High-contrast Imaging Plotting library
Project description
hciplot
HCIplot -- High-contrast Imaging Plotting library. The goal of this
library is to be the "Swiss army" solution for plotting and visualizing
multi-dimensional high-contrast imaging datacubes on Jupyter lab.
While visualizing FITS files is straightforward with SaoImage DS9 or any
other FITS viewer, exploring the content of an HCI datacube as an
in-memory numpy array (for example when running your Jupyter session
on a remote machine) is far from easy.
HCIplot contains two functions, plot_frames and plot_cubes,
and relies on the matplotlib and HoloViews libraries and
ImageMagick. With HCIplot you can:
- plot a single 2d array or create a mosaic of several 2d arrays,
- annotate save publication ready images,
- visualize 2d arrays as surface plots,
- create interactive plots when handling 3d or 4d arrays (thanks to
HoloViews, - save to disk a 3d array as an animation (gif or mp4).
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