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Add general integer exponents #128

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Add general integer exponents #128

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d67ff35
Add general integer exponents up to 6
Dec 16, 2024
d2dccf0
Merge branch 'main' into exponents
E-Rum Dec 20, 2024
ad9f194
Updated helper function for new Vesin version, started to change tests
E-Rum Dec 20, 2024
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29 changes: 25 additions & 4 deletions src/torchpme/potentials/inversepowerlaw.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
from typing import Optional

import torch
from torch.special import gammainc, gammaincc, gammaln
from torch.special import gammaln

from .potential import Potential

Expand All @@ -17,6 +17,27 @@ def gamma(x: torch.Tensor) -> torch.Tensor:
return torch.exp(gammaln(x))


# Auxilary function for stable Fourier transform implementation
def gammainc_upper_over_powerlaw(exponent, zz):
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Give the equation in the docstring here.

if exponent == 1:
return torch.exp(-zz) / zz
if exponent == 2:
return torch.sqrt(torch.pi / zz) * torch.erfc(torch.sqrt(zz))
if exponent == 3:
return -torch.expi(-zz)
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As far as I know torch.expi doesn't exist in torch module ...

if exponent == 4:
return 2 * (
torch.exp(-zz) - torch.sqrt(torch.pi * zz) * torch.erfc(torch.sqrt(zz))
)
if exponent == 5:
return torch.exp(-zz) + zz * torch.expi(-zz)
if exponent == 6:
return (
(2 - 4 * zz) * torch.exp(-zz)
+ 4 * torch.sqrt(torch.pi) * zz**1.5 * torch.erfc(torch.sqrt(zz))
) / 3
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We need an error message if the exponent is not supported.

I think there is a check already at the init of InversePowerLawPotential.

I would maybe call this function in the init of InversePowerLawPotential for the given exponent and maybe zz=0 to not have a duplicated test. But up to you.



class InversePowerLawPotential(Potential):
"""
Inverse power-law potentials of the form :math:`1/r^p`.
Expand Down Expand Up @@ -46,7 +67,7 @@ class InversePowerLawPotential(Potential):

def __init__(
self,
exponent: float,
exponent: int,
smearing: Optional[float] = None,
exclusion_radius: Optional[float] = None,
dtype: Optional[torch.dtype] = None,
Expand Down Expand Up @@ -103,7 +124,7 @@ def lr_from_dist(self, dist: torch.Tensor) -> torch.Tensor:
x = 0.5 * dist**2 / smearing**2
peff = exponent / 2
prefac = 1.0 / (2 * smearing**2) ** peff
return prefac * gammainc(peff, x) / x**peff
return self.from_dist(dist) - prefac * gammainc_upper_over_powerlaw(exponent, x)

@torch.jit.export
def lr_from_k_sq(self, k_sq: torch.Tensor) -> torch.Tensor:
Expand Down Expand Up @@ -136,7 +157,7 @@ def lr_from_k_sq(self, k_sq: torch.Tensor) -> torch.Tensor:
return torch.where(
k_sq == 0,
0.0,
prefac * gammaincc(peff, masked) / masked**peff * gamma(peff),
prefac * gammainc_upper_over_powerlaw(exponent, masked),
)

def self_contribution(self) -> torch.Tensor:
Expand Down
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