In this project, I got the chance to demonstrate my proficiency in Python, building a simple web-based dashboard to visualize real-time process data originating from one of the bioreactors.

The data is taken from Bioreactors. These bioreactors are purpose-built for cultivated meat, with a mission to 100x the industry’s capacity by 2035. The Data Systems team in these bioreactors is generally responsible for everything from production, model-based control software to web-based data monitoring applications. This project is to demonstrate my abilities in this domain

In this directory, you'll find a Dockerfile that defines the image your code will be copied into and installed in. Specifically, your source code will be installed into a Python 3.10 virtual environment as a package via pip, along with any dependencies you've specified in a requirements.txt file.

You'll also find a compose.yaml file that defines the container that'll be used to run your code. Specifically, to serve your web-based dashboard in a local browser at http://localhost:8888/, Docker is configured to start the container by executing run-app, the expected entrypoint for your application.

The data I will be visualizing is in a Postgres database, also configured in compose.yaml. Credentials to access this database are confidential at the moment. But the file can be used to use another database and configure it.

• POSTGRES_HOST provides the host
• POSTGRES_PORT provides the port
• POSTGRES_USER provides the user
• POSTGRES_PASSWORD provides the password
• POSTGRES_DB provides the database

An example can be found in local.env. Note that these will be subject to change, so make sure not to hard code these.

The tables in the database:

brx1=# \dt
                      List of relations
 Schema |           Name           | Type  |      Owner   
--------+--------------------------+-------+------------------
 public | CM_HAM_DO_AI1/Temp_value | table | process_trending
 public | CM_HAM_PH_AI1/pH_value   | table | process_trending
 public | CM_PID_DO/Process_DO     | table | process_trending
 public | CM_PRESSURE/Output       | table | process_trending

Each table has the same schema, like so:

brx1=# \d public."CM_HAM_DO_AI1/Temp_value"
                Table "public.CM_HAM_DO_AI1/Temp_value"
 Column |            Type             | Collation | Nullable | Default 
--------+-----------------------------+-----------+----------+---------
 time   | timestamp without time zone |           |          | 
 value  | double precision            |           |          | 

Each table contains the following data:

Run docker compose up and navigate your browser to http://localhost:8888/. That's it!

The dashboard allows the user to plot each of these four series (Temperature, pH, Distilled Oxygen, and Pressure) over time.

• Package installs successfully
• Dashboard can be viewed at http://localhost:8888/. It looks good
• Code is high quality, e.g. It follows PEP8. It is it fully type annotated,and there are comments

• User can select the time window
• Added a button to refresh the data without refreshing the page, or auto-refresh the page for the user
• Added a "Download as csv" button for the specific time period