jaws-libp 4.6.2

Jefferson Lab Alarm Warning System API

jaws-libp

Reusable Python Classes for JAWS.

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• Quick Start with Compose
• Install
• API
• Configure
• Build
• Develop
• Test
• Release
• See Also

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Quick Start with Compose

1. Grab project

git clone https://github.com/JeffersonLab/jaws-libp
cd jaws-libp

2. Launch Compose

docker compose up

3. Monitor active alarms

docker exec -it jaws-libp list_activations --monitor

4. Trip an alarm

docker exec jaws-libp set_activation alarm1

Note: The docker-compose services require significant system resources - tested with 4 CPUs and 4GB memory.

See: Docker Compose Strategy

See: More Usage Examples

Install

Requires Python 3.9+

pip install jaws-libp

Note: Using newer versions of Python may be problematic because the dependency confluent-kafka uses librdkafka, which often does not have a wheel file prepared for later versions of Python, meaning setuptools will attempt to compile it for you, and that often doesn't work (especially on Windows). Python 3.9 does have a wheel file for confluent-kafka so that's your safest bet. Wheel files also generally only are prepared for Windows, MacOS, and Linux. Plus only for architectures x86_64 and arm64, also only for glibc. If you use with musl libc or linux-aarch64 then you'll likely have to compile librdkafka yourself from source.

API

Sphinx Docs

Configure

Environment variables are used to configure jaws-libp:

Name	Description
BOOTSTRAP_SERVER	Host and port pair pointing to a Kafka server to bootstrap the client connection to a Kafka Cluster; example: kafka:9092
SCHEMA_REGISTRY	URL to Confluent Schema Registry; example: http://registry:8081

The Docker container can optionally handle the following environment variables as well:

Name	Description
ALARM_LOCATIONS	Path to an alarm locations file to import (example file), else an https URL to a file, else a comma separated list of location definitions with fields separated by the pipe symbol. Example Inline CSV: name|parent
ALARM_CATEGORIES	Path to an alarm categories file to import (example file), else an https URL to a file, else a comma separated list of catgory definitions with fields. Example Inline CSV: name
ALARM_CLASSES	Path to an alarm classes file to import (example file), else an https URL to a file, else a comma separated list of class definitions with fields separated by the pipe symbol. Example Inline CSV: name|category|priority|rationale|correctiveaction|pointofcontactusername|latching|filterable|ondelayseconds|offdelayseconds
ALARM_INSTANCES	Path to an alarm instances file to import (example file), else an https URL to a file, else a comma separated list of instance definitions with fields separated by the pipe symbol. Leave epicspv field empty for SimpleProducer. Example Inline CSV: name|class|epicspv|location|maskedby|screencommand

Build

This Python 3.9+ project is built with setuptools and may be run using the Python virtual environment feature to isolate dependencies. The pip tool can be used to download dependencies.

git clone https://github.com/JeffersonLab/jaws-libp
cd jaws-libp
python -m venv .venv_dev --upgrade-deps

Activate the virtual env using your shell specific command, then install in editable mode with dev deps and run build:

pip install -e .[dev]
python -m build

Note for JLab On-Site Users: Jefferson Lab has an intercepting proxy

See: Python Development Notes

Develop

Set up the build environment following the Build instructions.

In order to iterate rapidly when making changes it's often useful to run the Python scripts directly on the local workstation, perhaps leveraging an IDE. In this scenario run the service dependencies with Docker Compose:

docker compose -f deps.yml up

Note: The environment variable defaults work in this scenario and are defined as: BOOTSTRAP_SERVERS=localhost:9094 and SCHEMA_REGISTRY=http://localhost:8081

Test

The integration tests depend on a running Kafka instance, generally in Docker. The tests run automatically via the CI GitHub Action on every commit (unless [no ci] is included in the commit message). The tests can be run locally during development. Set up the development environment following the Develop instructions. Then with the deps.yml Docker containers running and the build virtual environment activated run:

pytest

Release

1. Bump the version number in pyproject.toml and commit and push to GitHub (using Semantic Versioning).
2. Create a new release on the GitHub Releases page corresponding to same version in pyproject.toml (Enumerate changes and link issues).
3. Publish to PyPi GitHub Action should run automatically.
4. Publish to gh-pages GitHub Action should run automatically.
5. Publish to DockerHub GitHub Action should run automatically.
6. Bump and commit quick start image version after confirming new image works

See Also

• jaws-libj (Java)
• Developer Notes