Comments (4)
Surely we can't be taking everything in base/special. Not having log
in Base would be heresy.
from specialfunctions.jl.
I think Faddeeva's actually C++, not Fortran? We can certainly leave log
in Base, but we can also do one thing at a time. Airy and Bessel can probably move here first.
from specialfunctions.jl.
Huh, could have sworn it was Fortran but it looks like you're right, it's C++. And Julia's own Steven G. Johnson wrote it, no less! 😄
Yeah, I figured we wouldn't do all of it at once. Bessel and airy sounds like a good place to start. I'd still like to rewrite (or at least refactor) the translated Julia code from AMOS to be more idiomatic, it just requires the mental gymnastics of following goto
s.
Hopefully moving the tests from Base will help increase the currently abysmal coverage.
from specialfunctions.jl.
I'll start a checklist here of stuff to copy over based on my understanding of the plan. Feel free to add/subtract at will.
- Airy
- Bessel
- Higher-order gamma functions (digamma et al.)
- Error functions
- Gamma, beta, zeta
from specialfunctions.jl.
Related Issues (20)
- SpecialFunctions do not compile on an Apple M2 (but on an Apple M1)? HOT 2
- TagBot trigger issue HOT 4
- Derivative of the generalised zeta function
- Add logabsgamma(::Complex) HOT 3
- incorrect reference HOT 2
- Address known-broken ChainRules tests
- Add gamma(::Complex{BigFloat}) HOT 1
- Inverse of erfi
- Reduce/remove inner allocations (e.g. `gamma_inc_taylor`, `gamma_inc_asym`) HOT 9
- Segfault in besselj HOT 2
- Regularized Kumer Functions? HOT 1
- Generalize beta, logbeta, etc. to any number of arguments
- Accuracy of `digamma` function can be improved HOT 1
- What about `gamma_inc_inv`'s derivative ? HOT 2
- `expint(nu,z)` has bad precision for `nu != 1`
- Failing to build on Apple M1 Sonoma HOT 1
- Registering Functions with `Symbolics.jl` HOT 1
- Large negative real number arguments return NaN for expintx HOT 1
- Rewrite all AMOS code in julia HOT 5
- Internals of the incomplete gamma function does not work with `ForwardDiff.jl`'s `Dual()` type due to enforced floating point precision HOT 4
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from specialfunctions.jl.