Standard Energy Efficiency Data (SEED) Platform

The Standard Energy Efficiency Data (SEED) Platform™ is a web-based application that helps organizations easily manage data on the energy performance of large groups of buildings. Users can combine data from multiple sources, clean and validate it, and share the information with others. The software application provides an easy, flexible, and cost-effective method to improve the quality and availability of data to help demonstrate the economic and environmental benefits of energy efficiency, to implement programs, and to target investment activity.

The SEED application is written in Python/Django, with AngularJS, Bootstrap, and other JavaScript libraries used for the front-end. The back-end database is required to be PostgreSQL.

The SEED web application provides both a browser-based interface for users to upload and manage their building data, as well as a full set of APIs that app developers can use to access these same data management functions.

Work on SEED Platform is managed by the National Renewable Energy Laboratory, with funding from the U.S. Department of Energy.

Getting Started

Development Setup

Installation on OSX

These instructions are for installing and running SEED on Mac OSX in development mode.

Quick Installation Instructions

This section is intended for developers who may already have their machine ready for general development. If this is not the case, skip to Prerequisites. Note that SEED uses python 3.

  • install Postgres 11.1 and redis for cache and message broker

  • install PostGIS 2.5 and enable it on the database using CREATE EXTENSION postgis;

  • install TimescaleDB 1.5.0

  • use a virtualenv (if desired)

  • git clone git@github.com:seed-platform/seed.git

  • create a local_untracked.py in the config/settings folder and add CACHE and DB config (example local_untracked.py.dist)

  • to enable geocoding, get MapQuest API key and attach it to your organization

  • export DJANGO_SETTINGS_MODULE=config.settings.dev in all terminals used by SEED (celery terminal and runserver terminal)

  • pip install -r requirements/local.txt
    • for condas python, you way need to run this command to get pip install to succeed: conda install -c conda-forge python-crfsuite

  • bin/install_javascript_dependencies.sh

  • ./manage.py migrate

  • ./manage.py create_default_user

  • ./manage.py runserver

  • DJANGO_SETTINGS_MODULE=config.settings.dev celery -A seed worker -l info -c 4 –maxtasksperchild=1000 –events

  • navigate to http://127.0.0.1:8000/app/#/profile/admin in your browser to add users to organizations

  • main app runs at 127.0.0.1:8000/app

The python manage.py create_default_user will setup a default superuser which must be used to access the system the first time. The management command can also create other superusers.

./manage.py create_default_user [email protected] --organization=lbl --password=demo123
Prerequisites

These instructions assume you have MacPorts or Homebrew. Your system should have the following dependencies already installed:

  • git (port install git or brew install git)

  • graphviz (brew install graphviz)

  • pyenv (Recommended)

    Note

    Although you could install Python packages globally, this is the easiest way to install Python packages. Setting these up first will help avoid polluting your base Python installation and make it much easier to switch between different versions of the code.

    brew install pyenv
    pyenv install <python3 version you want>
    pyenv virtualenv <python3 version you want> seed
    pyenv local seed
    
PostgreSQL 11.1

MacPorts:

sudo su - root
port install postgresql94-server postgresql94 postgresql94-doc
# init db
mkdir -p /opt/local/var/db/postgresql94/defaultdb
chown postgres:postgres /opt/local/var/db/postgresql94/defaultdb
su postgres -c '/opt/local/lib/postgresql94/bin/initdb -D /opt/local/var/db/postgresql94/defaultdb'

# At this point, you may want to add start/stop scripts or aliases to
# ~/.bashrc or your virtualenv ``postactivate`` script
# (in ``~/.virtualenvs/{env-name}/bin/postactivate``).

alias pg_start='sudo su postgres -c "/opt/local/lib/postgresql94/bin/pg_ctl \
    -D /opt/local/var/db/postgresql94/defaultdb \
    -l /opt/local/var/db/postgresql94/defaultdb/postgresql.log start"'
alias pg_stop='sudo su postgres -c "/opt/local/lib/postgresql94/bin/pg_ctl \
    -D /opt/local/var/db/postgresql94/defaultdb stop"'

pg_start

sudo su - postgres
PATH=$PATH:/opt/local/lib/postgresql94/bin/

Homebrew:

brew install postgres
# follow the post install instructions to add to launchagents or call
# manually with `postgres -D /usr/local/var/postgres`
# Skip the remaining Postgres instructions!

Configure PostgreSQL. Replace ‘seeddb’, ‘seeduser’ with desired db/user. By default use password seedpass when prompted. Use the code block below in development only since the seeduser is a SUPERUSER.

createuser -P seeduser
createdb `whoami`
psql -c 'CREATE DATABASE "seeddb" WITH OWNER = "seeduser";'
psql -c 'GRANT ALL PRIVILEGES ON DATABASE "seeddb" TO seeduser;'
psql -c 'ALTER ROLE seeduser SUPERUSER;
PostGIS 2.5

MacPorts:

# Assuming you're still root from installing PostgreSQL,
port install postgis2

Homebrew:

brew install postgis

Configure PostGIS:

psql -d seeddb -c "CREATE EXTENSION postgis;"

# For testing, give seed user superuser access:
# psql -c 'ALTER USER seeduser CREATEDB;'

If upgrading from an existing database or existing local_untracked.py file, make sure to add the MapQuest API Key and set the database engine to ‘ENGINE’: ‘django.contrib.gis.db.backends.postgis’.

Now exit any root environments, becoming just yourself (even though it’s not that easy being green), for the remainder of these instructions.

TimescaleDB 1.5.0

Note, as of version 1.5.0, dumping and restoring databases requires that both the source and target database have the same version of TimescaleDB.

Downloading From Source:

# Note: Installing from source should only be done
# if you have a Postgres installation not maintained by Homebrew.
# This installation requires C compiler (e.g., gcc or clang) and CMake version 3.4 or greater.

git clone https://github.com/timescale/timescaledb.git
cd timescaledb
git checkout 1.5.0

# Bootstrap the build system
./bootstrap

# If OpenSSL can't be found by cmake - run the following instead
# ./bootstrap -DOPENSSL_ROOT_DIR=<location of OpenSSL> # e.g., -DOPENSSL_ROOT_DIR=/usr/local/opt/openssl

# To build the extension
cd build && make

# To install
make install

# Find postgresql.conf
# Then uncomment the shared_preload_libraries line changing it to the following
# shared_preload_libraries = 'timescaledb'
psql -d postgres -c "SHOW config_file;"

# Restart PostgreSQL instance
Python Packages

Run these commands as your normal user id.

Change to a virtualenv (using virtualenvwrapper) or do the following as a superuser. A virtualenv is usually better for development. Set the virtualenv to seed.

workon seed

Make sure PostgreSQL command line scripts are in your PATH (if using MacPorts)

export PATH=$PATH:/opt/local/lib/postgresql94/bin

Some packages (uWSGI) may need to find your C compiler. Make sure you have ‘gcc’ on your system, and then also export this to the CC environment variable:

export CC=gcc

Install requirements with pip

pip install -r requirements/local.txt
NodeJS/npm

Install npm. You can do this by installing from nodejs.org, MacPorts, or Homebrew:

MacPorts:

sudo port install npm

Homebrew:

brew install npm
Configure Django and Databases

In the config/settings directory, there must be a file called local_untracked.py that sets up databases and a number of other things. To create and edit this file, start by copying over the template

cd config/settings
cp local_untracked.py.dist local_untracked.py

Edit local_untracked.py. Open the file you created in your favorite editor. The PostgreSQL config section will look something like this:

# postgres DB config
DATABASES = {
    'default': {
        'ENGINE': 'django.contrib.gis.db.backends.postgis',
        'NAME': 'seeddb',
        'USER': 'seeduser',
        'PASSWORD': 'seedpass',
        'HOST': 'localhost',
        'PORT': '5432',
    }
}

You may want to comment out the AWS settings.

