Developing NOMAD

Getting started

Clone the sources

If not already done, you should clone nomad. To clone the main NOMAD repository:

git clone nomad
cd nomad

Prepare your Python environment

You work in a Python virtual environment.


The nomad code currently targets python 3.7. If you host machine has an older version installed, you can use pyenv to use python 3.7 in parallel to your system’s python.


We strongly recommend to use virtualenv to create a virtual environment. It will allow you to keep nomad and its dependencies separate from your system’s python installation. Make sure to base the virtual environment on Python 3. To install virtualenv, create an environment and activate the environment use:

pip install virtualenv
virtualenv -p `which python3` .pyenv
source .pyenv/bin/activate


If you are a conda user, there is an equivalent, but you have to install pip and the right python version while creating the environment.

conda create --name nomad_env pip python=3.7
conda activate nomad_env

To install libmagick for conda, you can use (other channels might also work):

conda install -c conda-forge --name nomad_env libmagic


Make sure you have the most recent version of pip:

pip install --upgrade pip

Missing system libraries (e.g. on MacOS)

Even though the NOMAD infrastructure is written in python, there is a C library required by one of our python dependencies. Libmagic is missing on some systems. Libmagic allows to determine the MIME type of files. It should be installed on most unix/linux systems. It can be installed on MacOS with homebrew:

brew install libmagic

Install sub-modules.

Nomad is based on python modules from the NOMAD-coe project. This includes parsers, python-common and the meta-info. These modules are maintained as their own GITLab/git repositories. To clone and initialize them run:

git submodule update --init

All requirements for these submodules need to be installed and they need to be installed themselves as python modules. Run the script that will install everything into your virtual environment:

./ -e

If one of the Python packages that are installed during this process, fails because it cannot be compiled on your platform, you can try pip install --prefer-binary <packagename> to install set package manually.

The -e option will install the NOMAD-coe dependencies with symbolic links allowing you to change the downloaded dependency code without having to reinstall after.

Install nomad

Finally, you can add nomad to the environment itself (including all extras)

pip install -e .[all]

If pip tries to use and compile sources and this creates errors, it can be told to prefer binary version:

pip install -e .[all] --prefer-binary

Generate GUI artifacts

The NOMAD GUI requires static artifacts that are generated from the NOMAD Python codes.

nomad dev metainfo > gui/src/metainfo.json
nomad dev search-quantities > gui/src/searchQuantities.json
nomad dev units > gui/src/units.js

In additional, you have to do some more steps to prepare your working copy to run all the tests. See below.

Running the infrastructure

To run NOMAD, some 3-rd party services are neeed

  • elastic search: nomad’s search and analytics engine

  • mongodb: used to store processing state

  • rabbitmq: a task queue used to distribute work in a cluster

All 3rd party services should be run via docker-compose (see below). Keep in mind the docker-compose configures all services in a way that mirror the configuration of the python code in nomad/ and the gui config in gui/.env.development.

You can run all services with:

cd ops/docker-compose/infrastructure
docker-compose up -d mongo elastic rabbitmq

To shut down everything, just ctrl-c the running output. If you started everything in deamon mode (-d) use:

docker-compose down

Usually these services only used by NOMAD, but sometimes you also need to check something or do some manual steps. You can access mongodb and elastic search via your preferred tools. Just make sure to use the right ports.

Running NOMAD

Before you run NOMAD for development purposes, you should configure it to use the test realm of our user management system. By default, NOMAD will use the prod realm. Create a nomad.yaml file in the root folder:

  realm: fairdi_nomad_test

NOMAD consist of the NOMAD app/api, a worker, and the GUI. You can run app and worker with the NOMAD cli. These commands will run the services and show their logout put. You should open them in separate shells as they run continuously. They will not watch code changes and you have to restart manually.

nomad admin run app
nomad admin run worker

Or both together in once process:

nomad admin run appworker

The app will run at port 8000 by default.

To run the worker directly with celery, do (from the root)

celery -A nomad.processing worker -l info

When you run the gui on its own (e.g. with react dev server below), you have to have the app manually also. The gui and its dependencies run on node and the yarn dependency manager. Read their documentation on how to install them for your platform.

cd gui
yarn start

Running tests

To run the tests some additional settings and files are necessary that are not part of the code base.

First, you need to provide the springer.msg Springer materials database. It can be copied from /nomad/fairdi/db/data/springer.msg on our servers and should be placed at nomad/normalizing/data/springer.msg.

