Upgrade to MultiScaleTreeGraph v0.15.0

.github/workflows/CI.yml

@@ -23,7 +23,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - "1.9"
+          - "1.10"
           - "1"
         os:
           - ubuntu-latest

Project.toml

@@ -23,17 +23,17 @@ AbstractTrees = "0.4"
 CSV = "0.10"
 DataAPI = "1.15"
 DataFrames = "1"
-Dates = "1.9"
+Dates = "1.10"
 FLoops = "0.2"
-Markdown = "1.9"
-MultiScaleTreeGraph = "0.13, 0.14"
-PlantMeteo = "0.7"
+Markdown = "1.10"
+MultiScaleTreeGraph = "0.15"
+PlantMeteo = "0.8"
 SHA = "0.7.0"
-Statistics = "1.9"
+Statistics = "1.10"
 Tables = "1"
-Term = "1, 2"
-Test = "1.9"
-julia = "1.9"
+Term = "2"
+Test = "1.10"
+julia = "1.10"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

README.md

@@ -139,14 +139,14 @@ lines(model[:TT_cu], model[:LAI], color=:green, axis=(ylabel="LAI (m² m⁻²)",
 Model coupling is done automatically by the package, and is based on the dependency graph between the models. To couple models, we just have to add them to the `ModelMapping`. For example, let's couple the `ToyLAIModel` with a model for light interception based on Beer's law:
 
 ```julia
-# ] add PlantSimEngine, DataFrames, CSV
-using PlantSimEngine, PlantMeteo, DataFrames, CSV
+# ] add PlantSimEngine, PlantMeteo, Dates
+using PlantSimEngine, PlantMeteo, Dates
 
 # Include the model definition from the examples folder:
 using PlantSimEngine.Examples
 
 # Import the example meteorological data:
-meteo_day = CSV.read(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), DataFrame, header=18)
+meteo_day = read_weather(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), duration=Dates.Day)
 
 # Define the list of models for coupling:
 model = ModelMapping(

docs/make.jl

@@ -18,7 +18,7 @@ makedocs(;
         canonical="https://VirtualPlantLab.github.io/PlantSimEngine.jl",
         edit_link="main",
         assets=String[],
-        size_threshold=500000
+        size_threshold=700000
     ), pages=[
         "Home" => "index.md",
         "Introduction" => [

docs/src/FAQ/translate_a_model.md

@@ -3,9 +3,9 @@
 ```@setup mymodel
 using PlantSimEngine
 using CairoMakie
-using CSV, DataFrames
 # Import the example models defined in the `Examples` sub-module:
 using PlantSimEngine.Examples
+using PlantMeteo, Dates
 
 function lai_toymodel(TT_cu; max_lai=8.0, dd_incslope=500, inc_slope=70, dd_decslope=1000, dec_slope=20)
     LAI = max_lai * (1 / (1 + exp((dd_incslope - TT_cu) / inc_slope)) - 1 / (1 + exp((dd_decslope - TT_cu) / dec_slope)))
@@ -15,12 +15,12 @@ function lai_toymodel(TT_cu; max_lai=8.0, dd_incslope=500, inc_slope=70, dd_decs
     return LAI
 end
 
-meteo_day = CSV.read(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), DataFrame, header=18)
+meteo_day = read_weather(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), duration=Dates.Day)
 ```
 
 If you already have a model, you can easily use `PlantSimEngine` to couple it with other models with minor adjustments.
 
-## Toy LAI Model 
+## Toy LAI Model
 
 ### Model description
 
@@ -128,7 +128,7 @@ Now that we have everything set up, we can run a simulation. The first step here
 
 ```julia
 # Import the packages we need:
-using PlantMeteo, Dates, DataFrames
+using PlantMeteo, Dates
 
 # Define the period of the simulation:
 period = [Dates.Date("2021-01-01"), Dates.Date("2021-12-31")]

docs/src/index.md

@@ -3,13 +3,13 @@ CurrentModule = PlantSimEngine
 ```
 
 ```@setup readme
-using PlantSimEngine, PlantMeteo, DataFrames, CSV
+using PlantSimEngine, PlantMeteo, Dates
 