For Redis, edit the CACHES and CELERY_BROKER_URL values to look like this:

CACHES = {
    'default': {
        'BACKEND': 'redis_cache.cache.RedisCache',
        'LOCATION': "127.0.0.1:6379",
        'OPTIONS': {'DB': 1},
        'TIMEOUT': 300
    }
}
CELERY_BROKER_URL = 'redis://127.0.0.1:6379/1'
MapQuest API Key

Register for a MapQuest API key: https://developer.mapquest.com/plan_purchase/steps/business_edition/business_edition_free/register

Visit the Manage Keys page: https://developer.mapquest.com/user/me/apps Either create a new key or use the key initially provided. Copy the “Consumer Key” into the target organizations MapQuest API Key field under the organization’s settings page or directly within the DB.

Run Django Migrations

Change back to the root of the repository. Now run the migration script to set up the database tables

export DJANGO_SETTINGS_MODULE=config.settings.dev
./manage.py migrate
Django Admin User

You need a Django admin (super) user.

./manage.py create_default_user --username=[email protected] --organization=seedorg --password=badpass

Of course, you need to save this user/password somewhere, since this is what you will use to login to the SEED website.

If you want to do any API testing (and of course you do!), you will need to add an API KEY for this user. You can do this in postgresql directly:

psql seeddb seeduser
seeddb=> update landing_seeduser set api_key='DEADBEEF' where id=1;

The ‘secret’ key DEADBEEF is hard-coded into the test scripts.

Install Redis

You need to manually install Redis for Celery to work.

MacPorts:

sudo port install redis

Homebrew:

brew install redis
# follow the post install instructions to add to launchagents or
# call manually with `redis-server`
Install JavaScript Dependencies

The JS dependencies are installed using node.js package management (npm).

npm install
Start the Server

You should put the following statement in ~/.bashrc or add it to the virtualenv post-activation script (e.g., in ~/.virtualenvs/seed/bin/postactivate).

export DJANGO_SETTINGS_MODULE=config.settings.dev

The combination of Redis, Celery, and Django have been encapsulated in a single shell script, which examines existing processes and does not start duplicate instances:

./bin/start-seed.sh

When this script is done, the Django stand-alone server will be running in the foreground.

Login

Open your browser and navigate to http://127.0.0.1:8000

Login with the user/password you created before, e.g., admin@my.org and badpass.

Note

these steps have been combined into a script called start-seed.sh. The script will also not start Celery or Redis if they already seem to be running.

Installation using Docker

Docker works natively on Linux, Mac OSX, and Windows 10. If you are using an older version of Windows (and some older versions of Mac OSX), you will need to install Docker Toolbox.

Choose either Docker Native (Windows/OSX) or Docker Native (Ubuntu) to install Docker.

Docker Native (Ubuntu)

Follow instructions [here](https://docs.docker.com/engine/installation/linux/docker-ce/ubuntu/).

Docker Native (Windows/OSX)

Following instructions (for Mac)[https://docs.docker.com/docker-for-mac/install/] or (for Windows)[https://docs.docker.com/docker-for-windows/install/].

Building and Configuring Containers
  • Run Docker Compose

    docker-compose build
    

    Be Patient … If the containers build successfully, then start the containers

    docker volume create --name=seed_pgdata
    docker volume create --name=seed_media
    docker-compose up
    

    Note that you may need to build the containers a couple times for everything to converge

  • Login to container

    The docker-compose file creates a default user and password. Below are the defaults but can be overridden by setting environment variables.

    username: [email protected]
    password: super-secret-password
    

Note

Don’t forget that you need to reset your default username and password if you are going to use these Docker images in production mode!

Deployment Guide

SEED is intended to be installed on Linux instances in the cloud (e.g. AWS), and on local hardware. SEED Platform does not officially support Windows for production deployment. If this is desired, see the Django notes.

AWS Setup

Amazon Web Services (AWS) provides the preferred hosting for the SEED Platform.

seed is a Django Project and Django’s documentation is an excellent place for general understanding of this project’s layout.

Prerequisites

Ubuntu server 18.04 LTS

Note

These instructions have not been updated for Ubuntu 18.04. It is recommended to use Docker-based deployments.

sudo apt-get update
sudo apt-get upgrade
sudo apt-get install -y libpq-dev python-dev python-pip libatlas-base-dev \
gfortran build-essential g++ npm libxml2-dev libxslt1-dev git mercurial \
libssl-dev libffi-dev curl uwsgi-core uwsgi-plugin-python

PostgreSQL and Redis are not included in the above commands. For a quick installation on AWS it is okay to install PostgreSQL and Redis locally on the AWS instance. If a more permanent and scalable solution, it is recommended to use AWS’s hosted Redis (ElastiCache) and PostgreSQL service.

Note

postgresql >=9.4 is required to support JSON Type

# To install PostgreSQL and Redis locally
sudo apt-get install redis-server
sudo apt-get install postgresql postgresql-contrib
Amazon Web Services (AWS) Dependencies

The following AWS services can be used for SEED but are not required:

  • RDS (PostgreSQL >=9.4)

  • ElastiCache (redis)

  • SES

Python Dependencies

Clone the SEED repository from github

$ git clone [email protected]:SEED-platform/seed.git

enter the repo and install the python dependencies from requirements

$ cd seed
$ sudo pip install -r requirements/aws.txt

JavaScript Dependencies

npm is required to install the JS dependencies.

$ sudo apt-get install build-essential
$ sudo apt-get install curl
$ npm install

Database Configuration

Copy the local_untracked.py.dist file in the config/settings directory to config/settings/local_untracked.py, and add a DATABASES configuration with your database username, password, host, and port. Your database configuration can point to an AWS RDS instance or a PostgreSQL 9.4 database instance you have manually installed within your infrastructure.

# Database
DATABASES = {
    'default': {
        'ENGINE':'django.db.backends.postgresql_psycopg2',
        'NAME': 'seed',
        'USER': '',
        'PASSWORD': '',
        'HOST': '',
        'PORT': '',
    }
}

Note

In the above database configuration, seed is the database name, this is arbitrary and any valid name can be used as long as the database exists.

create the database within the postgres psql shell:

CREATE DATABASE seed;

or from the command line:

createdb seed

create the database tables and migrations:

python manage.py syncdb
python manage.py migrate

create a superuser to access the system

$ python manage.py create_default_user --username=[email protected] --organization=example --password=demo123

Note

Every user must be tied to an organization, visit /app/#/profile/admin as the superuser to create parent organizations and add users to them.

