How to write a parser¶

NOMAD uses parsers to convert raw code input and output files into NOMAD's common archive format. This is the documentation on how to develop such a parser.

Getting started¶

Let's assume we need to write a new parser from scratch.

First we need to install the nomad-lab Python package to get the necessary libraries. See how to get started for the installation guide:

pip install nomad-lab

We prepared an example parser in a github repository to learn how to write parsers. Clone it by:

git clone https://github.com/nomad-coe/nomad-parser-example.git --branch hello-world

Alternatively, you can fork the example project on GitHub to create your own parser. Clone your fork accordingly.

The project structure should be:

├── example
│   ├── exampleparser
│   │   ├── __init__.py
│   │   ├── __main__.py
│   │   ├── metainfo.py
│   │   ├── parser.py
│   ├── LICENSE.txt
│   ├── README.md
│   ├── setup.py

Next, you should install your new parser with pip. The -e parameter installs the parser in development. This means you can change the sources without having to reinstall:

cd example
pip install -e .

The main parser class is found in exampleparser/parser.py:

class ExampleParser:
    def parse(self, mainfile: str, archive: EntryArchive, logger):
        # Log a hello world, just to get us started. TODO remove from an actual parser.
        logger.info('Hello World')

        run = Run()
        archive.run.append(run)
        run.program = Program(name='EXAMPLE')

A parser is a simple Python module containing a single class. For simulation, the convention for the class name is <CodeName>Parser e.g. VASPParser. It has a main function, parse which takes the path to the mainfile and an empty EntryArchive object as input to be populated with the parsed quantities. The development of parsers is up to each user, and will heavily depend on what the user wants to parse. In the simple example above, we created a logger info entry and populated the archive with a root section called Run. We then created the program section and set the program name to Example.

You can run the parser (see the included __main__.py) with the path to the file to be parsed as argument:

python -m exampleparser tests/data/example.out

The output show the log entry and the minimal archive with a run section and the respective program.name as in the following:

{
  "run": [
    {
      "program": {
        "name": "EXAMPLE"
      }
    }
  ]
}

Match your raw file¶

Attention

This part of the documentation should be more substantiated. There will be a section about this topic soon.

Parsing test files¶

We will now show you how to parse ASCII files containing some structure information, a typical output of simulation codes.

Check out the master branch of the exampleparser project,

git checkout master

and examine the file to be parsed in tests/data/example.out:

2020/05/15
               *** super_code v2 ***

system 1
--------
sites: H(1.23, 0, 0), H(-1.23, 0, 0), O(0, 0.33, 0)
latice: (0, 0, 0), (1, 0, 0), (1, 1, 0)
energy: 1.29372

*** This was done with magic source                                ***
***                                x°42                            ***


system 2
--------
sites: H(1.23, 0, 0), H(-1.23, 0, 0), O(0, 0.33, 0)
cell: (0, 0, 0), (1, 0, 0), (1, 1, 0)
energy: 1.29372

At the top there is some general information such as date, name of the code (super_code) and its version (v2). Then there are two systems sections (system 1 and system 2) separated with a string containing a code-specific value magic source. Each system section contains data about the atom positions (sites), the lattice information (latice), and a variable energy.

In order to convert the information from this file into the archive, we first have to parse the necessary quantities: the date, system, energy, etc. The nomad-lab Python package provides a text_parser module for declarative parsing of text files. You can define text file parsers as in the following:

def str_to_sites(string):
    sym, pos = string.split('(')
    pos = np.array(pos.split(')')[0].split(',')[:3], dtype=float)
    return sym, pos


calculation_parser = TextParser(quantities=[
    Quantity('sites', r'([A-Z]\([\d\.\, \-]+\))', str_operation=str_to_sites),
    Quantity(
        Atoms.lattice_vectors,
        r'(?:latice|cell): \((\d)\, (\d), (\d)\)\,?\s*\((\d)\, (\d), (\d)\)\,?\s*\((\d)\, (\d), (\d)\)\,?\s*',
        repeats=False),
    Quantity('energy', r'energy: (\d\.\d+)'),
    Quantity('magic_source', r'done with magic source\s*\*{3}\s*\*{3}\s*[^\d]*(\d+)', repeats=False)])

mainfile_parser = TextParser(quantities=[
    Quantity('date', r'(\d\d\d\d\/\d\d\/\d\d)', repeats=False),
    Quantity('program_version', r'super\_code\s*v(\d+)\s*', repeats=False),
    Quantity(
        'calculation', r'\s*system \d+([\s\S]+?energy: [\d\.]+)([\s\S]+\*\*\*)*',
        sub_parser=calculation_parser,
        repeats=True)
])

