This is the main repository of the NOMAD parser for BigDFT.

Standalone Installation

The parser is designed to be usable as a separate python package. Here is an example of the call syntax:

    from bigdftparser import bigdftparser
    import matplotlib.pyplot as mpl

    # 0. Initialize a parser by giving a path to the BigDFT output file and a list of
    # default units
    path = "path/to/main.file"
    default_units = ["eV"]
    parser = bigdftparser(path, default_units=default_units)

    # 1. Parse
    results = parser.parse()

    # 2. Query the results with using the id's created specifically for NOMAD.
    scf_energies = results["energy_total_scf_iteration"]
    mpl.plot(scf_energies)
    mpl.show()

To install this standalone version, you need to first clone the git@gitlab.mpcdf.mpg.de:nomad-lab/python-common.git repository and the git@gitlab.mpcdf.mpg.de:nomad-lab/nomad-meta-info.git repository into the same folder. Then install the python-common package according to the instructions found in the README. After that, you can install this package by running either of the following two commands depending on your python version:

python setup.py develop --user  # for python2
python3 setup.py develop --user # for python3

Scala access

The scala layer in the Nomad infrastructure can access the parser functionality through the scalainterface.py file, by calling the following command:

    python scalainterface.py path/to/main/file

This scala interface is in it's own file to separate it from the rest of the code.

Support of different versions

The parser is designed to support multiple versions of BigDFT with a DRY approach: The initial parser class is based on BigDFT 1.8.0, and other versions will be subclassed from it. By sublassing, all the previous functionality will be preserved, new functionality can be easily created, and old functionality overridden only where necesssary.

Developer Info

This section describes some of the guidelines that are used in the development of this parser.

Documentation

This parser tries to follow the google style guide for documenting python code. Documenting makes it much easier to follow the logic behind your parser.

Testing

The parsers can become quite complicated and maintaining them without systematic testing is impossible. There are general tests that are performed automatically in the scala layer for all parsers. This is essential, but can only test that the data is outputted in the correct format and according to some general rules. These tests cannot verify that the contents are correct.

In order to truly test the parser output, regression testing is needed. The tests for this parser are located in the regtest folder. Tests provide one way to test each parseable quantity and python has a very good library for unit testing. When the parser supports a new quantity it is quite fast to create unit tests for it. These tests will validate the parsing, and also easily detect bugs that may rise when the code is modified in the future.

Profiling

The parsers have to be reasonably fast. For some codes there is already significant amount of data in the NoMaD repository and the time taken to parse it will depend on the performance of the parser. Also each time the parser evolves after system deployment, the existing data may have to be reparsed at least partially.

By profiling what functions take the most computational time and memory during parsing you can identify the bottlenecks in the parser. There are already existing profiling tools such as cProfile which you can plug into your scripts very easily.