Cache and Message Broker

The SEED project relies on redis for both cache and message brokering, and is available as an AWS ElastiCache service. local_untracked.py should be updated with the CACHES and CELERY_BROKER_URL settings.

CACHES = {
    'default': {
        'BACKEND': 'redis_cache.cache.RedisCache',
        'LOCATION': "seed-core-cache.ntmprk.0001.usw2.cache.amazonaws.com:6379",
        'OPTIONS': { 'DB': 1 },
        'TIMEOUT': 300
    }
}
CELERY_BROKER_URL = 'redis://seed-core-cache.ntmprk.0001.usw2.cache.amazonaws.com:6379/1'

Running Celery the Background Task Worker

Celery is used for background tasks (saving data, matching, creating projects, etc) and must be connected to the message broker queue. From the project directory, celery can be started:

celery -A seed worker -l INFO -c 2 -B --events --maxtasksperchild 1000

General Linux Setup

While Amazon Web Services (AWS) provides the preferred hosting for SEED, running on a bare-bones Linux server follows a similar setup, replacing the AWS services with their Linux package counterparts, namely: PostgreSQL and Redis.

SEED is a Django project and Django’s documentation is an excellent place to general understanding of this project’s layout.

Prerequisites

Ubuntu server/desktop 16.04 or newer (18.04 recommended)

Install the following base packages to run SEED:

sudo add-apt-repository ppa:timescale/timescaledb-ppa
sudo apt update
sudo apt upgrade
sudo apt install libpq-dev python3-dev python3-pip libatlas-base-dev \
gfortran build-essential nodejs npm libxml2-dev libxslt1-dev git \
libssl-dev libffi-dev curl uwsgi-core uwsgi-plugin-python mercurial
sudo apt install gdal-bin postgis
sudo apt install redis-server
sudo apt install timescaledb-postgresql-10 postgresql-contrib

# For running selenium/protractor
sudo apt install default-jre

Note

postgresql >=9.3 is required to support JSON Type

Configure PostgreSQL

Replace ‘seeddb’, ‘seeduser’ with desired db/user. By default use password seedpass when prompted

$ sudo timescaledb-tune
$ sudo service postgresql restart
$ sudo su - postgres
$ createuser -P "seeduser"
$ createdb "seeddb" --owner="seeduser"
$ psql
postgres=# GRANT ALL PRIVILEGES ON DATABASE "seeddb" TO "seeduser";
postgres=# ALTER USER "seeduser" CREATEDB CREATEROLE SUPERUSER;
postgres=# \q
$ exit

Python Dependencies

clone the seed repository from github

$ git clone [email protected]:SEED-platform/seed.git

enter the repo and install the python dependencies from requirements

$ cd seed
$ pip3 install -r requirements/local.txt

JavaScript Dependencies

$ npm install

Django Database Configuration

Copy the local_untracked.py.dist file in the config/settings directory to config/settings/local_untracked.py, and add a DATABASES configuration with your database username, password, host, and port. Your database configuration can point to an AWS RDS instance or a PostgreSQL 9.4 database instance you have manually installed within your infrastructure.

# Database
DATABASES = {
    'default': {
        'ENGINE': 'django.contrib.gis.db.backends.postgis',
        'NAME': 'seeddb',
        'USER': 'seeduser',
        'PASSWORD': '<PASSWORD>',
        'HOST': 'localhost',
        'PORT': '5432',
    }
}

Note

Other databases could be used such as MySQL, but are not supported due to the postgres-specific JSON Type

In in the above database configuration, seed is the database name, this is arbitrary and any valid name can be used as long as the database exists. Enter the database name, user, password you set above.

The database settings can be tested using the Django management command, python3 manage.py dbshell to connect to the configured database.

create the database tables and migrations:

$ python3 manage.py migrate

Cache and Message Broker

The SEED project relies on redis for both cache and message brokering, and is available as an AWS ElastiCache service or with the redis-server Linux package. (sudo apt install redis-server)

local_untracked.py should be updated with the CACHES and CELERY_BROKER_URL settings.

CACHES = {
    'default': {
        'BACKEND': 'redis_cache.cache.RedisCache',
        'LOCATION': '127.0.0.1:6379',
        'OPTIONS': {'DB': 1},
        'TIMEOUT': 300
    }
}
CELERY_BROKER_URL = 'redis://127.0.0.1:6379/1'

Creating the initial user

create a superuser to access the system

$ python3 manage.py create_default_user --username=[email protected] --organization=lbnl --password=badpass

Note

Of course, you need to save this user/password somewhere, since this is what you will use to login to the SEED website.

Every user must be tied to an organization, visit /app/#/profile/admin as the superuser to create parent organizations and add users to them.

Running celery the background task worker

Celery is used for background tasks (saving data, matching, creating projects, etc) and must be connected to the message broker queue. From the project directory, celery can be started:

DJANGO_SETTINGS_MODULE=config.settings.dev celery -A seed worker -l info -c 2 -B --events --maxtasksperchild=1000

Running the development web server

The Django dev server (not for production use) can be a quick and easy way to get an instance up and running. The dev server runs by default on port 8000 and can be run on any port. See Django’s runserver documentation for more options.

$ python3 manage.py runserver --settings=config.settings.dev

Running a production web server

Our recommended web server is uwsgi sitting behind nginx. The python package uwsgi is needed for this, and should install to /usr/local/bin/uwsgi We recommend using dj-static to load static files.

Note

The use of the dev settings file is production ready, and should be used for non-AWS installs with DEBUG set to False for production use.

$ pip3 install uwsgi dj-static

Generate static files:

$ python3 manage.py collectstatic --settings=config.settings.prod -i package.json -i npm-shrinkwrap.json -i node_modules/openlayers-ext/index.html

Update config/settings/local_untracked.py:

DEBUG = False
# static files
STATIC_ROOT = 'collected_static'
STATIC_URL = '/static/'

Start the web server (this also starts celery):

$ ./bin/start-seed

Warning

Note that uwsgi has port set to 80. In a production setting, a dedicated web server such as NGINX would be receiving requests on port 80 and passing requests to uwsgi running on a different port, e.g 8000.

Environment Variables

The following environment variables can be set within the ~/.bashrc file to override default Django settings.

export SENTRY_DSN=https://[email protected]/123
export DEBUG=False
export ONLY_HTTPS=True

Mail Services

AWS SES Service

In the AWS setup, we can use SES to provide an email service for Django. The service is configured in the config/settings/local_untracked.py:

EMAIL_BACKEND = 'django_ses.SESBackend'

In general, the following steps are needed to configure SES:

  1. Access Amazon SES Console - Quickstart

  2. Login to Amazon SES Console. Verify which region we are using (e.g., us-east-1)

  3. Decide on email address that will be sending the emails and add them to the SES Verified Emails.

  4. Test that SES works as expected (while in the SES sandbox). Note that you will need to add the sender and recipient emails to the verified emails while in the sandbox.

  5. Update the local_untracked.py file or set the environment variables for the docker file.

  6. Once ready, move the SES instance out of the sandbox. Following instructions here

  7. (Optional) Set up Amazon Simple Notification Service (Amazon SNS) to notify you of bounced emails and other issues.

  8. (Optional) Use the AWS Management Console to set up Easy DKIM, which is a way to authenticate your emails. Amazon SES console will have the values for SPF and DKIM that you need to put into your DNS.