 # Import the examples defined in the `Examples` sub-module:
 using PlantSimEngine.Examples
 
 # Import the example meteorological data:
-meteo_day = CSV.read(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), DataFrame, header=18)
+meteo_day = read_weather(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), duration=Dates.Day)
 
 # Define the model mapping:
 model = ModelMapping(
@@ -125,6 +125,8 @@ model = ModelMapping(
 )
 
 out = run!(model) # run the model and extract its outputs
+
+out[1:3,:]
 ```
 
 > **Note**  
@@ -144,14 +146,14 @@ lines(out[:TT_cu], out[:LAI], color=:green, axis=(ylabel="LAI (m² m⁻²)", xla
 Model coupling is done automatically by the package, and is based on the dependency graph between the models. To couple models, we just have to add them to the `ModelMapping`. For example, let's couple the `ToyLAIModel` with a model for light interception based on Beer's law:
 
 ```@example readme
-# ] add PlantSimEngine, DataFrames, CSV
-using PlantSimEngine, PlantMeteo, DataFrames, CSV
+# ] add PlantSimEngine, PlantMeteo
+using PlantSimEngine, PlantMeteo, Dates
 
 # Import the examples defined in the `Examples` sub-module
 using PlantSimEngine.Examples
 
 # Import the example meteorological data:
-meteo_day = CSV.read(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), DataFrame, header=18)
+meteo_day = read_weather(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), duration=Dates.Day)
 
 # Define the mapping for coupled models:
 model2 = ModelMapping(
@@ -162,6 +164,7 @@ model2 = ModelMapping(
 
 # Run the simulation:
 out2 = run!(model2, meteo_day)
+out2[1:3,:]
 ```
 
 The `ModelMapping` couples the models by automatically computing the dependency graph of the models. The resulting dependency graph is:

docs/src/model_coupling/model_coupling_user.md

@@ -1,7 +1,7 @@
 # Model coupling for users
 
 ```@setup usepkg
-using PlantSimEngine, PlantMeteo
+using PlantSimEngine, PlantMeteo, Dates
 # Import the example models defined in the `Examples` sub-module:
 using PlantSimEngine.Examples
 

docs/src/multirate/multirate_tutorial.md

@@ -163,6 +163,7 @@ for row in eachrow(week_df)
 end
 
 meteo_hourly = Weather(hourly_rows)
+meteo_hourly[1:3] # show the first 3 rows of the hourly weather table
 ```
 
 ## 2. Define simple tutorial models
@@ -300,7 +301,7 @@ Of course the outputs of the models are still available in the `status_outputs`
 
 ```@example multirate_tutorial
 outs = convert_outputs(out_status, DataFrame)
-outs["Plant"]
+outs["Plant"][1:3,:]
 ```
 
 ## 5. Deeper notes

docs/src/multiscale/multiscale_considerations.md

@@ -78,19 +78,17 @@ The [`run!`](@ref) function differs slightly from its single-scale version. The
 run!(mtg, mapping::ModelMapping, meteo, constants, extra; nsteps, tracked_outputs)
 ```
 
-Instead of a just the [`ModelMapping`](@ref), it also takes an MTG as the first argument. The optional `meteo` and `constants` argument are identical to the single-scale version. The `extra` argument is now reserved and should not be used. A new `nsteps` keyword argument is available to restrict the simulation to a specified number of steps. 
+Instead of a just the [`ModelMapping`](@ref), it also takes an MTG as the first argument. The optional `meteo` and `constants` argument are identical to the single-scale version. The `extra` argument is now reserved and should not be used. A new `nsteps` keyword argument is available to restrict the simulation to a specified number of steps.
 
