brownian_parameters
Stable function · Variable classification
Tier: stable · Kind: function · Topic: Variable classification
| Julia | TypeScript | Python | Rust | Go |
|---|---|---|---|---|
brownian_parameters | brownianParameters | brownian_parameters | brownian_parameters | BrownianParameters |
Julia
brownian_parameters(model::Model)
brownian_parameters(model::Model) -> Vector{String}
Parameters whose update.kind is "wiener" — the SDE noise sources
(esm-spec §6.3.1). Sorted lexicographically. Their presence is what makes
system_kind
"sde".
TypeScript
function brownianParameters(model: Model): string[]
Parameters whose update.kind is wiener — the SDE noise sources.
Python
brownian_parameters(model: Any) -> list[str]
Parameters whose update.kind is "wiener" — the SDE noise sources
(esm-spec §6.3.1), sorted.
With an update ARRAY a parameter is Brownian iff ANY rule is wiener. The
schema forbids wiener inside an array (a driving noise process is the
parameter’s whole value), so in practice an array means discrete; the test is
written over all the rules anyway rather than assuming the schema held.
Rust
pub fn brownian_parameters(model: &Model) -> Vec<String>
Parameters whose update.kind is wiener — the SDE noise sources.
Names are sorted lexicographically, not in declaration order (API_SPEC.md §5.3).
Go
func BrownianParameters(model *Model) []string
BrownianParameters returns the parameters whose update is wiener — the SDE noise sources, resampled every step with √dt increment scaling. Sorted lexicographically.
With an update ARRAY a parameter is Brownian iff ANY rule is wiener; the schema forbids wiener inside an array, so in practice an array always means discrete. Checking every rule rather than the first keeps the classification correct on a document that slipped past a lenient schema check.