P2.2 Analytic disk potentials: backend-agnostic namespace-swap#894
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## feat/backends #894 +/- ##
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Complete the backend-agnostic migration of the analytic disk family so the entire files (not just the main compute methods) are free of numpy. in the compute path: - KuzminDiskPotential._mass: numpy.sqrt -> xp.sqrt; drop unused numpy import. - RazorThinExponentialDiskPotential._mass: numpy.pi/numpy.exp -> math.pi/xp.exp. _z2deriv: numpy.infty (deprecated) -> math.inf. Bessel/quadrature methods (_evaluate, _Rforce, _zforce, _R2deriv) remain scipy.special-blocked (i0/i1/k0/k1/kn/iv) and are left as-is. - MiyamotoNagaiPotential: replace the numpy-only scalar fast-paths (`xp is numpy and isinstance(R, float) and sqrtbz == asqrtbz`) in _zforce, _dens, _z2deriv, _Rzderiv with a backend-agnostic config branch on `self._a == 0.0`. This is the a==0 simplification; it equals the general formula to ULP for b>0 and avoids the general formula's 0/0 (asqrtbz/sqrtbz) at the a==0, b==0, z==0 singular point under all backends (previously NaN for arrays, finite only for numpy scalars). Leftover numpy. in these files is config-time-only (leggauss nodes, astropy unit parsing, scalar setup constants) or genuinely scipy.special. Tests (tests/test_backend_disk.py): add numpy/jax/torch parity + grad-vs-FD for the migrated _mass methods, z==0 kink parity for Kuzmin _zforce/_Rzderiv/_z2deriv, MiyamotoNagai a==0 branch parity, and finite-value parity at the MiyamotoNagai a==0,b==0,z==0 singular point. Full module: 181 passed (numpy+jax+torch); numpy potential sanity (forceAsDeriv, 2ndDeriv, mass_axi, mass_axi_negz, poisson_surfdens) all pass with the C ext built. Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
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Part of the P2.x potential namespace-swap series (→ #892).
Migrated: MiyamotoNagai, KuzminDisk, FlattenedPower, IsothermalDisk, KGPotential, RazorThinExponentialDisk (
_surfdens). MN3ExponentialDisk is backend-agnostic transitively (delegates to MN).Deferred: KuzminKutuzovStaeckel (coords transform — out of family scope), RingPotential (ellipk/ellipe), RazorThin forces (Bessel), DoubleExponentialDisk (scipy).
Hazards fixed: MN
isinstance(R,float)a==0 fast-path gated onxp is numpy(numpy byte-identical; tracers always take the general autodiff-safe formula);numpy.fabs→xp.abs(torch);numpy.pi→math.pi.Tests:
tests/test_backend_disk.py— 138 passed (numpy/jax/torch parity + grad-vs-FD). Numpy path byte-identical.🤖 Generated with Claude Code