add Code of Conduct

CODE_OF_CONDUCT.md

@@ -0,0 +1,42 @@
+# Clawpack Community NumFOCUS Code of Conduct
+
+As a [NumFOCUS](https://numfocus.org) affiliated project we have elected to
+adopt the NumFOCUS code of conduct.
+
+## The Short Version
+
+NumFOCUS and the Clawpack community is dedicated to providing a
+harassment-free community for everyone, regardless of gender, sexual
+orientation, gender identity and expression, disability, physical appearance,
+body size, race, or religion. We do not tolerate harassment of community
+members in any form.
+
+Be kind to others. Do not insult or put down others. Behave professionally.
+Remember that harassment and sexist, racist, or exclusionary jokes are not
+appropriate for NumFOCUS or Clawpack.
+
+All communication should be appropriate for a professional audience including
+people of many different backgrounds. Sexual language and imagery is not
+appropriate.
+
+Thank you for helping make this a welcoming, friendly community for all.
+
+## Long Version
+
+You can find the long version of the Code of Conduct on the NumFOCUS website 
+https://numfocus.org/code-of-conduct.
+
+## How To Report
+
+If you feel that the Code of Conduct has been violated, feel free to submit a
+report, by using the form: [NumFOCUS Code of Conduct Reporting Form](https://numfocus.typeform.com/to/ynjGdT?typeform-source=numfocus.org). 
+
+## Who Will Receive Your Report
+
+Your report will be received and handled by NumFOCUS Code of Conduct Working
+Group; trained, and experienced contributors with diverse backgrounds. The
+group is making decisions independently from the Clawpack project, PyData,
+NumFOCUS or any other organization. 
+
+You can learn more about the current group members, as well as the reporting
+procedure [HERE](https://numfocus.org/code-of-conduct).

README.md

@@ -1,8 +1,17 @@
 # AMRClaw Package
+
+This package hosts the Adaptive Mesh Refinement version of the Clawpack code.  
+
+See the main [clawpack/clawpack repository](https://github.com/clawpack/clawpack)
+ README for more links.  
+
+See the [Documentation](https://www.clawpack.org/)
+for more details and installation instructions.
+
 The AMRClaw version of Clawpack provides Adaptive Mesh Refinement (AMR) capabilities in 2 and 3 space dimensions. (The two-dimensional code can also be used for 1-dimensional problems, see [AMRClaw for 1d problems](https://www.clawpack.org/amrclaw1d.html#amrclaw-1d).)
 
 Block-structured AMR is implemented, in which rectangular patches of the grid at level `L` are refined to level `L+1`.  The general algorithms are described in [BergerLeVeque98](https://doi.org/10.1137/S0036142997315974).
 
 **Links**
- - [Documentation](https://www.clawpack.org/amrclaw.html)
+ - [Documentation for AMRClaw](https://www.clawpack.org/amrclaw.html)
  - [![Tests](https://github.com/clawpack/amrclaw/actions/workflows/testing.yml/badge.svg)](https://github.com/clawpack/amrclaw/actions/workflows/testing.yml)