From 19eb1681de378267cd09bd398f82c677d6e163a6 Mon Sep 17 00:00:00 2001 From: Brian Groenke Date: Thu, 29 Feb 2024 15:57:44 +0100 Subject: [PATCH] Update example doc header --- examples/cglite_parameter_ensembles.jl | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/examples/cglite_parameter_ensembles.jl b/examples/cglite_parameter_ensembles.jl index acd95127..4effb83b 100644 --- a/examples/cglite_parameter_ensembles.jl +++ b/examples/cglite_parameter_ensembles.jl @@ -1,6 +1,5 @@ -# # [Fast heat conduction with CryoGridLite](@id example7) -# This example is very similar to [Example 1](@ref) but uses the -# fast implicit CryoGridLite solver of Langer et al. 2023. +# # [Parameter ensembles](@id example12) +# This example is very similar to [Example 7](@ref) but shows how to create and run parallelized parameter ensembels. # Make sure to explicitly import the `LiteImplicit` submodule which has # the relevant solver types. @@ -52,6 +51,8 @@ tspan = (DateTime(2000,1,1), DateTime(2010,12,31)) u0, du0 = initialcondition!(tile, tspan); prob = CryoGridProblem(tile, u0, tspan, saveat=24*3600.0, savevars=(:T,)) +# The CryoGridParams type behaves like a table and can be easily converted +# to a DataFrame with DataFrame(params) when DataFrames.jl is loaded. params = CryoGrid.parameters(tile) # you can use Julia's `vec` method to convert `CryoGridParams` into a `ComponentVector`