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How to develop and publish plugins

We provide template projects on GitHub. You can fork these projects and follow the instructions in their These instructions will give you everything you need to run and test your plugin as a plugin developer.
How-to install a plugin section explains how to add plugins to a NOMAD installation. Dedicated Explanation sections provide more background information on what is a schema and what is a parser

Develop a schema plugin

Getting started

Fork and clone the schema example project as described in How-to install a plugin.

Writing schemas in Python compared to YAML schemas

In this guide, we explain how to write and upload schemas in the .archive.yaml format. Writing and uploading such YAML schemas is a good way for NOMAD users to add schemas. But it has limitations. As a NOMAD developer or Oasis administrator you can add Python schemas to NOMAD. All build in NOMAD schemas (e.g. for electronic structure code data) are written an Python and are part of the NOMAD sources (nomad.datamodel.metainfo.*).

There is a 1-1 translation between Python schemas (written in classes) and YAML (or JSON) schemas (written in objects). Both use the same fundamental concepts, like section, quantity, or sub-section, introduced in YAML schemas.

Starting example

from nomad.metainfo import MSection, Quantity, SubSection, Units

class System(MSection):
    A system section includes all quantities that describe a single simulated
    system (a.k.a. geometry).

    n_atoms = Quantity(
        type=int, description='''
        A Defines the number of atoms in the system.

    atom_labels = Quantity(
        type=MEnum(, shape['n_atoms'])
    atom_positions = Quantity(type=float, shape=['n_atoms', 3], unit=Units.m)
    simulation_cell = Quantity(type=float, shape=[3, 3], unit=Units.m)
    pbc = Quantity(type=bool, shape=[3])

class Run(MSection):
    section_system = SubSection(sub_section=System, repeats=True)

We define simple metainfo schema with two sections called System and Run. Sections allow to organize related data into, well, sections. Each section can have two types of properties: quantities and sub-sections. Sections and their properties are defined with Python classes and their attributes.

Each quantity defines a piece of data. Basic quantity attributes are type, shape, unit, and description.

Sub-sections allow to place section within each other, forming containment hierarchies or sections and the respective data within them. Basic sub-section attributes are sub_section(i.e. a reference to the section definition of the sub-section) and repeats (determines whether a sub-section can be included once or multiple times).

The above simply defines a schema. To use the schema and create actual data, we have to instantiate the above classes:

run = Run()
system = run.m_create(System)
system.n_atoms = 3
system.atom_labels = ['H', 'H', 'O']

print(n_atoms = 3)

Section instances can be used like regular Python objects: quantities and sub-sections can be set and accessed like any other Python attribute. Special meta-info methods, starting with m_ allow us to realize more complex semantics. For example m_create will instantiate a sub-section and add it to the parent section in one step.

Another example for an m_-method is:


This will convert the data into JSON:

    "m_def" = "Run",
    "systems": [
            "n_atoms" = 3,
            "atom_labels" = [


The following describes the schema language (the sum of all possible definitions) and how it is expressed in Python.

Common attributes of Metainfo Definitions

In the example, you have already seen the basic Python interface to the Metainfo. Sections are represented in Python as objects. To define a section, you write a Python classe that inherits from MSection. To define sub-sections and quantities you use Python properties. The definitions themselves are also objects derived from classes. For sub-sections and quantities, you directly instantiate :classSubSection and :classQuantity. For sections there is a generated object derived from :class:Section and available via m_def from each section class and section instance.

These Python classes, used to represent metainfo definitions, form an inheritance hierarchy to share common properties

  • name, each definition has a name. This is typically defined by the corresponding Python property. E.g. a sections class name, becomes the section name; a quantity gets the name from its Python property, etc.
  • description, each definition should have one. Either set it directly or use doc strings
  • links, a list of useful internet references.
  • more, a dictionary of custom information. Any additional kwargs set when creating a definition are added to more.


Sections are defined with Python classes that extend MSection (or other section classes).

  • base_sections are automatically taken from the base classes ofc the Python class.
  • extends_base_section is a boolean that determines the inheritance. If this is False, normal Python inheritance implies and this section will inherit all properties (sub-sections, quantities) from all base classes. If this is True, all definitions in this section will be added to the properties of the base class section. This allows the extension of existing sections with additional properties.


