setup Manifest for julia 1.11

.github/workflows/CI.yml

@@ -19,7 +19,6 @@ jobs:
         version:
           - '1'
           - '1.6' 
-          #- '^1.7.0-0' 
         os:
           - ubuntu-latest
         arch:

.gitignore

@@ -1,8 +1,8 @@
 *.jl.*.cov
 *.jl.cov
 *.jl.mem
-/Manifest.toml
+/Manifest*.toml
 /docs/build/
 /tmp
-docs/Manifest.toml
-test/Manifest.toml
+docs/Manifest*.toml
+test/Manifest*.toml

.typos.toml

@@ -4,4 +4,6 @@ SOM = "SOM"
 som = "som"
 tha = "tha"
 Tha = "Tha"
+Gam = "Gam"
+
 

Project.toml

@@ -41,8 +41,5 @@ Tables = "1.6.0"
 TimeZones = "1.6.1"
 julia = "1.6"
 
-[extras]
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-
-[targets]
-test = ["Test"]
+[workspace]
+projects = ["test", "docs"]

inst/walkthrough_todo.jmd

@@ -270,7 +270,7 @@ g1_USO = stomatal_slope(thaf,model="USO",g0=0,robust_nls=true)
 g1_USO
 ```
 
-In this case, we have estimated $g_1$ from the USO (optimal stomatal optimization) model as described in Medlyn et al. 2011. The output is a model object that prints the model formulat that is used to estimate $g_1$, the estimated parameter value(s), as well as the weighted residual sum-of-squares. Further information on this model object can be obtained using the `summary` function.
+In this case, we have estimated $g_1$ from the USO (optimal stomatal optimization) model as described in Medlyn et al. 2011. The output is a model object that prints the model formula that is used to estimate $g_1$, the estimated parameter value(s), as well as the weighted residual sum-of-squares. Further information on this model object can be obtained using the `summary` function.
 In this case we have fixed the model intercept $g_0$ to 0 (this could also be any other value). We can also try to estimate $g_1$ and $g_0$ simultaneously (if we add `fitg0=true` to the function call above), but note that the two parameters are usually correlated, and that the values of $g_0$ are not straightforward to interpret (especially at ecosystem level). The option `robust_nls=true` specifies that $g_1$ is determined by a robust non-linear regression routine (from the `robustbase` package). We recommend to use this option since otherwise the parameter estimates are sensitive to outliers in $G_s$, which often occur even in filtered EC datasets.
 By default, the model takes VPD and atmospheric CO$_2$ concentration as measured at the tower as input. We can also calculate $g_1$ by taking the surface conditions, which are probably more relevant for plant physiological processes than those measured a certain distance above the canopy:
 

test/runtests.jl

@@ -29,6 +29,8 @@ const GROUP = get(ENV, "GROUP", "All") # defined in in CI.yml
         @time @safetestset "surface_conductance Tests" include("surface_conductance_test.jl")
         #@safetestset "Tests" include("test/evapotranspiration_test.jl")
         @time @safetestset "evapotranspiration Tests" include("evapotranspiration_test.jl")
+        #@safetestset "Tests" include("test/undoc_test.jl")
+        @time @safetestset "undocumented objects" include("undoc_test.jl")
     end
 end
 

test/undoc_test.jl

@@ -0,0 +1,12 @@
+using Bigleaf
+using Test
+
+@testset "Docstrings" begin
+    if VERSION >= v"1.11"
+        @test_broken isempty(Docs.undocumented_names(Bigleaf))
+    end
+    #undoc = Docs.undocumented_names(Bigleaf)
+    #@test_broken undoc == []
+    #@test undoc = [:bar, :baz]
+    #@test undoc = []
+end