mad_dash

A suite for displaying and managing histograms stored at Madison's Data Warehouse

There are three independent applications that read/write to a common MongoDB database server: a database-interface REST server, a web application, and a client script for production.

Database REST Server

A REST server that interfaces a local MongoDB server

Getting Started

python3 -m virtualenv -p python3 mad_dash_db_server
. mad_dash_db_server/bin/activate
pip install -r db_server/requirements.txt

Optional: First Kill All Active MongoDB Daemons

sudo killall mongod

Launch Local MongoDB Server and via Docker

./db_server/resources/mongo_test_server.sh

Launch Local via Docker

./db_server/resources/token_test_server.sh

Running the Server

python -m db_server

Production Client

A script to add histograms and I3 file lists to the MongoDB DMBS

Getting Started

1. Get I3 histogram pickle file(s). The filename will be used to identify the Mongo collection
2. Have an http connection (not required for debugging)

POSTing and Updating from Pickle Files

Ingesting from a single file...

python3 production_client/ingest_pickled_collections.py FILEPATH

Ingesting from multiple files...

python3 production_client/ingest_pickled_collections.py FILEPATH1 FILEPATH2 ...

Ingesting recursively from directories...

python3 production_client/ingest_pickled_collections.py -r DIRPATH1 ...

More Options

python3 production_client/ingest_pickled_collections.py -h

Web App

A dashboard for viewing and comparing histograms

Getting Started

python3 -m virtualenv -p python3 mad_dash_web_app
. mad_dash_web_app/bin/activate
pip install -r web_app/requirements.txt

Running the Server

python -m web_app

Viewing Webpage

Locally

Go to http://localhost:8050/

Testing

Manual Testing

1. Set up and start servers (see above)
2. pytest

Automated Testing