Skip to main content

Build, access, and explore a NEPC database.

Project description

NEPC

Build Status Documentation Status GitHub

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:

>>> 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.

Source Distribution

nepc-2020.7.22.tar.gz (30.2 kB view details)

Uploaded Source

Built Distribution

nepc-2020.7.22-py2.py3-none-any.whl (227.0 kB view details)

Uploaded Python 2Python 3

File details

Details for the file nepc-2020.7.22.tar.gz.

File metadata

  • Download URL: nepc-2020.7.22.tar.gz
  • Upload date:
  • Size: 30.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.2.0 requests-toolbelt/0.9.1 tqdm/4.48.0 CPython/3.7.2

File hashes

Hashes for nepc-2020.7.22.tar.gz
Algorithm Hash digest
SHA256 89d03c70753023f5ccbdc6250c75be525556f3f5058e3ac118d22a071428139f
MD5 23437c84d51b4fc4322282ebd8c38316
BLAKE2b-256 7511ec5f5e0af11c458e0b7defba884604e2d4cf2d49c3532286ec403307a4d9

See more details on using hashes here.

File details

Details for the file nepc-2020.7.22-py2.py3-none-any.whl.

File metadata

  • Download URL: nepc-2020.7.22-py2.py3-none-any.whl
  • Upload date:
  • Size: 227.0 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.2.0 requests-toolbelt/0.9.1 tqdm/4.48.0 CPython/3.7.2

File hashes

Hashes for nepc-2020.7.22-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 c7116235257e61419a1dbd935dba56261647d882d1364af8bd77e3011c237598
MD5 0caff43aef8166776836d76242ae7d00
BLAKE2b-256 651a08093e12d8c96f0281e2feca36b6ff5a96fddbf7142a7800642fae2f6a54

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page