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Aim

Parse multiple excel sheets containing common information but with different layouts into a data store

Roadmap

  • @TODO 2017-10-04 set-up repo, anonymise first sheets
  • @TODO 2017-10-04 define spec

Installation and usage

This tool is designed to be installed within a Python virtual environment. Once you have set up a (Python 3) virtualenv (or conda environment) you can install all dependencies and the package itself with:

pip install -r requirements/local.txt
pip install -e .

This is a 'developer' install for people working on the package. End users can instead use:

pip install -r requirements/base.txt
pip install .

The package provides two command line programs for ingesting and displaying procedure data:

  • xlsmunch [-h] [--verbose] xls_file ingests a single Excel file
  • dump_proc_db displays a summary of each entry in the database

Database setup

Before running for the first time, set up a postgres user and databases for the muncher to write to:

createuser xlsmuncher
createdb -O xlsmuncher xlsmuncher
createdb -O xlsmuncher xlsmuncher_test

Then ensure you have the following environment variables set whenever running:

export XLSMUNCHER_DB="postgresql://xlsmuncher:@localhost/xlsmuncher"
export XLSMUNCHER_TEST_DB="postgresql://xlsmuncher:@localhost/xlsmuncher_test"

If the database structure has changed since you last ran, you will need to drop and re-create the database (in due course we'll set up automatic data migrations to avoid this):

dropdb xlsmuncher
createdb -O xlsmuncher xlsmuncher

Running tests

From within the main project folder, simply run pytest.

To regenerate reference data, run pytest --regen.

Navigation

-|
 |-data
 |  |-anonymised (raw excel sheets)
 |-xlsmuncher (python package)