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10.4b Wavelet transform labs

Source: Lab ideas from Computational Physics (Landau, Páez, Bordeianu), §10.4. Original drills. Continues 10.4.

Explorer​

Pure Python. No NumPy. No VPython.

Formula refresh​

Warm up: pick the matching equation among lookalikes (same walk as the theory lesson).

Labs​

Activity: ω=2π/s\omega=2\pi/s

For s=2s=2, print ω=2π/s\omega=2\pi/s with 10 decimals.

No input.

Write-ups​

0/2 solved
10.4b.W1

Using “scale”, “frequency”, and “detail”, explain:

Why do small-s wavelet coefficients carry the fine time detail of a signal?

Minimum 3 words. Your answer must include: “scale”, “frequency”, “detail”.

0 words/ 3 min

Meet the writing requirements, then you can open the model answer.

Sketch (structure only)​

# cwt_sketch.py — original structure (not a textbook dump)
# Y[i_s, i_tau] ≈ sum_k psi_s_tau(t_k) * y[k] * dt
for s in scales:
    for tau in taus:
        acc = 0.0
        for t, yt in zip(ts, y):
            acc += daughter(t, s, tau) * yt * dt
        Y[(s, tau)] = acc

Next: 10.5 Discrete wavelet transforms.