thermostat VS grafanalib

all the past data could be used to run simulations/verify models, but really this should be a thing that learns along the way. Like you move into a house, install thermostat and temp sensors, and then as it runs it gets better and better at cooling your house. Many hard coded things need to be turned into dynamic ones, heater functionality needs to be added, and a handful of other quality of life things need to be added, but the idea is that someone could git clone it and go even as is. https://github.com/G4te-Keep3r/thermostat ***project summary**\*Custom thermostat that does more than simple on/off cycling. The 2 main goals that it started with was to not have it 80+ degrees during the day like the old thermostat would, and for the overall consistency of temperature to be better (old thermostat had almost a 10+ degree swing before it cycled back on). This started out so simple as "build a better thermostat" and just kept getting more complicated.

How does it compare to grafanalib (Python) or grabana (Go)?

grafanalib

Grafana is pretty great. There are a lot of built in connectors, but its ability to query arbitrary REST, JSON, oData etc endpoints along with databases makes it super flexible for what you mentioned.

https://grafana.com/grafana/plugins/simpod-json-datasource

It has a great GUI for building queries, plus solid RBAC, great charting, and a very flexible alert system. Configure in their GUI, then get your dashboard's code in as JSON for deployment. You can also define dashboard in actual code, ie grafanalib in Python and others- which is great for DevOps. https://github.com/weaveworks/grafanalib

They have nice stylesheets ready for TV displays, but it gets really fun when users get their hands on it and can zoom in and drill down on time-series data, filter with dynamic parameters and such.