<commit_before>astropy:
  name: Astropy
  logo: astropy.png
  url: http://www.astropy.org/
  repositories:
    github: astropy/astropy
  mailinglist:
    users: http://mail.python.org/mailman/listinfo/astropy
    devs: http://groups.google.com/group/astropy-dev
  chat:
    slack: http://joinslack.astropy.org/
  description: >-
    is a community-driven package intended to contain much of the core
    functionality and some common tools needed for performing
    astronomy and astrophysics with Python.

astronomy-commons:
  name: Astronomy Data Commons
  logo: astronomy-commons.png
  url: https://dirac.astro.washington.edu/data-engineering/
  repositories:
    github: astronomy-commons/
  mailinglist:
    devs: https://groups.google.com/forum/#!forum/astronomy-commons
  chat:
    matrix: https://riot.im/app/#/room/#astronomy-commons:matrix.org 
  description: >-
    is an initiative for software infrastructure for science platforms and
    scalable astronomy on cloud resources. Astronomy Data Commons is lead by
    researchers at the DiRAC Institute at the University of Washington.

ctlearn:
   name: CTLearn
   logo: ctlearn.png
   url: https://github.com/ctlearn-project
   repositories:
     github: ctlearn-project/ctlearn
   description: >-
      is a package under active development that pursues the
      application of deep-learning based methods to the analysis of
      data from imaging atmospheric Cherenkov telescopes (IACTs). <a
      href="https://github.com/ctlearn-project">CTLearn</a> includes
      modules for loading and manipulating IACT data and for running
      machine learning models using pixel-wise camera data as
      input. Its high-level interface provides a
      configuration-file-based workflow to drive reproducible training
      and prediction.
   chat:
     slack: https://ctlearn.slack.com

einsteinpy:
  name: EinsteinPy
  logo: einsteinpy.png
  url: https://einsteinpy.org
  repositories:
    github: einsteinpy/einsteinpy
  mailinglist:
    devs: https://groups.io/g/einsteinpy-dev
  chat:
    matrix: https://riot.im/app/#/room/#einsteinpy:matrix.org
  description: >-
    is a python package for solving problems in general relativity. Computations
    can be done for Schwarzschild, Kerr and Kerr-Newman geometries. Visualising
    relativistic orbits, advancement of perihelion can be simulated in seconds.
    See <a href="https://docs.einsteinpy.org/">documentation</a> for more
    information.
<commit_msg>added matrix channel for the ctlearn suborg<commit_after>astropy:
  name: Astropy
  logo: astropy.png
  url: http://www.astropy.org/
  repositories:
    github: astropy/astropy
  mailinglist:
    users: http://mail.python.org/mailman/listinfo/astropy
    devs: http://groups.google.com/group/astropy-dev
  chat:
    slack: http://joinslack.astropy.org/
  description: >-
    is a community-driven package intended to contain much of the core
    functionality and some common tools needed for performing
    astronomy and astrophysics with Python.

astronomy-commons:
  name: Astronomy Data Commons
  logo: astronomy-commons.png
  url: https://dirac.astro.washington.edu/data-engineering/
  repositories:
    github: astronomy-commons/
  mailinglist:
    devs: https://groups.google.com/forum/#!forum/astronomy-commons
  chat:
    matrix: https://riot.im/app/#/room/#astronomy-commons:matrix.org 
  description: >-
    is an initiative for software infrastructure for science platforms and
    scalable astronomy on cloud resources. Astronomy Data Commons is lead by
    researchers at the DiRAC Institute at the University of Washington.

ctlearn:
   name: CTLearn
   logo: ctlearn.png
   url: https://github.com/ctlearn-project
   repositories:
     github: ctlearn-project/ctlearn
   chat:
     matrix: https://riot.im/app/#/room/#ctlearn:matrix.org
   description: >-
      is a package under active development that pursues the
      application of deep-learning based methods to the analysis of
      data from imaging atmospheric Cherenkov telescopes (IACTs). <a
      href="https://github.com/ctlearn-project">CTLearn</a> includes
      modules for loading and manipulating IACT data and for running
      machine learning models using pixel-wise camera data as
      input. Its high-level interface provides a
      configuration-file-based workflow to drive reproducible training
      and prediction.

einsteinpy:
  name: EinsteinPy
  logo: einsteinpy.png
  url: https://einsteinpy.org
  repositories:
    github: einsteinpy/einsteinpy
  mailinglist:
    devs: https://groups.io/g/einsteinpy-dev
  chat:
    matrix: https://riot.im/app/#/room/#einsteinpy:matrix.org
  description: >-
    is a python package for solving problems in general relativity. Computations
    can be done for Schwarzschild, Kerr and Kerr-Newman geometries. Visualising
    relativistic orbits, advancement of perihelion can be simulated in seconds.
    See <a href="https://docs.einsteinpy.org/">documentation</a> for more
    information.

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