 ## Multi-scale output data structure
 
+The output structure, like the mapping, is a Julia `Dict` structure indexed by the scale name. Values are a per-scale `Vector{NamedTuple}` which lists the requested variables for every node at that scale, for every timestep in the simulation. Timestep and Multiscale Tree Graph nodes are also added to the output data, as a `:timestep`and a `:node` entry.
 
-The output structure, like the mapping, is a Julia `Dict` structure indexed by the scale name. Values are a per-scale `Vector{NamedTuple}` which lists the requested variables for every node at that scale, for every timestep in the simulation. Timestep and Multiscale Tree Graph nodes are also added to the output data, as a `:timestep`and a `:node` entry. 
-
-This dictionary structure makes the outputs as-is a little more verbose to inspect than in single-scale, but the general usage is similar, and it is both compact, and fast to convert to a `Dict{String, DataFrame}` which can make queries easier. 
+This dictionary structure makes the outputs as-is a little more verbose to inspect than in single-scale, but the general usage is similar, and it is both compact, and fast to convert to a `Dict{String, DataFrame}` which can make queries easier.
 
 !!! note
   Some of the mapped variables -those that map from scalar to vector- will not be added to the outputs to save some memory and space since they are redundant.
 
-
 To illustrate, here's an example output from part 3 of the Toy plant tutorial, zeroing in on a variable at the "Root" scale: [Fixing bugs in the plant simulation](@ref):
 
 ```julia

docs/src/multiscale/multiscale_coupling.md

@@ -111,7 +111,7 @@ mapping = ModelMapping(
 The model's constructor provides convenient default names for the scale corresponding to the reproductive organs. A user may override that if their naming schemes or MTG attributes differ.
 
 ```julia
-function ReproductiveOrganEmission(mtg::MultiScaleTreeGraph.Node; phytomer_symbol="Phytomer", male_symbol="Male", female_symbol="Female")
+function ReproductiveOrganEmission(mtg::MultiScaleTreeGraph.Node; phytomer_symbol=:Phytomer, male_symbol=:Male, female_symbol=:Female)
     ...
 end
 ```
@@ -168,4 +168,4 @@ function PlantSimEngine.run!(m::ReproductiveOrganEmission, models, status, meteo
         ...
     end
 end
-```
+```

docs/src/multiscale/multiscale_example_1.md

@@ -22,9 +22,8 @@ We'll need to make use of a few packages, as usual, after adding them to our Jul
 ```@example usepkg
 using PlantSimEngine
 using PlantSimEngine.Examples # to import the ToyDegreeDaysCumulModel model
-using PlantMeteo
+using PlantMeteo, Dates
 using MultiScaleTreeGraph # multi-scale
-using CSV, DataFrames # used to import the example weather data
 ```
 
 ## A basic growing plant
@@ -119,7 +118,7 @@ Let's first define a helper function that iterates across a Multiscale Tree Grap
 ```@example usepkg
 function get_n_leaves(node::MultiScaleTreeGraph.Node)
     root = MultiScaleTreeGraph.get_root(node)
-    nleaves = length(MultiScaleTreeGraph.traverse(root, x->1, symbol="Leaf"))
+    nleaves = length(MultiScaleTreeGraph.traverse(root, x->1, symbol=:Leaf))
     return nleaves
 end
 ```
@@ -246,27 +245,27 @@ mapping = ModelMapping(
 !!! note
     This excerpt (and the complete script file) showcase the final properly initialized mapping, but when developing, you are encouraged to make liberal use of the helper function [`to_initialize`](@ref) and check the PlantSimEngine user errors.
 
-### Running a simulation 
+### Running a simulation
 
 We only need an MTG, and some weather data, and then we'll be set. Let's create a simple MTG : 
 
