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fix use of acceleration helper #72

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merged 4 commits into from
Jan 24, 2024
Merged

fix use of acceleration helper #72

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2ec9beb
use `_acceleration`
nstarman Jan 20, 2024
ca99061
use private gradient func
nstarman Jan 20, 2024
f16a60f
simplify further
nstarman Jan 20, 2024
a216d63
make inputs arrays
nstarman Jan 20, 2024
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16 changes: 5 additions & 11 deletions src/galax/potential/_potential/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,7 +108,7 @@ def potential_energy(
E : Array[float, *batch]
The potential energy per unit mass or value of the potential.
"""
return self._potential_energy(q, t)
return self._potential_energy(q, xp.asarray(t))

@partial_jit()
def __call__(
Expand Down Expand Up @@ -160,7 +160,7 @@ def gradient(self, q: BatchVec3, /, t: BatchableFloatOrIntScalarLike) -> BatchVe
grad : Array[float, (*batch, 3)]
The gradient of the potential.
"""
return self._gradient(q, t) # vectorize doesn't allow kwargs
return self._gradient(q, xp.asarray(t)) # vectorize doesn't allow kwargs

# ---------------------------------------
# Density
Expand Down Expand Up @@ -192,7 +192,7 @@ def density(
rho : Array[float, *batch]
The potential energy or value of the potential.
"""
return self._density(q, t)
return self._density(q, xp.asarray(t))

# ---------------------------------------
# Hessian
Expand Down Expand Up @@ -222,17 +222,11 @@ def hessian(
Array[float, (*batch, 3, 3)]
The Hessian matrix of second derivatives of the potential.
"""
return self._hessian(q, t)
return self._hessian(q, xp.asarray(t))

###########################################################################
# Convenience methods

@partial_jit()
@vectorize_method(signature="(3),()->(3)")
def _acceleration(self, q: Vec3, /, t: FloatScalar) -> Vec3:
"""See ``acceleration``."""
return -self.gradient(q, t)

def acceleration(
self, q: BatchVec3, /, t: BatchableFloatOrIntScalarLike
) -> BatchVec3:
Expand All @@ -251,7 +245,7 @@ def acceleration(
The acceleration. Will have the same shape as the input
position array, ``q``.
"""
return -self.gradient(q, t)
return -self._gradient(q, xp.asarray(t))

@partial_jit()
def tidal_tensor(
Expand Down