SMTP service

Many options for setting up your own SMTP service/server or using other SMTP third party services are available and compatible including gmail. SMTP is not configured for working within Docker at the moment.

EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'

local_untracked.py

# PostgreSQL DB config
DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql_psycopg2',
        'NAME': 'seed',
        'USER': 'your-username',
        'PASSWORD': 'your-password',
        'HOST': 'your-host',
        'PORT': 'your-port',
    }
}

# config for local storage backend
DOMAIN_URLCONFS = {'default': 'config.urls'}

CACHES = {
    'default': {
        'BACKEND': 'redis_cache.cache.RedisCache',
        'LOCATION': '127.0.0.1:6379',
        'OPTIONS': {'DB': 1},
        'TIMEOUT': 300
    }
}
CELERY_BROKER_URL = 'redis://127.0.0.1:6379/1'

# SMTP config
EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'

# static files
STATIC_ROOT = 'collected_static'
STATIC_URL = '/static/'

Docker Deployment on AWS

Amazon Web Services (AWS) provides the preferred hosting for the SEED Platform.

seed is a Django Project and Django’s documentation is an excellent place for general understanding of this project’s layout.

Installation

Ubuntu server 18.04 or newer with a m5ad.xlarge (if using in Production instance)

  • After launching the instance, run the following commands to install docker.

# Install any upgrades
sudo apt-get update
sudo apt-get upgrade -y

# Remove any old docker engines
sudo apt-get remove docker docker-engine docker.io containerd runc

# Install docker community edition
sudo apt-get update
sudo apt-get install -y apt-transport-https ca-certificates curl gnupg-agent software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository \
    "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
    $(lsb_release -cs) \
    stable"

sudo apt-get update
sudo apt-get install -y docker-ce docker-ce-cli containerd.io
# Add your user to the docker group
sudo groupadd docker
sudo usermod -aG docker $USER
newgrp docker

Note

It is okay if the first command fails

  • Verify that the DNS is working correctly. Run the following and verify the response lists IPs (v6 most likely)

# verify that the dns resolves
docker run --rm seedplatform/seed getent hosts seed-platform.org
# or
docker run --rm tutum/dnsutils nslookup email.us-west-2.amazonaws.com
  • Install Docker compose

sudo curl -L "https://github.com/docker/compose/releases/download/1.25.4/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
  • Checkout SEED (or install from the releases).

git clone
  • Add in the Server setting into profile.d. For example add the content below (appropriately filled out) into /etc/profile.d/seed.sh

export POSTGRES_USER=seed
export POSTGRES_DB=seed
export POSTGRES_PASSWORD=GDEus3fasd1askj89QkAldjfX
export POSTGRES_PORT=5432
export SECRET_KEY="96=7jg%_&[email protected][email protected]*[email protected]%+^"

# The admin user is only valid only until the database is restored
export [email protected]
export SEED_ADMIN_PASSWORD="7FeBWal38*&k3jlfa92lakj8ih4"
export SEED_ADMIN_ORG=default

# For SES
export AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY>
export AWS_SECRET_ACCESS_KEY=<AWS_SECRET_KEY>
export AWS_SES_REGION_NAME=us-west-2
export AWS_SES_REGION_ENDPOINT=email.us-west-2.amazonaws.com
export [email protected]
  • Before launching the first time, make sure the persistent volumes and the backup directory exist.

docker volume create --name=seed_pgdata
docker volume create --name=seed_media

mkdir -p $HOME/seed-backups

Note

Make sure to have the seed-backups in your path, otherwise the db-postgres container will not launch.

  • Launch the project

cd <checkout dir>
./deploy.sh

Deploying with OEP

The preferred way to deploy with Docker is using docker swarm and docker stack. Look at the deploy.sh script for implementation details.

The short version is to simply run the command below. Note that the passing of the docker-compose.yml filename is not required if using docker-compose.local.yml.

`bash ./deploy.sh docker-compose.local.yml `

If deploying using a custom docker-compose yml file, then simple replace the name in the command above. This would be required if using the Open Efficiency Platform work (connecting SEED to Salesforce).

Migrations

Migrations are handles through Django; however, various versions have customs actions for the migrations. See the migrations page for more information.

Monitoring

Sentry

Sentry can monitor your webservers for any issues. To enable sentry add the following to your local_untracked.py files after setting up your Sentry account on sentry.io.

The RAVEN_CONFIG is used for the backend and the SENTRY_JS_DSN is used for the frontend. At the moment, it is recommended to setup two sentry projects, one for backend and one for frontend.

import raven

RAVEN_CONFIG = {
   'dsn': 'https://<user>:<key>@sentry.io/<job_id>',
   # If you are using git, you can also automatically configure the
   # release based on the git info.
   'release': raven.fetch_git_sha(os.path.abspath(os.curdir)),
}
SENTRY_JS_DSN = 'https://<key>@sentry.io/<job_id>'

API

Authentication

Authentication is handled via an encoded authorization token set in a HTTP header. To request an API token, go to /app/#/profile/developer and click ‘Get a New API Key’.

Authenticate every API request with your username (email, all lowercase) and the API key via Basic Auth. The header is sent in the form of Authorization: Basic <credentials>, where credentials is the base64 encoding of the email and key joined by a single colon :.

Using Python, use the requests library:

import requests

result = requests.get('https://seed-platform.org/api/version/', auth=(user_email, api_key))
print result.json()

Using curl, pass the username and API key as follows:

curl -u user_email:api_key http://seed-platform.org/api/version/

If authentication fails, the response’s status code will be 302, redirecting the user to /app/login.

Payloads

Many requests require a JSON-encoded payload and parameters in the query string of the url. A frequent requirement is including the organization_id of the org you belong to. For example:

curl -u user_email:api_key https://seed-platform.org/api/v2/organizations/12/

Or in a JSON payload:

curl -u user_email:api_key \
  -d '{"organization_id":6, "role": "viewer"}' \
  https://seed-platform.org/api/v2/users/12/update_role/

Using Python:

params = {'organization_id': 6, 'role': 'viewer'}
result = requests.post('https://seed-platform.org/api/v2/users/12/update_role/',
                       data=json.dumps(params),
                       auth=(user_email, api_key))
print result.json()

Responses

Responses from all requests will be JSON-encoded objects, as specified in each endpoint’s documentation. In the case of an error, most endpoints will return this instead of the expected payload (or an HTTP status code):

{
    "status": "error",
    "message": "explanation of the error here"
}

API Endpoints

A list of interactive endpoints are available by accessing the API menu item on the left navigation pane within you account on your SEED instance.

To view a list of non-interactive endpoints without an account, view swagger on the development server.

Data Model

_images/case-a.png _images/case-b.png _images/case-c.png _images/case-d.png _images/data-model.png

Todo

Documentation below is out of state and needs updated.

Our primary data model is based on a tree structure with BuildingSnapshot instances as nodes of the tree and the tip of the tree referenced by a CanonicalBuilding.

Take the following example: a user has loaded a CSV file containing information about one building and created the first BuildingSnapshot (BS0). At this point in time, BS0 is linked to the first CanonicalBuilding (CB0), and CB0 is also linked to BS0.