Second, you have to provide static files to serve the docs and NOMAD distribution:

cd docs
make html
cd ..
python compile
python sdist
cp dist/nomad-lab-*.tar.gz dist/nomad-lab.tar.gz

You need to have the infrastructure partially running: elastic, rabbitmq. The rest should be mocked or provided by the tests. Make sure that you do no run any worker, as they will fight for tasks in the queue.

cd ops/docker-compose/infrastructure
docker-compose up -d elastic rabbitmq
cd ../..
pytest -svx tests

We use pylint, pycodestyle, and mypy to ensure code quality. To run those:

nomad dev qa --skip-test

To run all tests and code qa:

nomad dev qa

This mimiques the tests and checks that the GitLab CI/CD will perform.

Setup your (I)DE

The documentation section on development guidelines details how the code is organized, tested, formatted, and documented. To help you meet these guidelines, we recomment to use a proper IDE for development and ditch any VIM/Emacs (mal-)practices.

Visual Studio Code

Here are some VSCode settings that will enable features for linting, some auto formating, line size ruler, etc.

    "python.venvPath": "${workspaceFolder}/.pyenv",
    "python.pythonPath": "${workspaceFolder}/.pyenv/bin/python",
    "editor.rulers": [90],
    "editor.renderWhitespace": "all",
    "editor.tabSize": 4,
    "[javascript]": {
        "editor.tabSize": 2
    "files.trimTrailingWhitespace": true,
    "editor.codeActionsOnSave": ["source.fixAll.eslint"],
    "python.linting.pylintEnabled": true,
    "python.linting.pylintArgs": [
    "python.linting.pycodestylePath": "pycodestyle",
    "python.linting.pycodestyleEnabled": true,
    "python.linting.pycodestyleArgs": ["--ignore=E501,E701,E731"],
    "python.linting.mypyEnabled": true,
    "python.linting.mypyArgs": [
    "files.watcherExclude": {
        "**/.git/objects/**": true,
        "**/.git/subtree-cache/**": true,
        "**/node_modules/*/**": true,
        "**/.pyenv/*/**": true,
        "**/__pycache__/*/**": true,
        "**/.mypy_cache/*/**": true,
        "**/.volumes/*/**": true,
        "**/docs/.build/*/**": true

Here are some example launch configs for VSCode:

  "version": "0.2.0",
  "configurations": [
      "type": "chrome",
      "request": "launch",
      "name": "Launch Chrome against localhost",
      "url": "http://localhost:3000",
      "webRoot": "${workspaceFolder}/gui"
      "name": "Python: API Flask (0.11.x or later)",
      "type": "python",
      "request": "launch",
      "module": "flask",
      "env": {
        "FLASK_APP": "nomad/app/"
      "args": [
      "name": "Python: some test",
      "type": "python",
      "request": "launch",
      "cwd": "${workspaceFolder}",
      "program": "${workspaceFolder}/.pyenv/bin/pytest",
      "args": [
      "name": "Python: Current File",
      "type": "python",
      "request": "launch",
      "program": "${file}"
      "name": "Python: Attach",
      "type": "python",
      "request": "attach",
      "localRoot": "${workspaceFolder}",
      "remoteRoot": "${workspaceFolder}",
      "port": 3000,
      "secret": "my_secret",
      "host": "localhost"

Code guidelines

Design principles

  • simple first, complicated only when necessary

  • adopting generic established 3rd party solutions before implementing specific solutions

  • only uni directional dependencies between components/modules, no circles

  • only one language: Python (except, GUI of course)


The are some rules or better strong guidelines for writing code. The following applies to all python code (and were applicable, also to JS and other code):

  • Use an IDE (e.g. vscode or otherwise automatically enforce code (formatting and linting). Use nomad qa before committing. This will run all tests, static type checks, linting, etc.

  • There is a style guide to python. Write pep-8 compliant python code. An exception is the line cap at 79, which can be broken but keep it 90-ish.

  • Test the public API of each sub-module (i.e. python file)

  • Be pythonic and watch this.

  • Document any public API of each sub-module (e.g. python file). Public meaning API that is exposed to other sub-modules (i.e. other python files).

  • Use google docstrings.

  • Add your doc-strings to the sphinx documentation in docs. Use .md, follow the example. Markdown in sphinx is supported via [recommonmark] ( and AutoStructify

  • The project structure is according to this guide. Keep it!

  • Write tests for all contributions.

