You can build a simple color spectrum picker using hsv values and a couple of sliders in Pd. Then just convert them to RGB with hsv2rgb. It’s not fancy, but it works pretty well for basic color picking!
You may also find this free tool helpful. https://colorpickerimage.org/

Hey — this is a classic issue: readsf~ doesn’t natively support MP3 / compressed formats in Pd / Purr-Data, so it’ll refuse with “bad header” errors on .mp3, .flac, etc.
A couple of workarounds you might try-

• Convert your MP3s to WAV (or another PCM format) and use those with readsf~

• Use an external based on FFmpeg / ffplay (someone on the forum suggested that route)

• Or load via another object that supports compressed formats (if available / compiled in your build)

If you tell me your OS and Purr-Data build, I can suggest a patch or external you can drop in that does support mp3 inside Pd.
Alternatively, check out this free tool. https://editmp3tags.com/

Hey — good question. Here’s a thought:
You could use [env~] to get RMS-levels, convert that to dB via 20 * log10(rms), and then calibrate by comparing with your real SLM (sound level meter). The catch is, your mic + ADC chain has unknown gain + frequency response, so you’ll likely need a correction factor (plus maybe an A-weighting filter).
If you like, I can help you build a small Pd patch that applies A-weighting + calibration so your values better match your SLM.
You can also try this free tool. https://noisemeter.co/#

Hey buglover, that's a seriously cool project! Building an actual calibrated SLM in Pd is pretty deep stuff.
For the dB(A) filter, I'd definitely recommend checking if any of the external libraries you're using (like iemlib) have a pre-cooked filter for the A-weighting curve. Trying to implement that IIR from scratch can be brutal, as you found with fexpr~!
And yes, absolutely go for the RT-kernel on the Pi. For any real-time audio and especially for continuous logging like an SLM, having that low-latency stability makes a huge difference and will save you endless headaches down the road.
You might find this free tool helpful. https://noisemeter.co/