Overview:

to understand how this project work, refer to: https://github.com/mbrochh/django-reactjs-boilerplate/tree/master

bundle has already been generated by command node_modules/.bin/webpack --config webpack.local.config.js , and all generated files are in ./djreact/static/bundles/local

Collaberations

Jin Yan(jyan16), Tong Zhang(tzhang48), Zexuan Huang(zhuang31), Zhiwei Zhang(zzhang83)

Website deployment

ec2-54-196-181-229.compute-1.amazonaws.com

How to Run React:

Our project needs python3, pip3 and npm.

1. cd to project root directory
2. run node_modules/.bin/webpack --config webpack.local.config.js to generate react bundle files. They should now be stored in backendApp/static/bundles/local.
3. npm install to install all packages
4. pip3 install -r req.txt to install all python package
5. node dev_server.js to start webpack dev server. Note that this is import to make react run correctly
6. python3 manage.py runserver to start Django

Views

There are three views in the system:

1. 'localhost:8000/' index view. You can upload csv file here. Our system will check whether your file is uploadable.
2. 'localhost:8000/data' the home page of website.

RESTful API

Our system uses AJAX to communicate between frontend and backend.

1. GET localhost:8000/all/?:

response = 
{
    'ok': bool,
    'data': {
        task_type: [
            {
                'name': string,
                'most_recent_time': string,
                'metric': string,
                'most_recent_result': 
                {
                    score_name: score_value,
                    ...
                }
            }, 
            ...
        ]
    },
    'statistic': {
        task_type: {
            {
                time: string,
                score_name: score_value,
                ...
            },
            ...
        }
    }
}

2. GET localhost:8000/dataset?data_name=my_dataset:

This request will response the individual data and its corresponding statistic result. The statistic result contains mean, median, standard deviation. Client could use data field in statistic to draw frequency density histogram for our score.

response = {
    ok: bool,
    dataset: {
        metric: string,
        most_recent_time: DateTime,
        name: string,
        type: string,
    },
    results: [
        {
            Baseline: float,
            duration: int,
            time: DateTime
        }, ...
    ],
    statistic: {
        'data': [(float, float), ...],
        'mean': float,
        'median': float,
        'standard_deviation': float,
    }
}

3. POST localhost:8000/upload?file_dir=my_file_directory:

Maintenance

Superuser for Django

1. name: jinyan
2. email: jin_yan@brown.edu
3. password: abcd12345678

normal user

1. name: tong
2. password: abcd12345678

NOTE

1. delete DATABASE_URL in your heroku config variable!!!!!!!!!
2. Compressor will not run is DEBUG is TRUE