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Merged
Jammy2211 merged 2 commits into from
Feb 12, 2026
Merged

feature/ell_comps_division_0_bug_fix #278

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19d0be2
revert geometry profiles to use angle method
Feb 12, 2026
85e78ab
same zero guard applied to shear and multipole
Feb 12, 2026
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17 changes: 12 additions & 5 deletions autogalaxy/convert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,7 @@ def ell_comps_from(axis_ratio: float, angle: float, xp=np) -> Tuple[float, float
return (ellip_y, ellip_x)



def axis_ratio_and_angle_from(
ell_comps: Tuple[float, float], xp=np
) -> Tuple[float, float]:
Expand Down Expand Up @@ -60,7 +61,7 @@ def axis_ratio_and_angle_from(
ell_comps
The elliptical components of the light or mass profile which are converted to an angle.
"""
angle = xp.arctan2(ell_comps[0], ell_comps[1]) / 2
angle = 0.5 * xp.arctan2(ell_comps[0], xp.where((ell_comps[0] == 0) & (ell_comps[1] == 0), 1.0, ell_comps[1]))
angle *= 180.0 / xp.pi
Comment on lines +64 to 65
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Copilot AI Feb 12, 2026

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Add regression coverage for the new (0,0) elliptical-components branch. In particular, test that axis_ratio_and_angle_from(ell_comps=(0.0, 0.0)) returns finite values (axis_ratio ~ 1.0, angle ~ 0.0) for both NumPy and JAX backends (e.g. xp=jax.numpy) to ensure the original NaN/undefined-backend behavior can’t regress.

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angle = xp.where(angle < -45, angle + 180, angle)
Expand Down Expand Up @@ -197,7 +198,7 @@ def shear_magnitude_and_angle_from(
gamma_2
The gamma 2 component of the shear.
"""
angle = xp.arctan2(gamma_2, gamma_1) / 2 * 180.0 / xp.pi
angle = 0.5 * xp.arctan2(gamma_2, xp.where((gamma_1 == 0) & (gamma_2 == 0), 1.0, gamma_1)) * 180.0 / xp.pi
magnitude = xp.sqrt(gamma_1**2 + gamma_2**2)
Comment on lines +201 to 202
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Copilot AI Feb 12, 2026

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Add regression coverage for the new (0,0) shear-components branch (e.g. shear_magnitude_and_angle_from(gamma_1=0.0, gamma_2=0.0, xp=jax.numpy)), asserting the returned magnitude/angle are finite and as expected. This ensures the backend-specific edge case this guard is addressing can’t regress.

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angle = xp.where(angle < 0, angle + 180.0, angle)
Expand Down Expand Up @@ -297,9 +298,15 @@ def multipole_k_m_and_phi_m_from(
-------
The normalization and angle parameters of the multipole.
"""
phi_m = (
xp.arctan2(multipole_comps[0], multipole_comps[1]) * 180.0 / xp.pi / float(m)
)
phi_m = xp.arctan2(
multipole_comps[0],
xp.where(
(multipole_comps[0] == 0) & (multipole_comps[1] == 0),
1.0,
multipole_comps[1],
),
) * 180.0 / xp.pi / float(m)
Comment on lines +301 to +308
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Copilot AI Feb 12, 2026

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Add regression coverage for the new (0,0) multipole-components handling (e.g. multipole_k_m_and_phi_m_from(multipole_comps=(0.0, 0.0), m=2, xp=jax.numpy)), checking the returned k_m / phi_m are finite and stable. This validates the intent of the xp.where guard across supported backends.

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k_m = xp.sqrt(multipole_comps[1] ** 2 + multipole_comps[0] ** 2)

phi_m = xp.where(phi_m < -90.0 / m, phi_m + 360.0 / m, phi_m)
Expand Down
3 changes: 1 addition & 2 deletions autogalaxy/profiles/geometry_profiles.py
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Expand Up @@ -106,8 +106,7 @@ def _cartesian_grid_via_radial_from(
"""
grid_angles = xp.arctan2(grid.array[:, 0], grid.array[:, 1])

cos_theta = xp.cos(grid_angles)
sin_theta = xp.sin(grid_angles)
cos_theta, sin_theta = self.angle_to_profile_grid_from(grid_angles=grid_angles, xp=xp, **kwargs)

return xp.multiply(radius[:, None], xp.vstack((sin_theta, cos_theta)).T)

Expand Down
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