 ```@example usepkg
- mtg = MultiScaleTreeGraph.Node(MultiScaleTreeGraph.NodeMTG("/", "Scene", 1, 0))   
-    plant = MultiScaleTreeGraph.Node(mtg, MultiScaleTreeGraph.NodeMTG("+", "Plant", 1, 1))
-    
-    internode1 = MultiScaleTreeGraph.Node(plant, MultiScaleTreeGraph.NodeMTG("/", "Internode", 1, 2))
-    MultiScaleTreeGraph.Node(internode1, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
-    MultiScaleTreeGraph.Node(internode1, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
+mtg = MultiScaleTreeGraph.Node(MultiScaleTreeGraph.NodeMTG("/", "Scene", 1, 0))   
+plant = MultiScaleTreeGraph.Node(mtg, MultiScaleTreeGraph.NodeMTG("+", "Plant", 1, 1))
+
+internode1 = MultiScaleTreeGraph.Node(plant, MultiScaleTreeGraph.NodeMTG("/", "Internode", 1, 2))
+MultiScaleTreeGraph.Node(internode1, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
+MultiScaleTreeGraph.Node(internode1, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
 
-    internode2 = MultiScaleTreeGraph.Node(internode1, MultiScaleTreeGraph.NodeMTG("<", "Internode", 1, 2))
-    MultiScaleTreeGraph.Node(internode2, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
-    MultiScaleTreeGraph.Node(internode2, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
+internode2 = MultiScaleTreeGraph.Node(internode1, MultiScaleTreeGraph.NodeMTG("<", "Internode", 1, 2))
+MultiScaleTreeGraph.Node(internode2, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
+MultiScaleTreeGraph.Node(internode2, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
 ```
 
-Import some weather data : 
+Import some weather data:
 
 ```@example usepkg
-meteo_day = CSV.read(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), DataFrame, header=18)
+meteo_day = read_weather(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), duration=Dates.Day)
 nothing # hide
 ```
 

docs/src/multiscale/multiscale_example_2.md

@@ -16,9 +16,8 @@ Once again, with a properly set-up Julia environment:
 ```@example usepkg
 using PlantSimEngine
 using PlantSimEngine.Examples
-using PlantMeteo
+using PlantMeteo, Dates, Dates
 using MultiScaleTreeGraph
-using CSV, DataFrames
 
 PlantSimEngine.@process "leaf_carbon_capture" verbose = false
 
@@ -38,7 +37,7 @@ end
 
 function get_n_leaves(node::MultiScaleTreeGraph.Node)
     root = MultiScaleTreeGraph.get_root(node)
-    nleaves = length(MultiScaleTreeGraph.traverse(root, x->1, symbol="Leaf"))
+    nleaves = length(MultiScaleTreeGraph.traverse(root, x->1, symbol=:Leaf))
     return nleaves
 end
 ```
@@ -78,12 +77,12 @@ It also makes use of a couple of helper functions to find the end root and compu
 ```@example usepkg
 function get_root_end_node(node::MultiScaleTreeGraph.Node)
     root = MultiScaleTreeGraph.get_root(node)
-    return MultiScaleTreeGraph.traverse(root, x->x, symbol="Root", filter_fun = MultiScaleTreeGraph.isleaf)
+    return MultiScaleTreeGraph.traverse(root, x->x, symbol=:Root, filter_fun = MultiScaleTreeGraph.isleaf)
 end
 
 function get_roots_count(node::MultiScaleTreeGraph.Node)
     root = MultiScaleTreeGraph.get_root(node)
-    return length(MultiScaleTreeGraph.traverse(root, x->x, symbol="Root"))
+    return length(MultiScaleTreeGraph.traverse(root, x->x, symbol=:Root))
 end
 
 PlantSimEngine.@process "root_growth" verbose = false
@@ -254,7 +253,7 @@ mtg = MultiScaleTreeGraph.Node(MultiScaleTreeGraph.NodeMTG("/", "Scene", 1, 0))
         MultiScaleTreeGraph.NodeMTG("+", "Root", 1, 3), 
     )
 