BS0 <-- CB0
BS0 --> CB0

These relations are represented in the database as foreign keys from the BuildingSnapshot table to the CanonicalBuilding table, and from the CanonicalBuilding table to the BuildingSnapshot table.

The tree structure comes to fruition when a building, BS0 in our case, is matched with a new building, say BS1, enters the system and is auto-matched.

Here BS1 entered the system and was matched with BS0. When a match occurs, a new BuildingSnapshot is created, BS2, with the fields from the existing BuildingSnapshot, BS0, and the new BuildingSnapshot, BS1, merged together. If both the existing and new BuildingSnapshot have data for a given field, the new record’s fields are preferred and merged into the child, B3.

The fields from new snapshot are preferred because that is the newer of the two records from the perspective of the system. By preferring the most recent fields this allows for evolving building snapshots over time. For example, if an existing canonical record has a Site EUI value of 75 and some changes happen to a building that cause this to change to 80 the user can submit a new record with that change.

All BuildingSnapshot instances point to a CanonicalBuilding.

BS0  BS1
  \ /
  BS2 <-- CB0

BS0 --> CB0
BS1 --> CB0
BS2 --> CB0

parents and children

BuildingSnapshots also have linkage to other BuildingSnapshots in order to keep track of their parents and children. This is represented in the Django model as a many-to-many relation from BuildingSnapshot to BuildingSnapshot. It is represented in the PostgreSQL database as an additional seed_buildingsnapshot_children table.

In our case here, BS0 and BS1 would both have children BS2, and BS2 would have parents BS0 and BS1.

Note

throughout most of the application, the search_buildings endpoint is used to search or list active building. This is to say, buildings that are pointed to by an active CanonicalBuilding. The search_mapping_results endpoint allows the search of buildings regardless of whether the BuildingSnapshot is pointed to by an active CanonicalBuilding or not and this search is needed during the mapping preview and matching sections of the application.

For illustration purposes let’s suppose BS2 and a new building BS3 match to form a child BS4.

parent

child

BS0

BS2

BS1

BS2

BS2

BS4

BS3

BS4

And the corresponding tree would look like:

BS0  BS1
  \ /
  BS2  BS3
    \  /
     BS4 <-- CB0

BS0 --> CB0
BS1 --> CB0
BS2 --> CB0
BS3 --> CB0
BS4 --> CB0

matching

During the auto-matching process, if a raw BuildingSnapshot matches an existing BuildingSnapshot instance, then it will point to the existing BuildingSnapshot instance’s CanonicalBuilding. In the case where there is no existing BuildingSnapshot to match, a new CanonicalBuilding will be created, as happened to B0 and C0 above.

field

BS0

BS1

BS2 (child)

id1

11

11

11

id2

12

12

id3

13

13

id4

14

15

15

manual-matching vs auto-matching

Since BuildingSnapshots can be manually matched, there is the possibility for two BuildingSnapshots each with an active CanonicalBuilding to match and the system has to choose to move only one CanonicalBuilding to the tip of the tree for the primary BuildingSnapshot and deactivate the secondary BuildingSnapshot’s CanonicalBuilding.

Take for example:

BS0  BS1
  \ /
  BS2  BS3
    \  /
     BS4 <-- CB0 (active: True)         BS5 <-- CB1 (active: True)

If a user decides to manually match BS4 and BS5, the system will take the primary BuildingSnapshot’s CanonicalBuilding and have it point to their child and deactivate CB1. The deactivation is handled by setting a field on the CanonicalBuilding instance, active, from True to False.

Here is what the tree would look like after the manual match of BS4 and BS5:

BS0  BS1
  \ /
  BS2  BS3
    \  /
     BS4  BS5 <-- CB1 (active: False)
       \  /
        BS6 <-- CB0 (active: True)

Even though BS5 is pointed to by a CanonicalBuilding, CB1, BS5 will not be returned by the normal search_buildings endpoint because the CanonicalBuilding pointing to it has its field active set to False.

Note

anytime a match is unmatched the system will create a new CanonicalBuilding or set an existing CanonicalBuilding’s active field to True for any leaf BuildingSnapshot trees.

what really happens to the BuildingSnapshot table on import (and when)

The above is conceptually what happens but sometimes the devil is in the details. Here is what happens to the BuildingSnapshot table in the database when records are imported.

Every time a record is added at least two BuildingSnapshot records are created.

Consider the following simple record:

Property Id

Year Ending

Property Floor Area

Address 1

Release Date

499045

2000

1234

1 fake st

12/12/2000

The first thing the user is upload the file. When the user sees the “Successful Upload!” dialog one record has been added to the BuildingSnapshot table.

This new record has an id (73700 in this case) and a created and modified timestamp. Then there are a lot of empty fields and a source_type of 0. Then there is the extra_data column which contains the contents of the record in key-value form:

Address 1

“1 fake st”

Property Id

“499045”

Year Ending

“2000”

Release Date

“12/12/2000”

Property Floor Area

“1234”

And a corresponding extra_data_sources that looks like

Address 1

73700

Property Id

73700

Year Ending

73700

Release Date

73700

Property Floor Area

73700

All of the fields that look like _source_id are also populated with 73700 E.G. owner_postal_code_source_id.

The other fields of interest are the organization field which is populated with the user’s default organization and the import_file_id field which is populated with a reference to a data_importer_importfile record.

At this point the record has been created before the user hits the “Continue to data mapping” button.

The second record (id = 73701) is created by the time the user gets to the screen with the “Save Mappings” button. This second record has the following fields populated:

  • id

  • created

  • modified

  • pm_property_id

  • year_ending

  • gross_floor_area

  • address_line_1

  • release_date

  • source_type (this is 2 instead of 0 as with the other record)

  • import_file_id

  • organization_id.

That is all. All other fields are empty. In this case that is all that happens.

Now consider the same user uploading a new file from the next year that looks like

Property Id

Year Ending

Property Floor Area

Address 1

Release Date

499045

2000

1234

1 fake st

12/12/2001

As before one new record is created on upload. This has id 73702 and follows the same pattern as 73700. And similarly 73703 is created like 73701 before the “Save Mappings” button appears.

However this time the system was able to make a match with an existing record. After the user clicks the “Confirm mappings & start matching” button a new record is created with ID 73704.

73704 is identical to 73703 (in terms of contents of the BuildingSnapshot table only) with the following exceptions:

  • created and modified timestamps are different

  • match type is populated and has a value of 1

  • confidence is populated and has a value of .9

  • source_type is 4 instead of 2

  • canonical_building_id is populated with a value

  • import_file_id is NULL

  • last_modified_by_id is populated with value 2 (This is a key into the landing_seeduser table)

  • address_line_1_source_id is 73701

  • gross_floor_area_source_id is populated with value 73701

  • pm_property_id_source_id is populated with 73701

  • release_date_source_id is populated with 73701

  • year_ending_source_id is populated with 73701

what really happens to the CanonicalBuilding table on import (and when)

In addition to the BuildingSnapshot table the CanonicalBuilding table is also updated during the import process. To summarize the above 5 records were created in the BuildingSnapshot table:

  1. 73700 is created from the raw 2000 data

  2. 73701 is the mapped 2000 data,

  3. 73702 is created from the raw 2001 data

  4. 73703 is the mapped 2001 data

  5. 73704 is the result of merging the 2000 and 2001 data.

