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ninamiolane
requested changes
Jan 19, 2026
| from bispectrum import SO3onS2 | ||
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| def power_spectrum(coeffs: torch.Tensor) -> torch.Tensor: |
Contributor
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can we call it spherical_power_spectrum?
"power spectrum" makes it sound like it's the 2D translation fourier power spectrum and its confusing.
| # Power law decay l^-2 with meaningful high-frequency content | ||
| torch.manual_seed(42) # Reproducibility | ||
| truth_coeffs = torch.randn(1, LMAX + 1, LMAX + 1, dtype=torch.complex64, device=device) | ||
| for l in range(LMAX + 1): |
Contributor
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I think with the convention for lmax from SHT, you don't need to do the "+1". Double check?
I think their arrays are of length lmax, that is range(lmax).
| # Pre-compute the "Structural Fingerprint" (rotation-invariant bispectrum) | ||
| # In practice, this could come from physical constraints or a reference signal | ||
| with torch.no_grad(): | ||
| truth_invariants = bsp_module(truth_coeffs) |
| # --- 2. Generate "Input" (Blurry Observation) --- | ||
| # Heavily dampen high frequencies to simulate degraded observation | ||
| input_coeffs = truth_coeffs.clone().detach() | ||
| for l in range(3, LMAX + 1): |
| from bispectrum import SO3onS2 | ||
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| def power_spectrum(coeffs: torch.Tensor) -> torch.Tensor: |
Contributor
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This is already define din the other file
| return corr.detach().cpu().numpy() | ||
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| def run_deblurring_demo(): |
Contributor
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Not sure I understand the difference btw this demo and the other one. Clarify docstrings or remove one of the two files
| from bispectrum import SO3onS2 | ||
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| def power_spectrum(coeffs: torch.Tensor) -> torch.Tensor: |
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