pymerlin 0.0.5

Python mission modeling framework for Aerie

pymerlin

A python modeling framework for Aerie.

TODO:

• Daemon tasks
• More interesting cells and resources
• Conditions on discrete cells
• Conditions on continuous cells
• Child tasks
• Spiceypy
• JPL time
• pip-installable models
• build Aerie-compatible jars and provide docker-compose file with python

Prerequisites

• python >=3.6.3 (only tested on 3.11 so far...)
• java >=21

Install pymerlin by running pip install pymerlin

1. Make a venv python -m venv venv
2. Activate the venv source ./venv/bin/activate
3. Install requirements python -m pip install -r requirements.txt
4. Start jupyter lab jupyter-lab
5. When jupyter opens, navigate to demo/simulation_example.py
6. Update the path in the first cell to point to your cloned pymerlin directory (we should eliminate the need for this hack)
7. Run all cells

Architecture

pymerlin is to merlin as pyspark is to spark. This means that pymerlin uses py4j as a bridge between a python process and a java process. This allows pymerlin to use the Aerie simulation engine directly, without having to re-implement it in python.

This means that running simulate starts a subprocess using java -jar /path/to/pymerlin.jar.

Approachability over performance

The main tenet of pymerlin is approachability, and its aim is to enable rapid prototyping of models and activities. While where possible, performance will be considered, it is expected that someone who wants to seriously engineer the performance of their simulation will port their code to Java - which has the double benefit of removing socket communication overhead, as well as giving the engineer a single Java process to instrument and analyze, rather than a hybrid system, which may be more difficult to characterize.

Building pymerlin.jar

cd pymerlin-java
./gradlew assemble
mv pymerlin/build/libs/pymerlin.jar ../pymerlin/_internal/jars