Update tutorials to latest version

Author: AleMorales

.vscode/settings.json

@@ -0,0 +1 @@
+{}

Project.toml

@@ -10,6 +10,7 @@ PlantGraphs = "615ad455-9aac-4340-9247-71ef610f781a"
 PlantRayTracer = "78485975-e4aa-407d-b3bb-ded5a4265d05"
 PlantViz = "358bd95d-d12c-439f-94b7-04b17e500c7f"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
+SkyDomes = "1838625c-7cf7-40c6-898b-904883e4b556"
 
 [compat]
 PlantGeomPrimitives = "0.0.3"
@@ -18,4 +19,5 @@ PlantGraphs = "0.0.2"
 PlantRayTracer = " 0.0.6"
 PlantViz = "0.0.6"
 Reexport = "1.2.2"
-julia = "1.9"
+SkyDomes = "0.1.6"
+julia = "1.11"

nice_trees.png

@@ -52,7 +52,7 @@ function VirtualPlantLab.feed!(turtle::Turtle, i::TreeTypes.Internode, data)
     ## Rotate turtle around the head to implement elliptical phyllotaxis
     rh!(turtle, data.phyllotaxis)
     HollowCylinder!(turtle, length = i.length, height = i.length/15, width = i.length/15,
-                move = true, color = RGB(0.5,0.4,0.0))
+                move = true, colors = RGB(0.5,0.4,0.0))
     return nothing
 end
 
@@ -62,7 +62,7 @@ function VirtualPlantLab.feed!(turtle::Turtle, l::TreeTypes.Leaf, data)
     ra!(turtle, -data.leaf_angle)
     ## Generate the leaf
     Ellipse!(turtle, length = l.length, width = l.width, move = false,
-             color = RGB(0.2,0.6,0.2))
+             colors = RGB(0.2,0.6,0.2))
     ## Rotate turtle back to original direction
     ra!(turtle, data.leaf_angle)
     return nothing
@@ -233,7 +233,7 @@ newforest = deepcopy(forest)
 @threads for i in eachindex(forest)
     newforest[i] = simulate(forest[i], getInternode, 6)
 end
-render(Scene(newforest), parallel = true)
+render(Scene(newforest, parallel = true))
 #=
 
 An alternative way to perform the simulation is to have an outer loop for each timestep and an internal loop over the different trees. Although this approach is not required for this simple model, most FSP models will probably need such a scheme as growth of each individual plant will depend on competition for resources with neighbouring plants. In this case, this approach would look as follows:
@@ -245,7 +245,7 @@ for step in 1:15
         newforest[i] = simulate(newforest[i], getInternode, 1)
     end
 end
-render(Scene(newforest), parallel = true)
+render(Scene(newforest, parallel = true))
 #=
 
 # Customizing the scene
@@ -276,7 +276,7 @@ VirtualPlantLab.translate!(soil, Vec(0.0, 10.5, 0.0))
 We can now add the `soil` to the `scene` object with the `add!` function.
 
 =#
-VirtualPlantLab.add!(scene, mesh = soil, color = RGB(1,1,0))
+VirtualPlantLab.add!(scene, mesh = soil, colors = RGB(1,1,0))
 #=
 
 We can now render the scene that combines the random forest of binary trees and a yellow soil. Notice that
@@ -297,5 +297,5 @@ compute the resolution from a physical width and height in cm and a dpi (e.g., u
 
 =#
 res = calculate_resolution(width = 16.0, height = 16.0, dpi = 1_000)
-output = render(scene, axes = false, resolution = res)
+output = render(scene, axes = false, size = res)
 export_scene(scene = output, filename = "nice_trees.png")

test/forest.jl

@@ -1,10 +1,19 @@
 #=
+
 # Growth forest
 
-Alejandro Morales
+Alejandro Morales & Ana Ernst
 
 Centre for Crop Systems Analysis - Wageningen University
 
+> ## TL;DR
+> Now we want to implement a more extended functionality of our [Forest]()!
+> - Growth rules, based on information stored in organs (dimensions, carbon assimilation)
+> - Update dimensions in function of assimilation
+> - Compute sink strength
+> - Merge Scenes
+> - Generate forest on grid and retrieve canopy-level data (e.g., LAI)
+>
 
