matplotlib/mpl_data_containers

Domain specific/interesting end goal use cases

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While initial development work will focus more heavily on synthetic data/toy examples of some relatively low-level artists, it is a good idea to keep in mind a wide range of applications, as that is the actual end goal that makes this work actually useful.

In particular the following, in no particular order:

  • Oceanography/Geospatial data
    • large datasets, subsampling data
    • transforms into map coordinates
    • integrations with cartopy, etc.
  • Astronomy data
    • It is a NASA funded grant, after all
    • large datasets
    • stress test units
    • spatial-type data
    • integration with data sources used in that domain
  • Biological data
    • Of particular interest to CZI grant
    • Microscopy data/images
  • Spectroscopy data
    • It is my own area of expertise, I have several kinds of plots that serve a variety of levels of difficulty
    • Specialized domain-specific data format
    • Composing multiple artists
    • particularly hard units support (spectroscopists can never agree what units to use, and like to say that length and energy units are interoperable)
    • easy "quick" plots from a self describing data format
    • multidimensional data, slicing into, etc.
    • interactivity, stress testing the level of hooks provided to modify the plot
  • Sports analytics data
    • relatively unique visualizations
    • see https://hockeyviz.com for many examples of a wide variety of plot types (made with matplotlib)
    • potential interest for live updating

These are just a few of the domains for which this dataset-centric approach may be useful, feel free to add more.