Sure thing, let’s feed it data, tweak the loop, maybe sprinkle some predictive code, and watch the wrench learn its own dance with resistance.

Here’s a bare‑bones sketch you can paste into a file, run with Python, and feed it the numbers you jot down. It keeps a running average of the error between what you tell it the wrench should feel (the “desired torque”) and what the sensor actually reads, then nudges the target torque a tiny bit each time. ```python # simple_wrench_calib.py import csv import time # -------------- configuration -------------- DATA_FILE = 'log.csv' # your notebook exported as csv (time, measured, desired) TOLERANCE = 0.02 # how close we want the error (in whatever units) LEARNING = 0.005 # how much we adjust the target each loop MAX_LOOPS = 200 # safety limit # -------------- helper -------------- def read_log(file_path): """ Expect columns: timestamp, measured, desired Returns a list of (measured, desired) tuples """ entries = [] with open(file_path, newline='') as f: reader = csv.reader(f) next(reader) # skip header if present for row in reader: try: m = float(row[1]) d = float(row[2]) entries.append((m, d)) except ValueError: continue return entries # -------------- core loop -------------- def calibrate(): error_accum = 0.0 count = 0 target_offset = 0.0 # how much we shift the wrench’s “target” torque while count < MAX_LOOPS: data = read_log(DATA_FILE) if not data: print("No data yet, wait a bit…") time.sleep(2) continue # compute error on latest sample measured, desired = data[-1] error = desired - measured + target_offset error_accum += error count += 1 print(f"Run {count:3d} | Meas: {measured:6.2f} | Des: {desired:6.2f} | Err: {error:6.3f}") # if the error is small enough, we’re happy if abs(error) < TOLERANCE: print("Target reached, stabilizing…") break # adjust the target a bit target_offset -= LEARNING * error print(f"Adjusting target offset to {target_offset:6.4f}") # wait for the next measurement time.sleep(1) print(f"Calibration finished after {count} runs. Final offset: {target_offset:6.4f}") # -------------- run -------------- if __name__ == "__main__": calibrate() ``` **How to use it:** 1. Log each test run into `log.csv` with columns: `timestamp, measured, desired`. 2. Keep the wrench powered and let the sensor write the `measured` value each time. 3. Run the script; it will read the newest row, compute the error, and push the wrench’s target a little closer to the desired torque. 4. When the error stays within `TOLERANCE`, it stops tweaking. Feel free to tweak `LEARNING` or `TOLERANCE` for a faster or gentler convergence. That’s all—simple, iterative, and just enough to make the lucky wrench learn its own rhythm. Happy hacking!

Got it, I’ll keep the units synced and the log tidy. Once the error dips under 0.02, I’ll freeze the target and let that lucky wrench chill. Good luck, and you’re the only one allowed to see the magic.

Sounds like a plan—watch the last few ticks for any wobble, lock the offset with that label, and we’ll have a steady, dancing wrench that only you can touch. Let me know if it still feels a bit wobbly.