 In this example we extend the binary forest example to have more complex, time-
 dependent development and growth based on carbon allocation. For simplicity, the
@@ -24,6 +33,7 @@ import Random
 using FastGaussQuadrature
 using Distributions
 Random.seed!(123456789)
+import GLMakie
 #=
 
 ## Model definition
@@ -39,41 +49,41 @@ module. The differences with respect to the previous example are:
     - Bud break probability is a function of distance to apical meristem
 
 =#
-## Data types
+# Data types
 module TreeTypes
     using VirtualPlantLab
     using Distributions
-    ## Meristem
+    # Meristem
     Base.@kwdef mutable struct Meristem <: VirtualPlantLab.Node
-        age::Int64 = 0   ## Age of the meristem
+        age::Int64 = 0   # Age of the meristem
     end
-    ## Bud
+    # Bud
     struct Bud <: VirtualPlantLab.Node end
-    ## Node
+    # Node
     struct Node <: VirtualPlantLab.Node end
-    ## BudNode
+    # BudNode
     struct BudNode <: VirtualPlantLab.Node end
-    ## Internode (needs to be mutable to allow for changes over time)
+    # Internode (needs to be mutable to allow for changes over time)
     Base.@kwdef mutable struct Internode <: VirtualPlantLab.Node
         age::Int64 = 0         ## Age of the internode
         biomass::Float64 = 0.0 ## Initial biomass
         length::Float64 = 0.0  ## Internodes
         width::Float64  = 0.0  ## Internodes
         sink::Exponential{Float64} = Exponential(5)
     end
-    ## Leaf
+    # Leaf
     Base.@kwdef mutable struct Leaf <: VirtualPlantLab.Node
         age::Int64 = 0         ## Age of the leaf
         biomass::Float64 = 0.0 ## Initial biomass
         length::Float64 = 0.0  ## Leaves
         width::Float64 = 0.0   ## Leaves
         sink::Beta{Float64} = Beta(2,5)
     end
-    ## Graph-level variables -> mutable because we need to modify them during growth
+    # Graph-level variables -> mutable because we need to modify them during growth
     Base.@kwdef mutable struct treeparams
-        ## Variables
+        # Variables
         biomass::Float64 = 2e-3 ## Current total biomass (g)
-        ## Parameters
+        # Parameters
         RGR::Float64 = 1.0   ## Relative growth rate (1/d)
         IB0::Float64 = 1e-3  ## Initial biomass of an internode (g)
         SIW::Float64 = 0.1e6 ## Specific internode weight (g/m3)
@@ -97,32 +107,32 @@ import .TreeTypes
 
 The methods for creating the geometry and color of the tree are the same as in
 the previous example.
+Create geometry + color for the internodes
 
 =#
-## Create geometry + color for the internodes
 function VirtualPlantLab.feed!(turtle::Turtle, i::TreeTypes.Internode, vars)
-    ## Rotate turtle around the head to implement elliptical phyllotaxis
+    # Rotate turtle around the head to implement elliptical phyllotaxis
     rh!(turtle, vars.phyllotaxis)
     HollowCylinder!(turtle, length = i.length, height = i.width, width = i.width,
-                move = true, color = RGB(0.5,0.4,0.0))
+                move = true, colors = RGB(0.5,0.4,0.0))
     return nothing
 end
 
-## Create geometry + color for the leaves
+# Create geometry + color for the leaves
 function VirtualPlantLab.feed!(turtle::Turtle, l::TreeTypes.Leaf, vars)
-    ## Rotate turtle around the arm for insertion angle
+    # Rotate turtle around the arm for insertion angle
     ra!(turtle, -vars.leaf_angle)
-    ## Generate the leaf
+    # Generate the leaf
     Ellipse!(turtle, length = l.length, width = l.width, move = false,
-             color = RGB(0.2,0.6,0.2))
-    ## Rotate turtle back to original direction
+             colors = RGB(0.2,0.6,0.2))
+    # Rotate turtle back to original direction
     ra!(turtle, vars.leaf_angle)
     return nothing
 end
 