The quantities to be parsed can be specified as a list of Quantity objects in TextParser. Each quantity should have a name and a regular expression (re) pattern to match the value. The matched value should be enclosed in a group(s) denoted by (...). In addition, we can specify the following arguments:

findall (default=True) Switches simultaneous matching of all quantities using re.findall. In this case, overlap between matches is not tolerated, i.e. two quantities cannot share the same block in the file. If this cannot be avoided, set findall=False switching tore.finditer. This will perform matching one quantity at a time which is slower but with the benefit that matching is done independently of other quantities.
repeats (default=False) Switches finding multiple matches for a quantity. By default, only the first match is returned.
str_operation (default=None) An external function to be applied on the matched value to perform more specific string operations.
sub_parser (default=None) A nested parser to be applied on the matched block. This can also be a TextParser object with a list of quantities to be parsed or other FileParser objects.
dtype (default=None) The data type of the parsed value.
shape (default=None) The shape of the parsed data.
unit (default=None) The pint unit of the parsed data.
flatten (default=True) Switches splitting the parsed string into a flat list.
convert (default=True) Switches automatic conversion of parsed value.
comment (default=None) String preceding a line to ignore.

A metainfo.Quantity object can also be passed as first argument in place of name in order to define the data type, shape and unit for the quantity. `TextParser returns a dictionary of key-value pairs, where the key is defined by the name of the quantities and the value is based on the matched re pattern.

To parse a file, simply do:

mainfile_parser.mainfile = mainfile
mainfile_parser.parse()

This will populate the mainfile_parser object with parsed data and it can be accessed like a Python dict with quantity names as keys or directly as attributes:

run = Run()
run.program = Program(
  name='super_code', version=mainfile_parser.get('program_version'))
date = datetime.datetime.strptime(mainfile_parser.date, '%Y/%m/%d')
run.program_compilation_datetime = date.timestamp()

for calculation in mainfile_parser.get('calculation', []):
    system = System(atoms=Atoms())

    system.atoms.lattice_vectors = calculation.get('lattice_vectors')
    sites = calculation.get('sites')
    system.atoms.labels = [site[0] for site in sites]
    system.atoms.positions = [site[1] for site in sites]
    run.system.append(system)

    calc = Calculation(energy=Energy())
    calc.system_ref = system
    calc.energy.total = EnergyEntry(value=calculation.get('energy') * units.eV)
    magic_source = calculation.get('magic_source')
    if magic_source is not None:
        calc.x_example_magic_value = magic_source
    run.calculation.append(calc)
archive.run.append(run)

When the parser is run on the given example file:

python -m exampleparser tests/data/example.out

you should get a more comprehensive archive with all the provided information..

Extending the Metainfo¶

The NOMAD Metainfo defines the schema of each archive. There are predefined schemas for both simulation nomad.datamodel.metainfo.simulation and experimental data nomad.datamodel.metainfo.eln. The sections Run, System, and Calculation in the example are taken from the simulation metainfo definitions. While this covers most of the data usually provided in code input/output files, some data are typically community-specific and applies only to a certain type of codes or methodologies.For these cases, we allow for the extension of the definitions like this (exampleparser/metainfo.py):

# We extend the existing common definition of a section "Calculation"
class ExampleCalculation(Calculation):
    # We alter the default base class behavior to add all definitions to the existing
    # base class instead of inheriting from the base class
    m_def = Section(extends_base_section=True)

    # We define an additional example quantity. Use the prefix x_<parsername>_ to denote
    # non common quantities.
    x_example_magic_value = Quantity(type=int, description='The magic value from a magic source.')

Testing a parser¶

We developed an initial parser on some example data, and learned how to print out the output in an archive format. As a good software development practice, we have to add testing the parser for future maintenance and to ease the future development.