In this process CanonicalBuilding is updated twice. First when the 2000 record is imported the CanonicalBuilding gets populated with one new row at the end of the matching step. I.E. when the user sees the “Load More Data” screen. At this point there is a new row that looks like

id

active

canonical_building_id

20505

TRUE

73701

At this point there is one new canonical building and that is the BuildingSnapshot with id 73701. Next the user uploads the 2001 data. When the “Matching Results” screen appears the CanonicalBuilding table has been updated. Now it looks like

id

active

canonical_building_id

20505

TRUE

73704

There is still only one canonical building but now it is the BuildingSnapshot record that is the result of merging the 2000 and 2001 data: id = 73704.

organization

BuildingSnapshots belong to an Organization field that is a foreign key into the organization model (orgs_organization in Postgres).

Many endpoints filter the buildings based on the organizations the requesting user belongs to. E.G. get_buildings changes which fields are returned based on the requesting user’s membership in the BuildingSnapshot’s organization.

*_source_id fields

Any field in the BuildingSnapshot table that is populated with data from a submitted record will have a corresponding _source_id field. E.G pm_property_id has pm_property_id_source_id, address_line_1 has address_line_1_source_id, etc…

These are foreign keys into the BuildingSnapshot that is the source of that value. To extend the above table

field

BS0

BS1

BS2 (child)

BS2 (child) _source_id

id1

11

11

BS0

id2

12

12

BS1

NOTE: The BuildingSnapshot records made from the raw input file have all the _source_id fields populated with that record’s ID. The non-canonical BuildingSnapshot records created from the mapped data have none set. The canonical BuildingSnapshot records that are the result of merging two records have only the _source_id fields set where the record itself has data. E.G. in the above address_line_1 is set to “1 fake st.” so there is a value in the canonical BuildingSnapshot’s address_line_1_source_id field. However there is no block number so block_number_source_id is empty. This is unlike the two raw BuildingSnapshot records who also have no block_number but nevertheless have a block_number_source_id populated.

extra_data

The BuildingSnapshot model has many “named” fields. Fields like “address_line_1”, “year_built”, and “pm_property_id”. However the users are allowed to submit files with arbitrary fields. Some of those arbitrary fields can be mapped to “named” fields. E.G. “Street Address” can usually be mapped to “Address Line 1”. For all the fields that cannot be mapped like that there is the extra_data field.

extra_data is Django json field that serves as key-value storage for other user-submitted fields. As with the other “named” fields there is a corresponding extra_data_sources field that serves the same role as the other _source_id fields. E.G. If a BuildingSnapshot has an extra_data field that looks like

an_unknown_field

1

something_else

2

It should have an extra_data_sources field that looks like

an_unknown_field

some_BuildingSnapshot_id

something_else

another_BuildingSnapshot_id

saving and possible data loss

When saving a Property file some fields that are truncated if too long. The following are truncated to 255 characters

  • jurisdiction_tax_lot_id

  • pm_property_id

  • custom_id_1

  • ubid

  • lot_number

  • block_number

  • district

  • owner

  • owner_email

  • owner_telephone

  • owner_address

  • owner_city_state

  • owner_postal_code

And the following are truncated to 255:

  • property_name

  • address_line_1

  • address_line_2

  • city

  • postal_code

  • state_province

  • building_certification

No truncation happens to any of the fields stored in extra_data.

Data Quality

Data quality checks are run after the data are paired, during import of Properties/TaxLots, or on-demand by selecting rows in the inventory page and clicking the action button. This checks whether any default or user-defined Rules are broken or satisfied by Property/TaxLot records.

Notably, in most cases when data quality checks are run, Labels can be applied for any broken Rules that have a Label. To elaborate, Rules can have an attached Label. When a data quality check is run, records that break one of these “Labeled Rules” are then given that Label. The case where this Label attachment does not happen is during import due to performance reasons.

Mapping

This document describes the set of calls that occur from the web client or API down to the back-end for the process of mapping data into SEED.

An overview of the process is:

  1. Import - A file is uploaded to the server

  2. Save - The file is batched saved into the database as JSON data

  3. Mapping - Mapping occurs on that file

  4. Matching / Merging

  5. Pairing

Import

From the web UI, the import process invokes seed.views.main.save_raw_data to save the data. When the data is done uploading, we need to know whether it is a Portfolio Manager file, so we can add metadata to the record in the database. The end of the upload happens in seed.data_importer.views.DataImportBackend.upload_complete. At this point, the request object has additional attributes for Portfolio Manager files. These are saved in the model seed.data_importer.models.ImportFile.

Mapping

Once files are uploaded, file header columns need to be mapped to SEED columns. Mappings can be specified/decided manually for any particular file import, or mapping profiles can be created and subsequently applied to any file imports.

When a column mapping profile is applied to an import file, file header columns defined in the profile must match exactly (spaces, lowercase, uppercase, etc.) in order for the corresponding SEED column information to be used/mapped.

Matching

Todo

document

Pairing

Todo

document

Modules

Configuration

Submodules

Template Context

:copyright (c) 2014 - 2020, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Department of Energy) and contributors. All rights reserved. # NOQA :author

config.template_context.sentry_js(request)
config.template_context.session_key(request)

Tests

Utils

:copyright (c) 2014 - 2020, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Department of Energy) and contributors. All rights reserved. # NOQA :author

config.utils.de_camel_case(name)

Views

WSGI

Data Package

Submodules

BEDES

Module contents

Data Importer Package

Submodules

Managers

Models

URLs

Utils

Views

Module contents

Features Package

Submodules

Module contents

Landing Package

Subpackages

seed.landing.management package
Subpackages
Landing Management Package
Submodules
Update EULA
Module contents
Module contents

Submodules

Forms

Models

Tests

URLs

Views

Module contents

Library Packages

Submodules

Module contents

Mapping Package

Submodules

seed.mappings.mapper module

seed.mappings.seed_mappings module

Module contents

Managers Package

Subpackages

Manager Tests Package
Submodules
Test JSON Manager
Module contents

Submodules

JSON

Module contents

Models

Submodules

AuditLog

Columns

Cycles

Joins

Generic Models

Projects

Properties

TaxLots

Module contents

Public Package

Submodules

Models

Module contents

SEED Package

Subpackages

Management Package
Subpackages
Module contents
Templatetags Package
Submodules
Test Helpers Package
Subpackages
Test Helper Factor Package
Subpackages
Test Helper Factory Lib Package
Submodules
Chomsky
Submodules
Helpers
Module contents
Tests Package
Submodules
Admin Views
Decorators
Exporters
Models
Tasks
Views
Tests
Utils

Inheritance

Submodules

Decorators

Factory

Models

Tasks

Token Generator

URLs

Utils

Views

Module contents

Serializers Package

Submodules

Serializers

Labels

Module contents

URLs Package

Submodules

Accounts

APIs

Main

Projects

Utilities Package

Submodules

APIs

Buildings

Constants

Mappings

Organizations

Projects

Time

Views Package

Submodules

Accounts

APIs

Main

Meters

Projects

Module contents

Developer Resources

General Notes

Flake Settings

Flake is used to statically verify code syntax. If the developer is running flake from the command line, they should ignore the following checks in order to emulate the same checks as the CI machine.

