Metadata-Version: 2.1
Name: hciplot
Version: 0.1.2
Summary: High-contrast Imaging Plotting library
Home-page: https://github.com/carlgogo/hciplot
Author: Carlos Alberto Gomez Gonzalez
Author-email: carlosgg33@gmail.com
License: MIT
Keywords: plotting,hci,package
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Astronomy
Description-Content-Type: text/markdown
Requires-Dist: numpy (~=1.16)
Requires-Dist: matplotlib (>=2.2)
Requires-Dist: bokeh (~=1.0)
Requires-Dist: holoviews (~=1.11)

# 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).