-## Insertion angle for the bud nodes
+# Insertion angle for the bud nodes
 function VirtualPlantLab.feed!(turtle::Turtle, b::TreeTypes.BudNode, vars)
-    ## Rotate turtle around the arm for insertion angle
+    # Rotate turtle around the arm for insertion angle
     ra!(turtle, -vars.branch_angle)
 end
 #=
@@ -131,10 +141,10 @@ end
 
 The meristem rule is now parameterized by the initial states of the leaves and
 internodes and will only be triggered every X days where X is the plastochron.
+Create right side of the growth rule (parameterized by the initial states
+of the leaves and internodes)
 
 =#
-## Create right side of the growth rule (parameterized by the initial states
-## of the leaves and internodes)
 function create_meristem_rule(vleaf, vint)
     meristem_rule = Rule(TreeTypes.Meristem,
                         lhs = mer -> mod(data(mer).age, graph_data(mer).plastochron) == 0,
@@ -155,36 +165,36 @@ rather than the number of internodes. An adhoc traversal is used to compute this
 length of the main branch a bud belongs to (ignoring the lateral branches).
 
 =#
-## Compute the probability that a bud breaks as function of distance to the meristem
+# Compute the probability that a bud breaks as function of distance to the meristem
 function prob_break(bud)
-    ## We move to parent node in the branch where the bud was created
+    # We move to parent node in the branch where the bud was created
     node =  parent(bud)
-    ## Extract the first internode
+    # Extract the first internode
     child = filter(x -> data(x) isa TreeTypes.Internode, children(node))[1]
     data_child = data(child)
-    ## We measure the length of the branch until we find the meristem
+    # We measure the length of the branch until we find the meristem
     distance = 0.0
     while !isa(data_child, TreeTypes.Meristem)
-        ## If we encounter an internode, store the length and move to the next node
+        # If we encounter an internode, store the length and move to the next node
         if data_child isa TreeTypes.Internode
             distance += data_child.length
             child = children(child)[1]
             data_child = data(child)
-        ## If we encounter a node, extract the next internode
+        # If we encounter a node, extract the next internode
         elseif data_child isa TreeTypes.Node
                 child = filter(x -> data(x) isa TreeTypes.Internode, children(child))[1]
                 data_child = data(child)
         else
             error("Should be Internode, Node or Meristem")
         end
     end
-    ## Compute the probability of bud break as function of distance and
-    ## make stochastic decision
+    # Compute the probability of bud break as function of distance and
+    # make stochastic decision
     prob =  min(1.0, distance*graph_data(bud).budbreak)
     return rand() < prob
 end
 
-## Branch rule parameterized by initial states of internodes
+# Branch rule parameterized by initial states of internodes
 function create_branch_rule(vint)
     branch_rule = Rule(TreeTypes.Bud,
             lhs = prob_break,
@@ -196,7 +206,6 @@ function create_branch_rule(vint)
 end
 #=
 
-
 ### Growth
 
 We need some functions to compute the length and width of a leaf or internode
@@ -236,9 +245,7 @@ sink_strength(leaf, vars) = leaf.age > vars.leaf_expansion ? 0.0 :
                             pdf(leaf.sink, leaf.age/vars.leaf_expansion)/100.0
 plot(0:1:50, x -> sink_strength(TreeTypes.Leaf(age = x), TreeTypes.treeparams()),
      xlabel = "Age", ylabel = "Sink strength", label = "Leaf")
-#=
 
-=#
 sink_strength(int) = pdf(int.sink, int.age)
 plot!(0:1:50, x -> sink_strength(TreeTypes.Internode(age = x)), label = "Internode")
 #=
@@ -275,19 +282,19 @@ strength.
 