Code

Description

E402

module level import not at top of file

E501

line too long (82 characters) or max-line = 100

E731

do not assign a lambda expression, use a def

W503

line break occurred before a binary operator

W504

line break occurred after a binary operator

To run flake locally call:

tox -e flake8

Django Notes

Adding New Fields to Database

Adding new fields to SEED can be complicated since SEED has a mix of typed fields (database fields) and extra data fields. Follow the steps below to add new fields to the SEED database:

  1. Add the field to the PropertyState or the TaxLotState model. Adding fields to the Property or TaxLot models is more complicated and not documented yet.

  2. Add field to list in the following locations:

  • models/columns.py: Column.DATABASE_COLUMNS

  • TaxLotState.coparent or PropertyState.coparent: SQL query and keep_fields

  1. Run ./manage.py makemigrations

  2. Add in a Python script in the new migration to add in the new column into every organizations list of columns. Note that the new_db_fields will be the same as the data in the Column.DATABASE_COLUMNS that were added.

    def forwards(apps, schema_editor):
        Column = apps.get_model("seed", "Column")
        Organization = apps.get_model("orgs", "Organization")
    
        new_db_fields = [
            {
                'column_name': 'geocoding_confidence',
                'table_name': 'PropertyState',
                'display_name': 'Geocoding Confidence',
                'data_type': 'number',
            }, {
                'column_name': 'geocoding_confidence',
                'table_name': 'TaxLotState',
                'display_name': 'Geocoding Confidence',
                'data_type': 'number',
            }
        ]
    
        # Go through all the organizatoins
        for org in Organization.objects.all():
            for new_db_field in new_db_fields:
                columns = Column.objects.filter(
                    organization_id=org.id,
                    table_name=new_db_field['table_name'],
                    column_name=new_db_field['column_name'],
                    is_extra_data=False,
                )
    
                if not columns.count():
                    new_db_field['organization_id'] = org.id
                    Column.objects.create(**new_db_field)
                elif columns.count() == 1:
                    # If the column exists, then just update the display_name and data_type if empty
                    c = columns.first()
                    if c.display_name is None or c.display_name == '':
                        c.display_name = new_db_field['display_name']
                    if c.data_type is None or c.data_type == '' or c.data_type == 'None':
                        c.data_type = new_db_field['data_type']
                    c.save()
                else:
                    print("  More than one column returned")
    
    
    class Migration(migrations.Migration):
        dependencies = [
            ('seed', '0090_auto_20180425_1154'),
        ]
    
        operations = [
            ... existing db migrations ...,
            migrations.RunPython(forwards),
        ]
    
  3. Run migrations ./manage.py migrate

  4. Run unit tests, fix failures. Below is a list of files that need to be fixed (this is not an exhaustive list)

  • test_mapping_data.py:test_keys

  • test_columns.py:test_column_retrieve_schema

  • test_columns.py:test_column_retrieve_db_fields

  1. (Optional) Update example files to include new fields

  2. Test import workflow with mapping to new fields

AngularJS Integration Notes

Template Tags

Angular and Django both use {{ and }} as variable delimiters, and thus the AngularJS variable delimiters are renamed {$ and $}.

window.BE.apps.seed = angular.module('BE.seed', ['$interpolateProvider'], function ($interpolateProvider) {
        $interpolateProvider.startSymbol("{$");
        $interpolateProvider.endSymbol("$}");
    }
);

Django CSRF Token and AJAX Requests

For ease of making angular $http requests, we automatically add the CSRF token to all $http requests as recommended by http://django-angular.readthedocs.io/en/latest/integration.html#xmlhttprequest

window.BE.apps.seed.run(function ($http, $cookies) {
    $http.defaults.headers.common['X-CSRFToken'] = $cookies['csrftoken'];
});

Routes and Partials or Views

Routes in static/seed/js/seed.js (the normal angularjs app.js)

SEED_app.config(['stateHelperProvider', '$urlRouterProvider', '$locationProvider', function (stateHelperProvider, $urlRouterProvider, $locationProvider) {
  stateHelperProvider
    .state({
      name: 'home',
      url: '/',
      templateUrl: static_url + 'seed/partials/home.html'
    })
    .state({
      name: 'profile',
      url: '/profile',
      templateUrl: static_url + 'seed/partials/profile.html',
      controller: 'profile_controller',
      resolve: {
        auth_payload: ['auth_service', '$q', 'user_service', function (auth_service, $q, user_service) {
          var organization_id = user_service.get_organization().id;
          return auth_service.is_authorized(organization_id, ['requires_superuser']);
        }],
        user_profile_payload: ['user_service', function (user_service) {
          return user_service.get_user_profile();
        }]
      }
    });
}]);

HTML partials in static/seed/partials/

Logging

Information about error logging can be found here - https://docs.djangoproject.com/en/1.7/topics/logging/

Below is a standard set of error messages from Django.

A logger is configured to have a log level. This log level describes the severity of the messages that the logger will handle. Python defines the following log levels:

DEBUG: Low level system information for debugging purposes
INFO: General system information
WARNING: Information describing a minor problem that has occurred.
ERROR: Information describing a major problem that has occurred.
CRITICAL: Information describing a critical problem that has occurred.

Each message that is written to the logger is a Log Record. The log record is stored in the web server & Celery

BEDES Compliance and Managing Columns

Columns that do not represent hardcoded fields in the application are represented using a Django database model defined in the seed.models module. The goal of adding new columns to the database is to create seed.models.Column records in the database for each column to import. Currently, the list of Columns is dynamically populated by importing data.

There are default mappings for ESPM are located here:

Resetting the Database

This is a brief description of how to drop and re-create the database for the seed application.

The first two commands below are commands distributed with the Postgres database, and are not part of the seed application. The third command below will create the required database tables for seed and setup initial data that the application expects (initial columns for BEDES). The last command below (spanning multiple lines) will create a new superuser and organization that you can use to login to the application, and from there create any other users or organizations that you require.

Below are the commands for resetting the database and creating a new user:

createuser -U seed seeduser

psql -c 'DROP DATABASE "seeddb"'
psql -c 'CREATE DATABASE "seeddb" WITH OWNER = "seeduser";'
psql -c 'GRANT ALL PRIVILEGES ON DATABASE "seeddb" TO seeduser;'
psql -c 'ALTER USER "seeduser" CREATEDB CREATEROLE SUPERUSER;'
psql -d seeddb -c 'CREATE EXTENSION IF NOT EXISTS postgis;'
psql -d seeddb -c 'CREATE EXTENSION IF NOT EXISTS timescaledb;'
psql -d seeddb -c 'SELECT timescaledb_pre_restore();'
psql -d seeddb -c 'SELECT timescaledb_post_restore();'

./manage.py migrate
./manage.py create_default_user \
    [email protected] \
    --password=password \
    --organization=testorg

Restoring a Database Dump

psql -c 'DROP DATABASE "seeddb";'
psql -c 'CREATE DATABASE "seeddb" WITH OWNER = "seeduser";'
psql -c 'GRANT ALL PRIVILEGES ON DATABASE "seeddb" TO "seeduser";'
psql -c 'ALTER USER "seeduser" CREATEDB CREATEROLE SUPERUSER;'
psql -d seeddb -c 'CREATE EXTENSION IF NOT EXISTS postgis;'
psql -d seeddb -c 'CREATE EXTENSION IF NOT EXISTS timescaledb;'
psql -d seeddb -c 'SELECT timescaledb_pre_restore();'

# restore a previous database dump (must be pg_restore 12+)
/usr/lib/postgresql/12/bin/pg_restore -U seeduser -d seeddb /backups/prod-backups/seedv2_20191203_000002.dump

psql -d seeddb -c 'SELECT timescaledb_post_restore();'

./manage.py migrate

# if needed add a user to the database
./manage.py create_default_user \
    [email protected] \
    --password=password \
    --organization=testorg

Migrating the Database

Migrations are handles through Django; however, various versions have customs actions for the migrations. See the migrations page for more information based on the version of SEED.

