Skip to main content

Build, access, and explore a NEPC database.

Project description

NEPC

Build Status Documentation Status GitHub DOI

The goals of the nepc project are to provide tools to:

  1. parse, evaluate, and populate metadata for electron scattering cross sections;
  2. build a NEPC MySQL database of cross sections;
  3. curate, access, visualize, and use cross section data from a NEPC database; and
  4. support verification and validation of electron scattering cross section data.

The database schema and Python module are designed for anyone interested in plasma chemistry with a background in physics at the graduate level.

Documentation for the nepc project: https://nepc.readthedocs.io.

Organization

The project is organized in the following directories:

  • tests - unit and integration testing
  • tests/data - data directory for the nepc_test database--an example NEPC database containing fictitious electron scattering cross section data used in unit and integration testing
  • tests/data/eda - example exploratory data analysis (EDA) of a NEPC database that is possible with the nepc Python module
  • tests/data/methods - code used to parse fictitious cross section data in LXCat format and create various NEPC Models for the nepc_test database
  • docs - files used by Sphinx to generate the NEPC documentation
  • nepc - the Python code for the nepc package and building a NEPC database
  • nepc/mysql - the Python code for creating a NEPC database from data in $NEPC_DATA_HOME; also creates the nepc_test database from data in $NEPC_HOME/tests/data

Getting Started

To install nepc with pip, run:

$ pip install nepc

Establish a connection to the database named nepc running on a production server (you must set an environment variable $NEPC_PRODUCTION that points to the production server):

>>> cnx, cursor = nepc.connect()

If you've built the nepc_test database on your local machine (see instructions here), establish a connection to it:

>>> cnx, cursor = nepc.connect(local=True, test=True)

Access the pre-defined plasma chemistry model, fict_min2, in the nepc_test database:

>>> fict_min2 = nepc.Model(cursor, "fict_min2")

Print a summary of the fict_min2 model, including a stylized Pandas dataframe:

>>> fict_min2.summary()

Plot the cross sections in fict_min2.

>>> fict_min2.plot(ylog=True, xlog=True, width=8, height=4) 

Additional examples of EDA using nepc are in tests/data/eda. Examples of methods for building data files for the nepc_test database, including parsing LXCat formatted data, are in tests/data/methods.

Built With

Pronunciation

NEPC rhymes with the loser of the Cola War. If NEPC were in the CMU Pronouncing Dictionary, its entry would be N EH P S IY ..

Approved for public release, distribution is unlimited.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for nepc, version 2020.8.28
Filename, size File type Python version Upload date Hashes
Filename, size nepc-2020.8.28-py2.py3-none-any.whl (282.4 kB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size nepc-2020.8.28.tar.gz (30.5 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page