Enforcing Rules: CI/CD

These guidelines are partially enforced by CI/CD. As part of CI all tests are run on all branches; further we run a linter, pep8 checker, and mypy (static type checker). You can run nomad qa to run all these tests and checks before committing.

The CI/CD will run on all refs that do not start with dev-. The CI/CD will not release or deploy anything automatically, but it can be manually triggered after the build and test stage completed successfully.

Names and identifiers

There are is some terminology consistently used in this documentation and the source code. Use this terminology for identifiers.

Do not use abbreviations. There are (few) exceptions: proc (processing); exc, e (exception); calc (calculation), repo (repository), utils (utilities), and aux (auxiliary). Other exceptions are f for file-like streams and i for index running variables. Btw., the latter is almost never necessary in python.


  • upload: A logical unit that comprises one (.zip) file uploaded by a user.

  • calculation: A computation in the sense that is was created by an individual run of a CMS code.

  • raw file: User uploaded files (e.g. part of the uploaded .zip), usually code input or output.

  • upload file/uploaded file: The actual (.zip) file a user uploaded

  • mainfile: The mainfile output file of a CMS code run.

  • aux file: Additional files the user uploaded within an upload.

  • repo entry: Some quantities of a calculation that are used to represent that calculation in the repository.

  • archive data: The normalized data of one calculation in nomad’s meta-info-based format.

Throughout nomad, we use different ids. If something is called id, it is usually a random uuid and has no semantic connection to the entity it identifies. If something is called a hash than it is a hash build based on the entity it identifies. This means either the whole thing or just some properties of said entities.

  • The most common hashes is the calc_hash based on mainfile and auxfile contents.

  • The upload_id is a UUID assigned at upload time and never changed afterwards.

  • The mainfile is a path within an upload that points to a main code output file. Since, the upload directory structure does not change, this uniquely ids a calc within the upload.

  • The calc_id (internal calculation id) is a hash over the mainfile and respective upload_id. Therefore, each calc_id ids a calc on its own.

  • We often use pairs of upload_id/calc_id, which in many context allow to resolve a calc related file on the filesystem without having to ask a database about it.

  • The pid or (coe_calc_id) is an sequential interger id.

  • Calculation handle or handle_id are created based on those pid. To create hashes we use nomad.utils.hash().


There are three important prerequisites to understand about nomad-FAIRDI’s logging:

  • All log entries are recorded in a central elastic search database. To make this database useful, log entries must be sensible in size, frequence, meaning, level, and logger name. Therefore, we need to follow some rules when it comes to logging.

  • We use an structured logging approach. Instead of encoding all kinds of information in log messages, we use key-value pairs that provide context to a log event. In the end all entries are stored as JSON dictionaries with @timestamp, level, logger_name, event plus custom context data. Keep events very short, most information goes into the context.

  • We use logging to inform about the state of nomad-FAIRDI, not about user behavior, input, data. Do not confuse this when determining the log-level for an event. For example, a user providing an invalid upload file, for example, should never be an error.

Please follow the following rules when logging:

  • If a logger is not already provided, only use nomad.utils.get_logger() to acquire a new logger. Never use the build-in logging directly. These logger work like the system loggers, but allow you to pass keyword arguments with additional context data. See also the structlog docs.

  • In many context, a logger is already provided (e.g. api, processing, parser, normalizer). This provided logger has already context information bounded. So it is important to use those instead of acquiring your own loggers. Have a look for methods called get_logger or attributes called logger.

  • Keep events (what usually is called message) very short. Examples are: file uploaded, extraction failed, etc.

  • Structure the keys for context information. When you analyse logs in ELK, you will see that the set of all keys over all log entries can be quit large. Structure your keys to make navigation easier. Use keys like nomad.proc.parser_version instead of parser_version. Use module names as prefixes.

  • Don’t log everything. Try to anticipate, how you would use the logs in case of bugs, error scenarios, etc.

  • Don’t log sensitive data.

  • Think before logging data (especially dicts, list, numpy arrays, etc.).

  • Logs should not be abused as a printf-style debugging tool.

The following keys are used in the final logs that are piped to Logstash. Notice that the key name is automatically formed by a separate formatter and may differ from the one used in the actual log call.

