ARS implementation ================== Prerequisites Require python 3+, django 3.1+ and channels packages. Test your python environment ```bash python --version ```

You might need to use python3 and pip3 commands on your system if Python is bound to 2.7

Install dependencies from the requirements.txt in the root of the GitHub repository ```bash pip install -r requirements.txt ``` Setup Create the django database ```bash python tr_sys/manage.py makemigrations tr_ars python tr_sys/manage.py migrate python tr_sys/manage.py createsuperuser ``` Start RabbitMQ ```bash docker run -d -p 5672:5672 rabbitmq ``` Start Celery task queuing ensure that USE_CELERY=True in tr_sys/settings.py ```bash cd tr_sys; celery -A tr_sys worker -l info ``` Bring up the server ```bash python tr_sys/manage.py runserver --noreload ``` Preview the message queue at http://localhost:8000/ars/api/messages Now post a new message to the queue ```bash curl -d @tr_ars/ars_query.json http://localhost:8000/ars/api/submit curl -d @tr_sys/tr_ara_unsecret/unsecretStatusQuery.json http://localhost:8000/ars/api/submit ``` Run tests after new code development (also see .travis.yml) ```bash python server.py test ``` [If desired] manipulate individual agents and their actors to the running ARS server ```bash python tr_sys/manage.py loaddata ../data/fixtures/channels.json python tr_sys/manage.py loaddata ../data/fixtures/agents.json python tr_sys/manage.py loaddata ../data/fixtures/actors.json curl -d @tr_sys/tr_ars/agent_bte.json http://localhost:8000/ars/api/agents > response1.htm curl -d @tr_sys/tr_ars/actor_runbte.json http://localhost:8000/ars/api/actors > response2.htm curl -d @tr_sys/tr_ara_unsecret/unsecretAgent.json http://localhost:8000/ars/api/agents > response1.htm curl -d @tr_sys/tr_ara_unsecret/unsecretActor.json http://localhost:8000/ars/api/actors > response2.htm ```