Julia¶

Julia reads and writes the same files with the same calls, in Julia's spelling: functions in the Gamut module taking the study first, declarations as Dicts or named tuples, do blocks that close the file, and positions counted from 1. The package needs the core library (installing). An example written in Julia has a Julia tab, quoted from a program in examples/julia; the examples say which are. Run one from a working folder with julia --project=<checkout>/julia <checkout>/examples/julia/run_table.jl.

The same calls¶

Python	Julia
`with gamut.create(path, writer=w, ...) as study:`	`Gamut.create(path; writer=w, ...) do study`
`study.add_run(values, fields=...)`	`Gamut.add_run!(study, values; fields=...)`
`study.get_field("p", rows=r, nodes=n)`	`Gamut.get_field(study, "p"; rows=r, nodes=n)`
`study.select("mach > 0.7")`	`Gamut.select(study, "mach > 0.7")`, positions from 1
`study.outside("alpha", rows=r)`	`Gamut.outside(study, "alpha"; rows=r)`
`study.row_count`, `study.type`	`study.row_count`, `study.type`
`study.related("cl")`	`Gamut.related(study, "cl")`
`study.model_numbers("fit")`	`Gamut.model_numbers(study, "fit")`
`gamut.append`, `extract`, `write_copy`, `validate`	`Gamut.append!`, `extract`, `write_copy`, `validate`

Conventions¶

Declarations are Dicts whose values are named tuples, (units="degree", role="condition"), or a units string alone for a result. select returns positions counted from 1, and every read takes them as rows; arrays come back with their axes in logical order, cp[run, node]. Cells are a tuple (types, offsets, connectivity) in VTK's convention, as in every language, so the node numbers inside connectivity count from 0, and the stored codes of a label or mesh label count from 0 too: a zone's code is its place in categories minus one. A do block finishes the file when it ends, and removes it if the block throws and the file cannot be finished. A refusal throws a GamutError that names the rule.

Inputs outside a model's range¶

Gamut.evaluate evaluates a model file and returns its answer, a study open to read. An input outside the range the model was fitted on is evaluated with a warning (W4), and the answer keeps the model's bounds, so it can say which rows lie outside and how far. nearest.jl asks the models page's nearest-run model about 5 and 12 degrees:

answer = Gamut.evaluate("lift_nearest.gamut", Dict("alpha" => [5.0, 12.0]))
outside = Gamut.outside(answer, "alpha")
close(answer)

degrees outside the fitted range: 0 2

outside gives a number per row in the parameter's units: 0 inside the bounds or on one, negative below them, positive above.

Notes¶

Values arrive in the units they were stored in, which Gamut.describe(study, name)["units"] names. Converting them is left to the caller; only Python's reads convert, through pint.
Julia evaluates model files whose models are the built-in nearest neighbour; models of other types are registered in Python or MATLAB.
Gamut.library_path() names the core library in use, set by GAMUT_LIBRARY.

Next: C++ and C.