolsize of 1    
  scat_plot = scatterplot3d(x,y,z,RGB_TABLE=2, $
              SYM_OBJECT=orb(),$ ;use an orb object as symbol
              SYM_SIZE=1, $ ;Set symbol size to 1
              MAGNITUDE=z, $ ;change color with Z value
              /SYM_FILLED, clip=0,$ ;Fill symbols and no clipping
              xticklen=0, yticklen=0, zticklen=0, $ ;remove ticks
              xsubticklen=0, ysubticklen=0, zsubticklen=0, $ ;remove ticks
              xmajor=5, ymajor=5, $ ;Only use 5 ticks on each axis
              xrange=[0,1], yrange=[0,1],$ ;force the x and y range
              ASPECT_RATIO=1.0,$  ;Don't distort the image
              DEPTH_CUE=[0,4], $ ;Make things farther away fade
              AXIS_STYLE=2, $  ;Make the axis a box
              background_color = 'light yellow')
 
 
  ;Draw 9 more plots where the symbol size
  ;changes each plot
  for ind = 2L, 10 do begin
   z = RANDOMU(seed, 10)
   scat_plot_loop = scatterplot3d(x,y,z,$
      RGB_TABLE=2, SYM_OBJECT=orb(),SYM_SIZE=ind/2, $
      MAGNITUDE=z, /SYM_FILLED, clip=0, /OVERPLOT)
  endfor

  ;Generate polygons to create a grid
  ;on the Z-Y and Z-X planes
  x = [0.25,0.25,0.5,0.5,0.75,0.75]
  y = [0.999,0.999,0.999,0.999,0.999,0.999]
  z = [0.00,1.00,0.0,1.0,0.00,1.00]

  ;Connect every 2 points in polygon data
  ;with lines using the CONNECTIVITY keyword

  con = [2,0,1,2,2,3,2,4,5]

  poly0 = polyline(x,y,z,/DATA,CONNECTIVITY=con)
 
  temp = x
  x=y
  y=temp
 
  poly1 = polyline(x,y,z,/DATA,CONNECTIVITY=con)
 
  temp = z
  z = y
  y = temp
 
  poly2 = polyline(x,y,z,/DATA,CONNECTIVITY=con)
 
  temp = x
  x=y
  y=temp
 
  poly3 = polyline(x,y,z,/DATA,CONNECTIVITY=con)
 
  ;Remove the axis from the front of
  ;the plot
  ax = scat_plot.AXES
  ax[2].hide=1
  ax[6].hide=1
  ax[7].hide=1
  ax[3].ticklen=1.0
  ax[1].ticklen=1.0
 
end

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