waterbeacon

Water quality data

Water quality index calc

You'll need to install python3 along with pip.

Next, you'll need to install python virtual environment wrapper. You can do so by running pip install virtualenvwrapper.

Now, activate a python virtual environment by running mkvirtualenv ${ENV_NAME}. The ENV_NAME is arbitrary. We'll use "wb" for simplicity.

Finally, run pip install -r requirements.txt to install necessarry dependencies.

Before moving forward, make sure that you have postgresql installed and running. Run brew install postgresql

Make sure you have the folling tools are installed:

• Postgres
• pgAdmin
• PSequel
• You may need to manually install GeoDjango. If you have trouble with it, consider using brew.

May be required:

• libpq-dev
• postgis
• gdal-bin

Create an empty .env file touch .env.

Create the waterbeacon database by running createdb waterbeacon

(Optional - Linux Users) You may need to create a new Postgres User or update the default Postgres User to have access to the database. Do this by running the following:

sudo su - postgres

psql

CREATE USER myprojectuser WITH PASSWORD 'password';

GRANT ALL PRIVILEGES ON DATABASE waterbeacon TO myprojectuser;

You should then update the 'USER' and 'PASSWORD' to the dev.py file in the settings folder to your newly created postgres user.

When that completes, run ./manage.py migrate --settings=settings.dev to create a local postgres db instance for development.

(Optional - never necessary if csvs exist) To get all the new facility location data from the EPA, you can run ./manage.py download_epa_facility_data --settings=settings.dev

(Optional - never necessary if csvs exist) To get all the new SDWA data from the EPA, you can run ./manage.py download_epa_water_data --settings=settings.dev

(Optional) Populate the rawdata app with EPA data by running the following:

./manage.py import_epa_facility_data --settings=settings.dev;./manage.py import_epa_water_data --settings=settings.dev;./manage.py insert_facility_fips --settings=settings.dev;./manage.py data_cruncher --settings=settings.dev

Finally, run ./manage.py runserver --settings=settings.dev to kick off a dev server.

The dev server will run on localhost:8000 by default. If you prefer another port, just pass the desired port number following the previous command. That is ./manage.py runserver --settings=settings.dev ${PORT}

Note: to deactive the virtual env wrapper, just run deactivate.

Navigate to the frontend directory where the react app is located and run install

• cd frontend
• npm install

After installation you can run npm start which will run the react app on localhost:3000 with "hot reload" enabled.

In primary folder create a .env file and place the following parameters:

DEBUG=
DATABASE_URL=
PG_USERNAME=
PG_PASSWORD=
ENVIRONMENT=
DJANGO_SETTINGS_MODULE=
CELERY_BROKER_URL=
FLOWER_PORT=
TWITTER_ACCESS_TOKEN_SECRET=
TWITTER_ACCESS_TOKEN=
TWITTER_CONSUMER_SECRET=
TWITTER_CONSUMER_KEY=
VIRTUAL_PORT=
SENTRY_DSN=
EMAIL_FROM=

Within ./frontend folder create a .env file and place the following parameters:

VIRTUAL_HOST=[domain]