Using Olympus Planner-s to optimize SDL-Demo

[sgbaird]

I plan to adapt this notebook to one using the supported Planner-s in Olympus. Any interest in making a Jupyter notebook implementation PR in the notebooks directory using the simulator described in 2.2-sensor-simulator.ipynb? I'd then re-run this using the experimental demo and add co-authors where appropriate and if of interest. If that's asking a bit much, no worries, but figured I'd float the idea👍Planning to implement it one way or another, though it seems like it would be a lot more efficient for someone already familiar with the internals to work on it #16

It would be interesting to make an interactive Plotly figure with legend groups or similar defined according to the categories in Planners. See sparks-baird/self-driving-lab-demo#23 for additional context.

[rileyhickman]

Hello,

Would you be willing to include the SDL demo (at least the simulated one) as an Olympus dataset? This might make the implementation of this example even easier.

[sgbaird]

@rileyhickman I'd be happy to do that. Any idea how many pre-computed points you'd want?

[rileyhickman]

Well the more the merrier, but I imagine anything over 100 points would suffice. You could even just use the experimental points that you have already collected with your previous optimization experiments.

[sgbaird]

Here's a basic implementation of optimizing SDL-Demo using GpyOpt via Olympus https://colab.research.google.com/github/sparks-baird/self-driving-lab-demo/blob/main/notebooks/6.0-olympus-benchmarking-basic.ipynb based on the example you shared in #16. Still need to get the custom dataset ready.

[sgbaird]

Here's the error vs. iteration number:

[sgbaird]

Any suggestions on an upper limit for the number of datapoints? Is 10k too much? (there are six adjustable parameters - three relate to the sensor settings)

EDIT: I have a file with ~40k datapoints across 7 parameters (~5 repeats per configuration due to noise) and 8 wavelength outputs