Quantity definitions are the main building block of meta-info schemas. Each quantity represents a single piece of data. Quantities can be defined by:

  • A type, that can be a primitive Python type (str, int, bool), a numpy data type (np.dtype('float64')), a MEnum('item1', ..., 'itemN'), a predefined metainfo type (Datetime, JSON, File, ...), or another section or quantity to define a reference type.
  • A shape that defines the dimensionality of the quantity. Examples are: [] (number), ['*'] (list), [3, 3] (3 by 3 matrix), ['n_elements'] (a vector of length defined by another quantity n_elements).
  • A physics unit. We use Pint here. You can use unit strings that are parsed by Pint, e.g. meter, m, m/s^2. As a convention the metainfo uses only SI units.


A sub-section defines a named property of a section that refers to another section. It allows to define that a section can contain another section.

  • sub_section (aliases section_def, sub_section_def) defines the section that can be contained.
  • repeats is a boolean that determines whether the sub-section relationship allows multiple section or only one.

References and Proxies

Beside creating hierarchies (e.g. tree structures) with subsections, the metainfo also allows to create cross references between sections and other sections or quantity values:

class Calculation(MSection):
    system = Quantity(type=System.m_def)
    atom_labels = Quantity(type=System.atom_labels)

calc = Calculation()
calc.system =[-1]
calc.atom_labels =[-1]

To define a reference, define a normal quantity and simply use the section or quantity you want to refer to as type. Then you can assign respective section instances as values.

In Python memory, quantity values that reference other sections simply contain a Python reference to the respective section instance. However, upon serializing/storing metainfo data, these references have to be represented differently.

Value references are a little different. When you read a value reference, it behaves like the reference value. Internally, we do not store the values, but a reference to the section that holds the referenced quantity. Therefore, when you want to assign a value reference, use the section with the quantity and not the value itself.

References are serialized as URLs. There are different types of reference URLs:

  • #/run/0/calculation/1, a reference in the same Archive
  • /run/0/calculation/1, a reference in the same archive (legacy version)
  • ../upload/archive/mainfile/{mainfile}#/run/0, a reference into an Archive of the same upload
  • /entries/{entry_id}/archive#/run/0/calculation/1, a reference into the Archive of a different entry on the same NOMAD installation
  • /uploads/{upload_id}/archive/{entry_id}#/run/0/calculation/1, similar to the previous one but based on uploads
  •{upload_id}/archive/{entry_id}#/run/0/calculation/1, a global reference towards a different NOMAD installation (Oasis)

The host and path parts of URLs correspond with the NOMAD API. The anchors are paths from the root section of an Archive, over its sub-sections, to the referenced section or quantity value. Each path segment is the name of the subsection or an index in a repeatable subsection: /system/0 or /system/0/atom_labels.

References are automatically serialized by :py:meth:MSection.m_to_dict. When de-serializing data with :py:meth:MSection.m_from_dict these references are not resolved right away, because the reference section might not yet be available. Instead references are stored as :class:MProxy instances. These objects are automatically replaced by the referenced object when a respective quantity is accessed.

If you want to define references, it might not be possible to define the referenced section or quantity beforehand, due to the way Python definitions and imports work. In these cases, you can use a proxy to reference the reference type. There is a special proxy implementation for sections:

class Calculation(MSection):
    system = Quantity(type=SectionProxy('System')

The strings given to SectionProxy are paths within the available definitions. The above example works, if System is eventually defined in the same package.


In the old meta-info this was known as abstract types.

Categories are defined with Python classes that have :class:MCategory as base class. Their name and description are taken from the name and docstring of the class. An example category looks like this:

class CategoryName(MCategory):
    ''' Category description '''
    m_def = Category(links=[''], categories=[ParentCategory])


Metainfo packages correspond to Python packages. Typically your metainfo Python files should follow this pattern:

from nomad.metainfo import Package

m_package = Package()

# Your section classes and categories


Adding Python schemas to NOMAD

Now you know how to write a schema as a Python module, but how should you integrate new schema modules into the existing code and what conventions need to be followed?

Schema super structure

You should follow the basic developer's getting started to setup a development environment. This will give you all the necessary libraries and allows you to place your modules into the NOMAD code.

The EntryArchive section definition sets the root of the archive for each entry in NOMAD. It therefore defines the top level sections:

  • metadata, all "administrative" metadata (ids, permissions, publish state, uploads, user metadata, etc.)
  • results, a summary with copies and references to data from method specific sections. This also presents the searchable metadata.
  • workflows, all workflow metadata
  • Method specific sub-sections, e.g. run. This is were all parsers are supposed to add the parsed data.

