updating layout of gn-20 dashboard

grandnational-dashboard/README.rst

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+***************************************
+Grand National 2020 Countdown Dashboard
+***************************************
+
+This repo contains all files for the Grand National 2020 Countdown Dashboard.
+
+.. image:: https://git.nonprod.williamhill.plc/wh_capacity/grandnational-dashboard/uploads/47fdb6cc5b3a6658ee884ab148fb8a29/image.png
+  :width: 40%
+
+
+The dashboard is installed on ``sc1uxpremn81`` running in docker.
+
+The dashboard uses ``python`` as an ASGI server, ``css`` for formatting, and ``jQuery`` for front-end scripts.
+
+We use `uvicorn <https://www.uvicorn.org>`_ as an ASGI server and `gunicorn <https://gunicorn.org>`_ to manage the asynchronous workers. We use `bootstrap <https://getbootstrap.com/>`_ for front-end formatting and, `jQuery <https://jquery.com/>`_ for front-end scripts. We use `npm <https://www.npmjs.com>`_, `bower <https://bower.io/>`_ and `poetry <https://python-poetry.org>`_ to manage dependencies.
+
+Optionally we can use `docker <https://www.docker.com/>`_ to orchestrate uvicorn and guvicorn.
+
+
+Running Locally
+===============
+
+Requirements
+------------
+
+You will need installed:
+
+- python>=3.6.2
+- npm
+- docker `(optional)`
+  
+on your machine as a pre-requisite. Versions of python lower than 3.6.2 may work, but you will have to delete the ``poetry.lock`` file before installing the python dependencies. Versions lower than 3.6.2 will not come with `venv` - you will need to install an alternative (``virtualenv``) or use ``potery`` to manage the virtual environment for python.
+
+Installation Steps
+------------------
+
+Create workspace
+^^^^^^^^^^^^^^^^
+
+On your machine clone the repo:
+
+``cd ~``
+
+``git clone https://git.nonprod.williamhill.plc/wh_capacity/grandnational-dashboard.git``
+
+In this folder, create a virtual environment for python:
+
+``cd ~/grandnational-dashboard``
+
+``python3 -m venv gn-dashboard``
+
+Activate this environment:
+
+``source gn-dashboard/bin/activate``
+
+
+Install Dependencies
+^^^^^^^^^^^^^^^^^^^^
+
+Install ``bower`` using ``npm``:
+
+``npm install -g bower``
+
+Install poetry:
+
+``pip install poetry``
+
+Install the node depdencies with ``npm``:
+
+``npm install``
+
+`this reads the dependencies from package.json`.
+
+Install the bower dependencies with ``bower``:
+
+``bower install``
+
+`this reads the dependencies from bower.json`.
+
+Install the python dependencies with poetry:
+
+``poetry install --no-dev``
+
+`this reads the dependencies from pyproject.toml and poetry.lock`
+
+Copy The Large Static Files
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The video used as a banner is too large for gitlab. You should do:
+
+``cd static/assets/videos``
+
+and
+
+``wget http://sc1uxpremn81:9001/static/assets/videos/gn19-short.mp4``.
+
+Alternatively, if the dashboard isn't online, you can download the file manually from confluence:
+
+https://conf.willhillatlas.com/display/CME/Static+Files
+
+Place this video locally in ``static/assets/videos/`` saved as ``gn19-short.mp4``.
+
+Run the Webserver
+^^^^^^^^^^^^^^^^^
+
+Manually
+""""""""
+
+Running the webserver is done with ``uvicorn``. Go into ``grandnational_dashboard`` and launch uvicorn:
+
+``cd grandnational_dashboard``
+
+``uvicorn main:app``
+
+(The folder name is the same as the repo name. This may be confusing but is intentional when creating a project with poetry without a src folder. The full path you should be in is ``~/grandnational-dashboard/grandnational_dashboard`` and there should be a ``main.py`` file inside this folder.)
+
+Your dashboard should then be available at http://127.0.0.1:8000
+
+
+Docker
+""""""
+
+We can use Docker for deployment. To do this go back to the root of the project and build the docker image from the ``Dockerfile``.
+
+``cd ~/grandnational-dashboard``
+
+``docker build . -t gn-20-dash``
+
+When this has built you can run the container with:
+
+``docker run --rm --name dashboard -p 8000:80 gn-20-dash:latest``
+
+Your dashboard will then be available at http://127.0.0.1:8000.
+
+This docker image runs gunicorn to manage the asynchronous workers. It's scaleable, and will smartly use the resources of the machine it's running on.