Hey Neptune, I’ve been tinkering with some real‑time tide‑prediction algorithms—mind if I run them past you? I think your ancient knowledge could really help tweak the models.

Here’s the core of the tide‑prediction routine I’ve wrapped up in Python. It pulls the last 24‑hour sea‑level record from the local buoy, normalises it, then runs a quick FFT to extract the dominant frequencies. After that I feed those into a simple sinusoidal model and interpolate for the next few hours. Feel free to point out any inefficiencies or let me know if you’d prefer a vectorised approach or a different library. ```python import numpy as np import pandas as pd from scipy.fft import rfft, rfftfreq # Load the raw sea‑level data (timestamp, height in cm) df = pd.read_csv('buoy_24h.csv', parse_dates=['timestamp']) heights = df['height_cm'].values times = df['timestamp'].values # Normalise by removing the mean and scaling to unit variance norm_heights = (heights - heights.mean()) / heights.std() # Compute the FFT n = len(norm_heights) freqs = rfftfreq(n, d=1/(24*60)) # 1 sample per minute fft_vals = rfft(norm_heights) # Identify the top 3 peaks (excluding the zero‑frequency component) peak_idx = np.argsort(np.abs(fft_vals[1:]))[-3:] + 1 peak_freqs = freqs[peak_idx] peak_amps = np.abs(fft_vals[peak_idx]) # Build a predictive model: sum of sinusoids def predict_tide(future_minutes): future_times = np.arange(future_minutes) pred = np.zeros_like(future_times, dtype=float) for amp, freq in zip(peak_amps, peak_freqs): pred += amp * np.sin(2 * np.pi * freq * future_times) # Reverse normalisation pred = pred * heights.std() + heights.mean() return pred # Example: predict the next 120 minutes future_pred = predict_tide(120) print(future_pred) ``` If you’re worried about the FFT resolution or want to incorporate a more robust physical model (like the M2 and S2 constituents), just let me know.

Neptune, thanks for the pointers – all of them make sense. I’ll adjust the sampling to the real timestamps, add an epsilon for the std guard, and keep the complex FFT so I can recover both sine and cosine components. Adding the M2 and S2 constituents will give the model a solid foundation for multi‑day forecasts. I’ll push the updated script up to the repo and drop you a link once it’s ready to test. Keep those suggestions coming; the ocean doesn’t care how much you tweak it.

Here’s the updated repo: https://github.com/skachatpro/tide‑model‑v2 I’ve added the M2, S2, and S1 constituents, and fixed the timestamp spacing and normalisation guard. Take a look, tweak as you see fit, and let me know how the predictions line up with the real tide data. Happy testing!