-meteo_day = CSV.read(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), DataFrame, header=18)
+meteo_day = read_weather(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), duration=Dates.Day)
 
 outs = run!(mtg, mapping, meteo_day)
 mtg

docs/src/multiscale/multiscale_example_3.md

@@ -3,21 +3,21 @@
 ```@setup usepkg
 using PlantSimEngine
 using PlantSimEngine.Examples
-using PlantMeteo, CSV, DataFrames
+using PlantMeteo, Dates
 using MultiScaleTreeGraph
 function get_root_end_node(node::MultiScaleTreeGraph.Node)
     root = MultiScaleTreeGraph.get_root(node)
-    return MultiScaleTreeGraph.traverse(root, x->x, symbol="Root", filter_fun = MultiScaleTreeGraph.isleaf)
+    return MultiScaleTreeGraph.traverse(root, x->x, symbol=:Root, filter_fun = MultiScaleTreeGraph.isleaf)
 end
 
 function get_roots_count(node::MultiScaleTreeGraph.Node)
     root = MultiScaleTreeGraph.get_root(node)
-    return length(MultiScaleTreeGraph.traverse(root, x->x, symbol="Root"))
+    return length(MultiScaleTreeGraph.traverse(root, x->x, symbol=:Root))
 end
 
 function get_n_leaves(node::MultiScaleTreeGraph.Node)
     root = MultiScaleTreeGraph.get_root(node)
-    nleaves = length(MultiScaleTreeGraph.traverse(root, x->1, symbol="Leaf"))
+    nleaves = length(MultiScaleTreeGraph.traverse(root, x->1, symbol=:Leaf))
     return nleaves
 end
 
@@ -210,24 +210,24 @@ mapping = ModelMapping(
 "Leaf" => ( ToyLeafCarbonCaptureModel(),),
 )
 
-    mtg = MultiScaleTreeGraph.Node(MultiScaleTreeGraph.NodeMTG("/", "Scene", 1, 0))   
+mtg = MultiScaleTreeGraph.Node(MultiScaleTreeGraph.NodeMTG("/", "Scene", 1, 0))   
 
-    plant = MultiScaleTreeGraph.Node(mtg, MultiScaleTreeGraph.NodeMTG("+", "Plant", 1, 1))
-    
-    internode1 = MultiScaleTreeGraph.Node(plant, MultiScaleTreeGraph.NodeMTG("/", "Internode", 1, 2))
-    MultiScaleTreeGraph.Node(internode1, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
-    MultiScaleTreeGraph.Node(internode1, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
+plant = MultiScaleTreeGraph.Node(mtg, MultiScaleTreeGraph.NodeMTG("+", "Plant", 1, 1))
+
+internode1 = MultiScaleTreeGraph.Node(plant, MultiScaleTreeGraph.NodeMTG("/", "Internode", 1, 2))
+MultiScaleTreeGraph.Node(internode1, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
+MultiScaleTreeGraph.Node(internode1, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
 
-    internode2 = MultiScaleTreeGraph.Node(internode1, MultiScaleTreeGraph.NodeMTG("<", "Internode", 1, 2))
-    MultiScaleTreeGraph.Node(internode2, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
-    MultiScaleTreeGraph.Node(internode2, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
+internode2 = MultiScaleTreeGraph.Node(internode1, MultiScaleTreeGraph.NodeMTG("<", "Internode", 1, 2))
+MultiScaleTreeGraph.Node(internode2, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
+MultiScaleTreeGraph.Node(internode2, MultiScaleTreeGraph.NodeMTG("+", "Leaf", 1, 2))
 
-    plant_root_start = MultiScaleTreeGraph.Node(
-        plant, 
-        MultiScaleTreeGraph.NodeMTG("+", "Root", 1, 3), 
-    )
+plant_root_start = MultiScaleTreeGraph.Node(
+    plant, 
+    MultiScaleTreeGraph.NodeMTG("+", "Root", 1, 3), 
+)
 
-    meteo_day = CSV.read(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), DataFrame, header=18)
+meteo_day = read_weather(joinpath(pkgdir(PlantSimEngine), "examples/meteo_day.csv"), duration=Dates.Day)
 
 ```