Keys that are autogenerated for all logs:

  • @timestamp: Timestamp for the log

  • @version: Version of the logger

  • host: The host name from which the log originated

  • path: Path of the module from which the log was created

  • tags: Tags for this log

  • type: The message_type as set in the LogstashFormatter

  • level: The log level: DEBUG, INFO, WARNING, ERROR

  • logger_name: Name of the logger

  • nomad.service: The service name as configured in

  • nomad.release: The release name as configured in

Keys that are present for events related to processing an entry:

  • nomad.upload_id: The id of the currently processed upload

  • nomad.calc_id: The id of the currently processed entry

  • nomad.mainfile: The mainfile of the currently processed entry

Keys that are present for events related to exceptions:

  • exc_info: Stores the full python exception that was encountered. All uncaught exceptions will be stored automatically here.

  • digest: If an exception was raised, the last 256 characters of the message are stored automatically into this key. If you wish to search for exceptions in Kibana, you will want to use this value as it will be indexed unlike the full exception object.


Branches and clean version history

The master branch of our repository is protected. You must not (even if you have the rights) commit to it directly. The master branch references the latest official release (i.e. what the current NOMAD runs on). The current development is represented by version branches, named vx.x.x. Usually there are two or more of these branched, representing the development on minor/bugfix versions and the next major version(s). Ideally these version branches are also not manually push to.

Instead you develop on feature branches. These are branches that are dedicated to implement a single feature. They are short lived and only exist to implement a single feature.

The lifecycle of a feature branch should look like this:

  • create the feature branch from the last commit on the respective version branch that passes CI

  • do your work and push until you are satisfied and the CI passes

  • create a merge request on GitLab

  • discuss the merge request on GitLab

  • continue to work (with the open merge request) until all issues from the discussion are resolved

  • the maintainer performs the merge and the feature branch gets deleted

While working on a feature, there are certain practices that will help us to create a clean history with coherent commits, where each commit stands on its own.

git commit --amend

If you committed something to your own feature branch and then realize by CI that you have some tiny error in it that you need to fix, try to amend this fix to the last commit. This will avoid unnecessary tiny commits and foster more coherent single commits. With amend you are basically adding changes to the last commit, i.e. editing the last commit. If you push, you need to force it git push origin feature-branch --force-with-lease. So be careful, and only use this on your own branches.

git rebase <version-branch>

Lets assume you work on a bigger feature that takes more time. You might want to merge the version branch into your feature branch from time to time to get the recent changes. In these cases, use rebase and not merge. Rebase puts your branch commits in front of the merged commits instead of creating a new commit with two ancestors. It basically moves the point where you initially branched away from the version branch to the current position in the version branch. This will avoid merges, merge commits, and generally leave us with a more consistent history. You can also rebase before create a merge request, basically allowing for no-op merges. Ideally the only real merges that we ever have, are between version branches.

git merge --squash <other-branch>

When you need multiple branches to implement a feature and merge between them, try to use squash. Squashing basically puts all commits of the merged branch into a single commit. It basically allows you to have many commits and then squash them into one. This is useful if these commits where just made for synchronization between workstations or due to unexpected errors in CI/CD, you needed a save point, etc. Again the goal is to have coherent commits, where each commits makes sense on its own.

Often a feature is also represented by an issue on GitLab. Please mention the respective issues in your commits by adding the issue id at the end of the commit message: My message. #123.

We tag releases with vX.X.X according to the regular semantic versioning practices. After releasing and tagging the version branch is removed. Do not confuse tags with version branches. Remember that tags and branches are both Git references and you can accidentally pull/push/checkout a tag.

The main NOMAD GitLab-project (nomad-fair) uses Git-submodules to maintain its parsers and other dependencies. All these submodules are places in the /dependencies directory. There are helper scripts to install (./ and commit changes to all submodules (./ After merging or checking out, you have to make sure that the modules are updated to not accidentally commit old submodule commits again. Usually you do the following to check if you really have a clean working directory.

git checkout something-with-changes
git submodule update
git status


We currently use git submodules to manage NOMAD internal dependencies (e.g. parsers). All dependencies are python packages and installed via pip to your python environement.

This allows us to target (e.g. install) individual commits. More importantly, we can address c ommit hashes to identify exact parser/normalizer versions. On the downside, common functions for all dependencies (e.g. the python-common package, or nomad_meta_info) cannot be part of the nomad-FAIRDI project. In general, it is hard to simultaneously develop nomad-FAIRDI and NOMAD-coe dependencies.

Another approach is to integrate the NOMAD-coe sources with nomad-FAIRDI. The lacking availability of individual commit hashes, could be replaces with hashes of source-code files.

We use the master branch on all dependencies. Of course feature branches can be used on dependencies to manage work in progress.