 =#
 function grow!(tree, all_leaves, all_internodes)
-    ## Compute total biomass increment
+    # Compute total biomass increment
     tvars = data(tree)
     ΔB    = tvars.RGR*tvars.biomass
     tvars.biomass += ΔB
-    ## Total sink strength
+    # Total sink strength
     total_sink = 0.0
     for leaf in all_leaves
         total_sink += sink_strength(leaf, tvars)
     end
     for int in all_internodes
         total_sink += sink_strength(int)
     end
-    ## Allocate biomass to leaves and internodes
+    # Allocate biomass to leaves and internodes
     for leaf in all_leaves
         leaf.biomass += ΔB*sink_strength(leaf, tvars)/total_sink
     end
@@ -325,18 +332,18 @@ parallel.
 get_meristems(tree) = apply(tree, Query(TreeTypes.Meristem))
 function daily_step!(forest)
     @threads for tree in forest
-        ## Retrieve all the relevant organs
+        # Retrieve all the relevant organs
         all_leaves = get_leaves(tree)
         all_internodes = get_internodes(tree)
         all_meristems = get_meristems(tree)
-        ## Update the age of the organs
+        # Update the age of the organs
         age!(all_leaves, all_internodes, all_meristems)
-        ## Grow the tree
+        # Grow the tree
         grow!(tree, all_leaves, all_internodes)
         tvars = data(tree)
         size_leaves!(all_leaves, tvars)
         size_internodes!(all_internodes, tvars)
-        ## Developmental rules
+        # Developmental rules
         rewrite!(tree)
     end
 end
@@ -350,30 +357,27 @@ orientation and RGR:
 =#
 RGRs = rand(Normal(0.3,0.01), 10, 10)
 histogram(vec(RGRs))
-#=
 
-=#
 orientations = [rand()*360.0 for i = 1:2.0:20.0, j = 1:2.0:20.0]
 histogram(vec(orientations))
-#=
 
-=#
 origins = [Vec(i,j,0) for i = 1:2.0:20.0, j = 1:2.0:20.0];
+nothing #hide
 #=
 
 The following initalizes a tree based on the origin, orientation and RGR:
 
 =#
 function create_tree(origin, orientation, RGR)
-    ## Initial state and parameters of the tree
+    # Initial state and parameters of the tree
     vars = TreeTypes.treeparams(RGR = RGR)
-    ## Initial states of the leaves
+    # Initial states of the leaves
     leaf_length, leaf_width = leaf_dims(vars.LB0, vars)
     vleaf = (biomass = vars.LB0, length = leaf_length, width = leaf_width)
-    ## Initial states of the internodes
+    # Initial states of the internodes
     int_length, int_width = int_dims(vars.LB0, vars)
     vint = (biomass = vars.IB0, length = int_length, width = int_width)
-    ## Growth rules
+    # Growth rules
     meristem_rule = create_meristem_rule(vleaf, vint)
     branch_rule   = create_branch_rule(vint)
     axiom = T(origin) + RH(orientation) +
@@ -387,7 +391,6 @@ function create_tree(origin, orientation, RGR)
 end
 #=
 
-
 ## Visualization
 
 As in the previous example, it makes sense to visualize the forest with a soil
@@ -401,10 +404,10 @@ Base.@kwdef struct Soil <: VirtualPlantLab.Node
     width::Float64
 end
 function VirtualPlantLab.feed!(turtle::Turtle, s::Soil, vars)
-    Rectangle!(turtle, length = s.length, width = s.width, color = RGB(255/255, 236/255, 179/255))
+    Rectangle!(turtle, length = s.length, width = s.width, colors = RGB(255/255, 236/255, 179/255))
 end
-soil_graph = RA(-90.0) + T(Vec(0.0, 10.0, 0.0)) + # Moves into position
-             Soil(length = 20.0, width = 20.0) # Draws the soil tile
+soil_graph = RA(-90.0) + T(Vec(0.0, 10.0, 0.0)) + ## Moves into position
+             Soil(length = 20.0, width = 20.0) ## Draws the soil tile
 soil = Scene(Graph(axiom = soil_graph));
 render(soil, axes = false)
 #=
@@ -415,13 +418,12 @@ or a function):
 
 =#
 function render_forest(forest, soil)
-    scene = Scene(vec(forest)) # create scene from forest
-    scene = Scene([scene, soil]) # merges the two scenes
+    scene = Scene(vec(forest)) ## create scene from forest
+    scene = Scene([scene, soil]) ## merges the two scenes
     render(scene)
 end
 #=
 
-
 ## Retrieving canopy-level data
 
 We may want to extract some information at the canopy level such as LAI. This is
@@ -456,3 +458,4 @@ And compute the leaf area index:
 
 =#
 get_LAI(forest)
+#=