nixpkgs/pkgs/development/octave-modules/stk/default.nix
Silvan Mosberger 4f0dadbf38 treewide: format all inactive Nix files
After final improvements to the official formatter implementation,
this commit now performs the first treewide reformat of Nix files using it.
This is part of the implementation of RFC 166.

Only "inactive" files are reformatted, meaning only files that
aren't being touched by any PR with activity in the past 2 months.
This is to avoid conflicts for PRs that might soon be merged.
Later we can do a full treewide reformat to get the rest,
which should not cause as many conflicts.

A CI check has already been running for some time to ensure that new and
already-formatted files are formatted, so the files being reformatted here
should also stay formatted.

This commit was automatically created and can be verified using

    nix-build a08b3a4d19.tar.gz \
      --argstr baseRev b32a094368
    result/bin/apply-formatting $NIXPKGS_PATH
2024-12-10 20:26:33 +01:00

34 lines
1.2 KiB
Nix

{
buildOctavePackage,
lib,
fetchurl,
}:
buildOctavePackage rec {
pname = "stk";
version = "2.8.1";
src = fetchurl {
url = "https://github.com/stk-kriging/stk/releases/download/${version}/${pname}-${version}-octpkg.tar.gz";
sha256 = "sha256-wTjM9LUcC8BEj3TNxAz877LqJvuoxWUse9PIZoWGnIU=";
};
meta = with lib; {
homepage = "https://octave.sourceforge.io/stk/index.html";
license = licenses.gpl3Plus;
maintainers = with maintainers; [ KarlJoad ];
description = "STK is a (not so) Small Toolbox for Kriging";
longDescription = ''
The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on
the interpolation/regression technique known as kriging, which is very
closely related to Splines and Radial Basis Functions, and can be
interpreted as a non-parametric Bayesian method using a Gaussian Process
(GP) prior. The STK also provides tools for the sequential and non-sequential
design of experiments. Even though it is, currently, mostly geared towards
the Design and Analysis of Computer Experiments (DACE), the STK can be
useful for other applications areas (such as Geostatistics, Machine
Learning, Non-parametric Regression, etc.).
'';
};
}