We use the Python unit test framework pytest. A typical test would take one example file, parse it, and check assertions about the output:

def test_example():
    parser = ExampleParser()
    archive = EntryArchive()
    parser.parse('tests/data/example.out', archive, logging)

    run = archive.run[0]
    assert len(run.system) == 2
    assert len(run.calculation) == 2
    assert run.calculation[0].x_example_magic_value == 42

You can run all tests in the tests directory like this:

python -m pytest -svx tests

You should define individual test cases with example files that demonstrate certain features of the underlying code/format.

Other FileParser classes¶

Aside from TextParser, other FileParser classes are also defined. These include:

DataTextParser uses the numpy.loadtxt function to load a structured data file. The loaded data can be accessed from property data.
XMLParser uses the ElementTree module to parse an XML file. The parse method of the parser takes in an XPath-style key to access individual quantities. By default, automatic data type conversion is performed, which can be switched off by setting convert=False.

Add the parser to NOMAD¶

NOMAD has to manage multiple parsers and must decide during processing which parsers to run on which files. To accomplish this, specific parser attributes are matched to a file. These are specified by interfacing the parser with MatchingParser. There are a couple of ways to do this, first as a plug-in (nomad.config.__init__.py::plugins) and second, directly adding it to the list of parsers (nomad.parsing.parsers.py::parsers), the former being the preferred route. See how to write parser plug-ins to learn more.

MatchingParserInterface(
    'parsers/example',
    mainfile_contents_re=(r'^\s*#\s*This is example output'),
    mainfile_mime_re=r'(application/.*)|(text/.*)',
    supported_compressions=["gz", "bz2", "xz"],
    mainfile_alternative=False,
    mainfile_contents_dict={'program': {'version': '1', 'name': 'EXAMPLE'}})

mainfile_mime_re: A regular expression on the MIME type of files. The parser is run only on files with matching MIME type. The MIME type is guessed with libmagic.
mainfile_contents_re: A regular expression that is applied to the first 4k characters of a file. The parser is run only on files where this matches.
mainfile_name_re: A regular expression that can be used to match against the name and path of the file.
supported compressions: A list of [gz, bz2] if the parser supports compressed files.
mainfile_alternative: If True, a file is mainfile unless another file in the same directory matches mainfile_name_re.
mainfile_contents_dict: A dictionary to match the contents of the file. If provided, it will load the file and match the value of the key(s) provided. One can also specify the keys that should be present by using the tags __has_key, __has_all_keys and __has_only_keys. For example, one can have {'program': {'__has_all_keys': ['version', 'name']}} to specify that version and name must be present in the file to be matched.

Not all of these attributes have to be used. Those that are given must all match in order to use the parser on a file.

The NOMAD infrastructure keeps a list of parser objects (in nomad/parsing/parsers.py::parsers). These parsers are considered in the order they appear in the list. The first matching parser is used to parse a given file.

While each parser project should provide its own tests, a test should be added to the infrastructure parser tests (tests/parsing/test_parsing.py) to guarantee that the processing runs through.

Once the parser is successfully installed and added, it becomes also available through the command line interface and normalizers are applied as well:

nomad parse tests/data/example.out

Developing an existing parser¶

A number of parsers are constantly being developed in NOMAD.

Description	Project url
electronic structure codes	https://github.com/nomad-coe/electronic-parsers.git
atomistic codes	https://github.com/nomad-coe/atomistic-parsers.git
workflow engines	https://github.com/nomad-coe/workflow-parsers.git
databases	https://github.com/nomad-coe/database-parsers.git

To refine an existing parser, you should install the parser via the nomad-lab package:

pip install nomad-lab

Clone the parser project:

git clone <parser-project-url>
cd <parser-dir>

Either remove the installed parser and pip install the cloned version:

rm -rf <path-to-your-python-env>/lib/python3.9/site-packages/<parser-module-name>
pip install -e .

Or set PYTHONPATH so that the cloned code takes precedence over the installed code:

PYTHONPATH=. nomad parse <path-to-example-file>

Alternatively, you can also do a full developer setup of the NOMAD infrastructure and enhance the parser there.