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High-contrast Imaging Plotting library

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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 JupyterLab. 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. HCIplot allows to:

  • Plot a single frame (2d array) or create a mosaic of frames.


  • Annotate and save publication ready frames/mosaics.

  • 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).


You can install HCIplot with pip:

pip install hciplot

JupyterLab can be installed either with pip or with conda:

conda install -c conda-forge jupyterlab

The PyViz extension must be installed to display the holoviews widgets on JupyterLab:

jupyter labextension install @pyviz/jupyterlab_pyviz

If you want to create animations with plot_cubes you need to install ImageMagick with your system's package manager (e.g. brew if you are on MacOS or apt-get if you are on Ubuntu).

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