https://github.com/mycarta/reproducing-exponential-grayscale-cmap

Reproducing an exponential grayscale cmap
https://github.com/mycarta/reproducing-exponential-grayscale-cmap

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Reproducing an exponential grayscale cmap

• Host: GitHub
• URL: https://github.com/mycarta/reproducing-exponential-grayscale-cmap
• Owner: mycarta
• Created: 2019-10-20T02:41:54.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-05-22T03:24:26.000Z (over 4 years ago)
• Last Synced: 2025-01-22T08:10:05.677Z (2 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 2.26 MB
• Stars: 1
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Reproducing an exponential grayscale cmap

What I learned in the process of reproducing [Figure 1](https://images.app.goo.gl/aGeNcomJVExzwYJs8) from the paper "Perception of visual information: The role of colour in seismic interpretation", by Froner et alii, First Break, March 2013.

And giving some general advice for how to go about it:

- Get permission. Ask for permission to use the data, if the data is not open, or if uncertain; if it's only a figure, ask for permission to show the original with the reproduced one

- Do the math. Define, study and test the equations / functions and other computations necessary to replicate the figure

- Get the plots right. Figure out the specific plotting stuff to replicate the figure (and improve it if necessary)

- Extra work. Go further, add interactivity for others to experiment with it

- Test the results. Test on the data that come with the paper, if possible, or other data (model and real)

- Pay back. Share your results. It with a permissive license, ideally CC-BY

#### Interactive notebooks:

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/mycarta/Reproducing-exponential-grayscale-cmap/master)