/docker-django-basin3d

Docker image defintion for building a Django REST API BASIN-3D with public data plugins

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docker-django-basin3d

Docker image definition for building a Django REST API BASIN-3D with public data plugins

The official django-basin3d docker image

What is django-basin3d?

from django-basin3d.readthedocs.org

Django BASIN-3D is a software that synthesizes diverse earth science data from a variety of remote sources in real-time without the need for storing all the data in a single database. It is a data brokering framework designed to parse, translate, and synthesize diverse observations from well-curated repositories into standardized formats for scienfic uses such as analysis and visualization. Thus, it provides unified access to a diverse set of data sources and data types by connecting to the providers in real-time and transforming the data streams to provide an integrated view. BASIN-3D enables users to always have access to the latest data from each of the sources, but to deal with the data as if all of the data were integrated in local storage. Importantly users can integrate public data without establishing a prior working relationships with each data source.

Django BASIN-3D is an extendable Python/Django application that uses a generalized data synthesis model that applies across a variety of earth science observation types (hydrology, geochemistry, climate etc.) The synthesis models, BASIN-3D’s abstracted formats, are based on the Open Geospatial Consortium (OGC) and ISO “Observations and Measurements” (OGC 10-004r3 / ISO 19156: 2013) and OGC “Timeseries Profile of Observations and Measurement “(OGC 15-043r3) data standards. The synthesized data available via REpresentational State Transfer (REST) Application Programming Interfaces (API).

The current version of BASIN-3D can be used to integrate time-series earth science observations across a hierarchy of spatial locations. The use of the OGC/ISO framework makes BASIN-3D extensible to other data types, and in the future we plan to add support for remote sensing and gridded (e.g. model output) data. BASIN-3D is able to handle multiscale data, through the use of the OGC/ISO framework that allows for specification of hierarchies of spatial features (e.g., points, plots, sites, watersheds, basin). Thus, users can retrieve data for a specific point location or river basin.

Build image

Vist https://github.com/BASIN-3D/django-basin3d/tags to determine the version of django-basin3d to build

VERSION=<tag>
./build.sh $VERSION

Build an image for a private repository

 VERSION=<tag>
 REGISTRY=registry.example.com  ./build.sh $VERSION

Build an image with specific UID and GID.

 VERSION=<tag>
 IMAGE_GID=75555 IMAGE_UID=75556 ./build.sh $VERSION

Note the image name and tag is formated django-basin3d:<version>-p<num> (e.g django-basin3d:0.0.4-p2)

Test Image

Test image with the runTests.sh

./runTests.sh django-basin3d:<tag>
**********
SUCCESS!!
**********
******************************
Cleaning up testing artifacts
******************************
edfc46a9e869a0004e2d89ef47545891de31b19b36e4efb8151ed60035738f52
90d96d5674c1979e520d1ea7fec7653a13aafafb32fe1d4e432b7922a943dcc9
edfc46a9e869a0004e2d89ef47545891de31b19b36e4efb8151ed60035738f52
90d96d5674c1979e520d1ea7fec7653a13aafafb32fe1d4e432b7922a943dcc9
test-network

Usage

The following examples demonstrate how to run this image

The following is a very minimal example. Please refer to the django-basin3d.readthedocs.org documentation for more information about django-basin3d.

docker run  \
   -p 8080:8080 \
   --name basin3d-app  -it django-basin3d:<tag>

The REST API should be able to be accessed at http://localhost:8080

How to use this image

Image Build Arguments

The following are the optional build arguments for this docker image.

IMAGE_GID: (default: 4999) the UID to run Django BASIN-3D service as.

IMAGE_UID: (default: 5000 ) the UID to run Django BASIN-3D service as.

docker build --build-arg IMAGE_UID=<uid> 
    --build-arg IMAGE_GID=<gid>

Image Environment Variables

The following environment variables are optional:

ADMIN_USER: the django superuser username.

ADMIN_EMAIL: (recommended) The email for the django superuser

ADMIN_PASSWORD: (recommended) The password for the admin user

ADMIN_PASSWORD_FILE: (recommended) A file that contains the admin user password. This file must be mountied inside the container.

CSRF_COOKIE_SECURE: Default is False (See https://docs.djangoproject.com/en/3.2/ref/settings/#csrf-cookie-secure)

DJANGO_DEBUG: Default is False. Set to True for setting Django app in debug mode .

SECRET_KEY: (recommended) The session secret key for the Django application

SESSION_COOKIE_SECURE: Default is False (See https://docs.djangoproject.com/en/3.2/ref/settings/#session-cookie-secure)

The following datbase environment variables are optional. The container will start up with a Sqllite DB by default.

SQL_ENGINE: Default engine is sqlite

SQL_DATABASE: Default is /app/basin3d_app/db.sqlite3

SQL_USER: Default is basin3d

SQL_PASSWORD: Default is empty string

SQL_HOST: Default is localhost

SQL_PORT: Default is 5432

Run the docker container with a Postgres DB

docker network create basin3d-network

DB_NAME=basin3d
DB_PASSWORD=basin3d
docker run  \
   -p 5432:5432 \
   -e POSTGRES_PASSWORD=$DB_PASSWORD \
   -e POSTGRES_USER=$DB_NAME \
   --network basin3d-network -d \
   --name basin3d-db  -it postgres:alpine 

docker run  \
       -p 8080:8080     \
       -e ADMIN_PASSWORD=changeme \
       -e SQL_ENGINE=django.db.backends.postgresql_psycopg2 \
       -e SQL_DATABASE=$DB_NAME \
       -e SQL_PASSWORD=$DB_PASSWORD \
       -e SQL_HOST=basin3d-db \
       --name basin3d-app  -d \
       --network=basin3d-network  \
       -it django-basin3d:<tag> 

The REST API should be able to be accessed at http://localhost:8080

Stop and remove running containers and network

docker stop basin3d-app basin3d-db
docker rm basin3d-app basin3d-db
docker network rm basin3d-network

Copyright

Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets (BASIN-3D) Copyright (c) 2019, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.

NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works

License

See LICENSE

Supported Docker versions

This image is officially supported on Docker Desktop 3.6.0

People

Current Project Team Members: