ejpcsvparser 0.4.0

EJP CSV parser for building article objects.

ejp-csv-parser

EJP CSV parser for building article objects.

This library reads CSV files containing article manuscript data, creates objects defined in the elifearticle library, and sets object properties from the CSV data values.

Currently it reads CSV files for data including: title, abstract, DOI, editor, authors, group authors, license, received date, subjects, keywords, research organisms, datasets, funding, and ethics.

The settings.py module defines file names, column names, angle bracket escape sequence, folder names, and similar settings which can be adjusted slightly if required.

The parse.py module is a good starting place to invoke the library, if given an article ID value, it can read the CSV files for data, create objects and set their properties for that particular article.

The csv_data.py module contains the logic for reading the CSV files, linking rows from multiple files by their index columns, escaping and converting some character encoding, and accounting for comma characters that are not intende to delimit data fields.

The objects instantiated by this library are used to generate a JATS XML file for a Publish on Accept (PoA) research article.

Requirements and install

a) Install from pypi package index

pip install ejpcsvparser

b) Install locally

Clone the git repo

git clone https://github.com/elifesciences/ejp-csv-parser.git

Create a python virtual environment and activate it

python3 -m venv venv
source venv/bin/activate

Install it locally

pip install -r requirements.txt
python setup.py install

In order to run the transform function as written, it will require strip-coverletter to be installed and ready to run locally, which will also require Docker to be installed and running.

Example usage

This library is meant to be integrated into another operational system, where the CSV files are downloaded from an S3 bucket and then processed. The test scenarios may provide more details about how it could be invoked, and the following example is a simple way to see how it works using interactive Python and using files from the "tests/test_data/" folder as the CSV input:

>>> from ejpcsvparser import parse
>>> article, error_count, error_messages = parse.build_article(21598)
>>> print(article.doi)
10.7554/eLife.21598
>>> print(article.title)
Cryo-EM structures of the autoinhibited <italic>E. coli</italic> ATP synthase in three rotational states

Run code tests

Use pytest for testing, install it if missing:

pip install pytest

Run tests

pytest

Run tests with coverage (install it if missing):

coverage run -m pytest

then report on code coverage

coverage report -m

License

Licensed under MIT.