The main NOMAD Python project includes Metainfo definitions in the following modules:

  • nomad.metainfo defines the Metainfo itself. This includes a self-referencing schema. E.g. there is a section Section, etc.
  • nomad.datamodel mostly defines the section metadata that contains all "administrative" metadata. It also contains the root section EntryArchive.
  • nomad.datamodel.metainfo defines all the central, method specific (but not parser specific) definitions. For example the section run with all the simulation definitions (computational material science definitions) that are shared among the respective parsers.

Extending existing sections

Parsers can provide their own definitions. By conventions, these are placed into a metainfo sub-module of the parser Python module. The definitions here can add properties to existing sections (e.g. from nomad.datamodel.metainfo). By convention us a x_mycode_ prefix. This is done with the extends_base_section Section property. Here is an example:

from nomad.metainfo import Section
from nomad.datamodel.metainfo.workflow import Workflow

class MyCodeRun(Workflow)
    m_def = Section(extends_base_section=True)
    x_mycode_execution_mode = Quantity(
        type=MEnum('hpc', 'parallel', 'single'), description='...')

Schema conventions

  • Use lower snake case for section properties; use upper camel case for section definitions.
  • Use a _ref suffix for references.
  • Use subsections rather than inheritance to add specific quantities to a general section. E.g. the section workflow contains a section geometry_optimization for all geometry optimization specific workflow quantities.
  • Prefix parser-specific and user-defined definitions with x_name_, where name is the short handle of a code name or other special method prefix.

Use Python schemas to work with data

Access structured data via API

The API section demonstrates how to access an Archive, i.e. retrieve the processed data from a NOAMD entry. This API will give you JSON data likes this:
    "run": [
            "program": {...},
            "method": [...],
            "system": [
                    "type": "bulk",
                    "configuration_raw_gid": "-ZnDK8gT9P3_xtArfKlCrDOt9gba",
                    "is_representative": true,
                    "chemical_composition": "KKKGaGaGaGaGaGaGaGaGa",
                    "chemical_composition_hill": "Ga9K3",
                    "chemical_composition_reduced": "K3Ga9",
                    "atoms": {...},
                    "springer_material": [...],
                    "symmetry": [...]
            "calculation": [...],
    "workflow": [...],
    "metadata": {...},
        "material": {...},
        "method": {...},
        "properties": {...},

This will show you the Archive as a hierarchy of JSON objects (each object is a section), where each key is a property (e.g. a quantity or subsection). Of course you can use this data in this JSON form. You can expect that the same keys (each item has a formal definition) always provides the same type of data. However, not all keys are present in every archive, and not all lists might have the same number of objects. This depends on the data. For example, some runs contain many systems (e.g. geometry optimizations), others don't; typically bulk systems will have symmetry data, non bulk systems might not. To learn what each key means, you need to look up its definition in the Metainfo.

You can browse the NOMAD metainfo schema or the archive of each entry (e.g. a VASP example) in the web-interface.

Wrap data with Python schema classes

In Python, JSON data is typically represented as nested combinations of dictionaries and lists. Of course, you could work with this right away. To make it easier for Python programmers the NOMAD Python package allows you to use this JSON data with a higher level interface, which provides the following advantages:

  • code completion in dynamic coding environments like Jupyter notebooks
  • a cleaner syntax that uses attributes instead of dictionary access
  • all higher dimensional numerical data is represented as numpy arrays
  • allows to navigate through references
  • numerical data has a Pint unit attached to it

For each section the Python package contains a Python class that corresponds to its definition in the metainfo. You can use these classes to access json_data downloaded via API:

from nomad.datamodel import EntryArchive

archive = EntryArchive.m_from_dict(json_data)
calc =[0].calculation[-1]
total_energy_in_ev =
formula = calc.system_ref.chemical_formula_reduced

Archive data can also be serialized into JSON again:

import json

print(json.dumps(calc.m_to_dict(), indent=2))

Access structured data via the NOMAD Python package

The NOMAD Python package provides utilities to query large amounts of archive data. This uses the built-in Python schema classes as an interface to the data.

Custom normalizers

For custom schemas, you might want to add custom normalizers. All files are parsed and normalized when they are uploaded or changed. The NOMAD metainfo Python interface allows you to add functions that are called when your data is normalized.

