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Gamut

Gamut: an open file format for parametric simulation studies

By Bijective Systems, with libraries for Python, MATLAB, Julia, C++, C and Rust.

Documentation · Installing · Examples · Specification · PyPI: bijective-gamut · Releases

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Gamut is a file format for parametric simulation studies: the hundreds or thousands of CFD or FEA runs behind a design sweep, an aerodynamic database or a surrogate model. One file holds the run table, each run's inputs, results and status, and, when the runs have a mesh, the mesh and every field on it, all with units. A model fitted to the runs is saved the same way, as plain numbers with no code. Every language reads and writes the file through one core, so each reads what another wrote, value for value.

Python (with the command line), MATLAB, Julia, C++, C and Rust each read and write a .gamut file through the C interface of the one C++ core; Fortran is planned.

Example

A sweep of a wing's angle of attack: six runs written at once, a seventh added later, and the lift of the runs that converged read back. cl stands for your solver's results.

alpha = np.arange(0.0, 11.0, 2.0)  # six angles of attack, in degrees
cl = 0.11 * alpha                  # stands for the solver's lift coefficients

with gamut.create("sweep.gamut", writer="example sweep",
                   parameters={"alpha": {"units": "degree", "role": "condition"}},
                   labels={"status": {"role": "status",
                                      "categories": ["converged", "failed"]}},
                   scalars={"cl": "1"}) as study:
    study.add_runs({"alpha": alpha, "cl": cl,
                    "status": ["converged"] * len(alpha)})

with gamut.open("sweep.gamut", mode="a") as study:
    study.add_run({"alpha": 12, "status": "failed"})

with gamut.open("sweep.gamut") as study:
    lift = study.get_scalar("cl", rows=study.select("status == converged"))
MATLAB
alpha = (0:2:10)';   % six angles of attack, in degrees
cl = 0.11 * alpha;   % stands for the solver's lift coefficients

study = gamut.create('sweep.gamut', 'writer', 'example sweep', ...
    'parameters', struct('alpha', struct('units', 'degree', ...
                                         'role', 'condition')), ...
    'labels', struct('status', struct('role', 'status', ...
                                      'categories', {{'converged', 'failed'}})), ...
    'scalars', struct('cl', '1'));
status = repmat({'converged'}, numel(alpha), 1);
study.addRuns(struct('alpha', alpha, 'cl', cl, 'status', {status}));
study.close();

study = gamut.open('sweep.gamut', 'mode', 'a');
study.addRun(struct('alpha', 12, 'status', 'failed'));
study.close();

study = gamut.open('sweep.gamut');
lift = study.getScalar('cl', 'rows', study.select('status == converged'));
study.close();
Julia
alpha = collect(0.0:2.0:10.0)  # six angles of attack, in degrees
cl = 0.11 .* alpha             # stands for the solver's lift coefficients

Gamut.create("sweep.gamut"; writer="example sweep",
              parameters=Dict("alpha" => (units="degree", role="condition")),
              labels=Dict("status" => (role="status",
                                       categories=["converged", "failed"])),
              scalars=Dict("cl" => "1")) do study
    status = fill("converged", length(alpha))
    Gamut.add_runs!(study, Dict("alpha" => alpha, "cl" => cl, "status" => status))
end

Gamut.open("sweep.gamut"; mode="a") do study
    Gamut.add_run!(study, Dict("alpha" => 12.0, "status" => "failed"))
end

lift = Gamut.open("sweep.gamut") do study
    Gamut.get_scalar(study, "cl"; rows=Gamut.select(study, "status == converged"))
end
C++
const std::vector<double> alpha = {0, 2, 4, 6, 8, 10};  // angles of attack, degrees
std::vector<double> cl;  // stands for the solver's lift coefficients
for (const double a : alpha) cl.push_back(0.11 * a);

auto study = gamut::create("sweep.gamut", "example sweep");
study.declare_parameter("alpha", "condition", "degree");
study.declare_label("status", "status", {"converged", "failed"});
study.declare_scalar("cl", "1");
const std::vector<std::string> status(alpha.size(), "converged");
study.add_runs({{"alpha", alpha}, {"cl", cl}, {"status", status}});
study.close();

study = gamut::open("sweep.gamut", "a");
study.add_run({{"alpha", 12.0}, {"status", "failed"}});
study.close();

study = gamut::open("sweep.gamut");
const std::vector<double> lift =
    study.get_scalar("cl", study.select("status == converged"));
Rust
let alpha = vec![0.0, 2.0, 4.0, 6.0, 8.0, 10.0];   // angles of attack, degrees
let cl: Vec<f64> = alpha.iter().map(|a| 0.11 * a).collect();   // stands for the solver's lift coefficients

let mut study = gamut::create("sweep.gamut", "example sweep", "", "")?;
study.declare_parameter("alpha", "condition", "degree")?;
study.declare_label("status", "status", &["converged", "failed"])?;
study.declare_scalar("cl", "1")?;
let status = vec!["converged"; alpha.len()];
study.add_runs(&[("alpha", alpha.into()), ("cl", cl.into()), ("status", status.into())], &[])?;
study.close()?;

let mut study = gamut::open_append("sweep.gamut")?;
study.add_run(&[("alpha", 12.0.into()), ("status", "failed".into())], &[])?;
study.close()?;

let study = gamut::open("sweep.gamut")?;
let lift = study.get_scalar("cl", study.select("status == converged")?)?;

All five print the same line:

lift of the runs that converged: 0.00 0.22 0.44 0.66 0.88 1.10

The examples go on from here, a step at a time and in each language: a mesh shared by every run, families of shapes, meshes that change, time series, uncertainty, and models fitted to the runs.

Performance

File size and read times of Exodus II, CGNS and PhysicsNeMo-Mesh relative to Gamut, for a triangulated capsule geometry family and an XFOIL airfoil sweep. Every format uses the same mesh within each dataset.

On the triangulated capsule family, Gamut and compressed Exodus occupy about the same space, and Gamut is smaller and reads one run faster than PhysicsNeMo-Mesh, which writes faster; on the smaller airfoil sweep, PhysicsNeMo-Mesh reads faster and Gamut is smaller. PhysicsNeMo-Mesh needs triangles, so every format here uses the capsule converted to triangles. The benchmarks give the full timings, settings and machine, the original quadrilateral results, and the workloads where another approach is faster.

Install

Language Install
Python 3.11 or later pip install bijective-gamut
MATLAB R2023b or later open Gamut.mltbx from the latest release
Julia 1.10 or later Pkg.add(url="https://github.com/bijective-systems-org/gamut", subdir="julia", rev="v2.1.0")
C++ and C CMake build and install, then find_package(gamut); include gamut/gamut.hpp (C++17) or gamut/gamut.h (C)
Rust 1.70 or later bijective-gamut = { git = "https://github.com/bijective-systems-org/gamut", tag = "v2.1.0" }, with GAMUT_LIB_DIR at a release's core

In Python, install bijective-gamut, then import gamut; the PyPI project named gamut is a different package. Installing has the details for each language, and llms.txt gives coding agents a plain-text overview.

License

Apache 2.0. Made by Bijective Systems. If you use Gamut in published work, please cite it (CITATION.cff).

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