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RaMa-Scene a web-platform to analyse Environmentally Extended Input-Output data and generate scenarios

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RaMa-Scene


RaMa-Scene is a django 2.0 based web-application that allows for analyzing Environmentally Extended Input-Output (EEIO) tables. EXIOBASE v3.3 is used in this project. Demo version: http://cml.liacs.nl:8080/ramascene/

License: GPL v3 Contributions welcome

Developers Guide


http://rama-scene.readthedocs.io/en/latest/

Getting started


Retrieve the raw datasets

EXIOBASE-Rama-Scene (modified version including secondary materials):

http://doi.org/10.5281/zenodo.3533196

Clone the project

git clone https://bitbucket.org/CML-IE/rama-scene.git

Create a virtual environment (python3.5 or higher) and install the app requirements (make sure you have python-dev installed via apt-get)

$ pip3 install -r requirements.txt 

Install node.js (node version: 3.10.10 or higher)

$ sudo apt-get update
$ sudo apt-get install nodejs

Note: On debian apt install nodejs-legacy

Install redis (for Django Channels)

$ sudo apt install redis-server

Install rabbitMQ (for Celery)

$ sudo apt-get install -y erlang

$ sudo apt-get install rabbitmq-server

Then enable and start the RabbitMQ service:

$ systemctl enable rabbitmq-server

$ systemctl start rabbitmq-server

Check the status to make sure everything is running: $ systemctl status rabbitmq-server

Note: Perform all next steps in the virtualenv and in the rootfolder of the project

Set the following environment variables (see sample-dev-env.sh):

export DJANGO_SETTINGS_MODULE=ramasceneMasterProject.config.dev
export DATASETS_VERSION=version downloaded e.g. v3
export DATASETS_DIR=my/path/to/datasets
export OPENBLAS_NUM_THREADS=adjust according to how many cores you want to use

If you are on Linux and using the OpenBlas library for Numpy. It is advised to set the number of threads Numpy uses. To find which library is used in python:

>>>import numpy as np
>>>np.__config__.show()

Prepare the database

$ python3 manage.py makemigrations
$ python3 manage.py migrate

Populate the database

$ python3 manage.py populateHierarchies

Prepare static resources

$ npm install

Built React bundle

$ ./node_modules/.bin/webpack --config dev-webpack.config.js 

Start Celery

Start the default module to enable handling of analytical calculations:

$ celery -A ramasceneMasterProject worker -l info  --concurrency 1 --queue calc_default -n worker1.%h

Start the modelling module to enable handling of modelling calculations:

$ celery -A ramasceneMasterProject worker -l info  --concurrency 1 --queue modelling -n worker2.%h

Start the development server

$ python3 manage.py runserver

Access the app via the webrowser: http://127.0.0.1:8000/ramascene/

[Optional] enable debug logging

To enable debug logging, open the ramasceneMasterProject/config/dev.py file. Uncomment the "logging for Django" section.

[Optional] run tests

In case you want to run tests you can perform unit tests in the root folder:

$ python3 manage.py test -v2

For integration tests you need to start the celery workers first (explained above). You can perform the integration test with the following command:

$ pytest -vs

If the test has succeeded, you�ll need to repopulate the database with the following command:

$ python3 manage.py populateHierarchies

Core dependencies


The app uses Celery [4.1.0] (http://docs.celeryproject.org/en/latest/django/first-steps-with-django.html), Django channels [2.1.5] (https://channels.readthedocs.io/en/latest/)