Here is an example:

from nomad.datamodel import EntryData, ArchiveSection
from nomad.metainfo.metainfo import Quantity, Datetime, SubSection

class Sample(ArchiveSection):
    added_date = Quantity(type=Datetime)
    formula = Quantity(type=str)

    sample_id = Quantity(type=str)

    def normalize(self, archive, logger):
        super(Sample, self).normalize(archive, logger)

        if self.sample_id is None:
            self.sample_id = f'{self.added_date}--{self.formula}'

class SampleDatabase(EntryData):
    samples = SubSection(section=Sample, repeats=True)

To add a normalize function, your section has to inherit from ArchiveSection which provides the base for this functionality. Now you can overwrite the normalize function and add you own behavior. Make sure to call the super implementation properly to support schemas with multiple inheritance. In order to control the order by which the normalizations are executed, one can define normalizer_level which is set to 0 by default. The normalize functions are always called for any sub section before the parent section. However, the order for any sections on the same level will be from low values of normalizer_level to high.

If we parse an archive like this:

  m_def: 'examples.archive.custom_schema.SampleDatabase'
    - formula: NaCl
      added_date: '2022-06-18'

we will get a final normalized archive that contains our data like this:

  "data": {
    "m_def": "examples.archive.custom_schema.SampleDatabase",
    "samples": [
        "added_date": "2022-06-18T00:00:00+00:00",
        "formula": "NaCl",
        "sample_id": "2022-06-18 00:00:00+00:00--NaCl"

Schema plugin metadata

A Schema describes a NOMAD Python schema that can be loaded as a plugin.

name type
name str A short descriptive human readable name for the plugin.
description str A human readable description of the plugin.
plugin_documentation_url str The URL to the plugins main documentation page.
plugin_source_code_url str The URL of the plugins main source code repository.
python_package str Name of the python package that contains the plugin code and a plugin metadata file called nomad_plugin.yaml.
package_path str Path of the plugin package. Will be determined using python_package if not explicitly defined.
key str Key used to identify this plugin.
plugin_type str The type of the plugin. This has to be the string schema for schema plugins.
default: schema

Develop a Parser plugin

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.

Fork and clone the parser example project as described in How-to install a plugin. Follow the original how-to on writing a parser.

Parser plugin metadata

A Parser describes a NOMAD parser that can be loaded as a plugin.

The parser itself is references via python_name. For Parser instances python_name must refer to a Python class that has a parse function. The other properties are used to create a MatchingParserInterface. This comprises general metadata that allows users to understand what the parser is, and metadata used to decide if a given file "matches" the parser.

name type
name str A short descriptive human readable name for the plugin.
description str A human readable description of the plugin.
plugin_documentation_url str The URL to the plugins main documentation page.
plugin_source_code_url str The URL of the plugins main source code repository.
python_package str Name of the python package that contains the plugin code and a plugin metadata file called nomad_plugin.yaml.
plugin_type str The type of the plugin. This has to be the string parser for parser plugins.
default: parser
parser_class_name str The fully qualified name of the Python class that implements the parser. This class must have a function def parse(self, mainfile, archive, logger).
parser_as_interface int By default the parser metadata from this config (and the loaded nomad_plugin.yaml) is used to instantiate a parser interface that is lazy loading the actual parser and performs the mainfile matching. If the parser interface matching based on parser metadata is not sufficient and you implemented your own is_mainfile parser method, this setting can be used to use the given parser class directly for parsing and matching.
default: False
mainfile_contents_re str A regular expression that is applied the content of a potential mainfile. If this expression is given, the parser is only considered for a file, if the expression matches.
mainfile_name_re str A regular expression that is applied the name of a potential mainfile. If this expression is given, the parser is only considered for a file, if the expression matches.
default: .*
mainfile_mime_re str A regular expression that is applied the mime type of a potential mainfile. If this expression is given, the parser is only considered for a file, if the expression matches.
default: text/.*
mainfile_binary_header bytes Matches a binary file if the given bytes are included in the file.
mainfile_binary_header_re bytes Matches a binary file if the given binary regular expression bytes matches the file contents.
mainfile_alternative int If True, the parser only matches a file, if no other file in the same directory matches a parser.
default: False
mainfile_contents_dict dict Is used to match structured data files like JSON or HDF5.
supported_compressions List[str] Files compressed with the given formats (e.g. xz, gz) are uncompressed and matched like normal files.
default: []
domain str The domain value dft will apply all normalizers for atomistic codes. Deprecated.
default: dft
level int The order by which the parser is executed with respect to other parsers.
default: 0