Testing

JS tests can be run with Jasmine at the url /angular_js_tests/.

Python unit tests are run with

python manage.py test --settings=config.settings.test
Note on geocode-related testing:

Most of these tests use VCR.py and cassettes to capture and reuse recordings of HTTP requests and responses. Given that, unless you want to make changes and/or refresh the cassettes/recordings, there isn’t anything needed to run the geocode tests.

In the case that the geocoding logic/code is changed or you’d like to the verify the MapQuest API is still working as expected, you’ll need to run the tests with a small change. Namely, you’ll want to provide the tests with an API key via an environment variable called “TESTING_MAPQUEST_API_KEY” or within your local_untracked.py file with that same variable name.

In order to refresh the actual cassettes, you’ll just need to delete or move the old ones which can be found at “.seed/tests/data/vcr_cassettes”. The API key should be hidden within the cassettes, so these new cassettes can and should be pushed to GitHub.

Run coverage using

coverage run manage.py test --settings=config.settings.test
coverage report --fail-under=83

Python compliance uses PEP8 with flake8

flake8
# or
tox -e flake8

JS Compliance uses jshint

jshint seed/static/seed/js

Best Practices

  1. Make sure there is an issue created for items you are working on (for tracking purposes and so that the item appears in the changelog for the release)

  2. Use the following labels on the GitHub issue:

    Feature (features will appear as “New” item in the changelog) Enhancement (these will appear as “Improved” in the changelog) Bug (these will appear as “Fixed” in the changelog)

  3. Move the ticket/issue to ‘In Progress’ in the GitHub project tracker when you begin work

  4. Branch off of the ‘develop’ branch (unless it’s a hotfix for production)

  5. Write a test for the code added.

  6. Make sure to test locally. note that all branches created and pushed to GitHub will also be tested automatically.

  7. When done, create a pull request (you can group related issues together in the same PR). Assign a reviewer to look over the code

  8. Use the “DO NOT MERGE” label for Pull Requests that should not be merged

  9. When PR has been reviewed and approved, move the ticket/issue to the ‘Ready to Deploy to Dev’ box in the GitHub project tracker.

Release Instructions

To make a release do the following:

  1. Github admin user, on develop branch: update the package.json file with the most recent version number. Always use MAJOR.MINOR.RELEASE.

  2. Update the docs/sources/migrations.rst file with any required actions.

  3. Run the docs/scripts/change_log.py script and add the changes to the CHANGELOG.md file for the range of time between last release and this release. Only add the Closed Issues. Also make sure that all the pull requests have a related Issue in order to be included in the change log.

python docs/scripts/change_log.py –k GITHUB_API_TOKEN –s 2018-02-26 –e 2018-05-30
  1. Paste the results (remove unneeded Accepted Pull Requests) into the CHANGELOG.md. Make sure to cleanup the formatting.

  2. Make sure that any new UI needing localization has been tagged for translation, and that any new translation keys exist in the lokalise.com project. (see translation documentation).

  3. Once develop passes, then create a new PR from develop to master.

  4. Draft new Release from Github (https://github.com/SEED-platform/seed/releases).

  5. Include list of changes since previous release (i.e. the content in the CHANGELOG.md)

  6. Verify that the Docker versions are built and pushed to Docker hub (https://hub.docker.com/r/seedplatform/seed/tags/).

  7. Go to Read the Docs and enable the latest version to be active (https://readthedocs.org/dashboard/seed-platform/versions/)

License

Copyright (c) 2014 - 2020, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Department of Energy) and contributors. All rights reserved.

1. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

(1) Redistributions of source code must retain the copyright notice, this list of conditions and the following disclaimer. (2) Redistributions in binary form must reproduce the copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. (3) Neither the name of the University of California, Lawrence Berkeley National Laboratory, U.S. Dept. of Energy nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. (4) Neither the names Standard Energy Efficiency Data Platform, Standard Energy Efficiency Data, SEED Platform, SEED, derivatives thereof nor designations containing these names, may be used to endorse or promote products derived from this software without specific prior written permission from the U.S. Dept. of Energy.

2. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Help

For SEED-Platform Users

Please visit our User Support website for tutorials and documentation to help you learn how to use SEED-Platform.

https://sites.google.com/a/lbl.gov/seed/

There is also a link to the SEED-Platform Users forum, where you can connect with other users.

https://groups.google.com/forum/#!forum/seed-platform-users

For direct help on a specific problem, please email: SEED-Support@lists.lbl.gov

For SEED-Platform Developers

The Open Source code is available on the Github organization SEED-Platform:

https://github.com/SEED-platform

Please join the SEED-Platform Dev forum where you can connect with other developers.

https://groups.google.com/forum/#!forum/seed-platform-dev

Frequently Asked Questions

Here are some frequently asked questions and/or issues.

Questions

What is the SEED Platform?

The Standard Energy Efficiency Data (SEED) Platform™ is a web-based application that helps organizations easily manage data on the energy performance of large groups of buildings. Users can combine data from multiple sources, clean and validate it, and share the information with others. The software application provides an easy, flexible, and cost-effective method to improve the quality and availability of data to help demonstrate the economic and environmental benefits of energy efficiency, to implement programs, and to target investment activity.

The SEED application is written in Python/Django, with AngularJS, Bootstrap, and other JavaScript libraries used for the front-end. The back-end database is required to be PostgreSQL.

The SEED web application provides both a browser-based interface for users to upload and manage their building data, as well as a full set of APIs that app developers can use to access these same data management functions.

Work on SEED Platform is managed by the National Renewable Energy Laboratory, with funding from the U.S. Department of Energy.

Issues

Why is the domain set to example.com?

If you see example.com in the emails that are sent from your hosted version of SEED then you will need to update your django sites object in the database.

$ ./manage.py shell

from django.contrib.sites.models import Site
one = Site.objects.all()[0]
one.domain = 'newdomain.org'
one.name = 'SEED'
one.save()

Why aren’t the static assets being served correctly?

Make sure that your local_untracked.py file does not have STATICFILES_STORAGE set to anything. If so, then comment out that section and redeploy/recollect/compress your static assets.

Updating this documentation

This python code documentation was generated by running the following:

$ pip install -r requirements/local.txt
$ sphinx-apidoc -o docs/source/modules . seed/lib/mcm seed/lib/superperms
$ cd docs
$ make html

Indices and tables