using VirtualPlantLab

import GLMakie

module Queries

using VirtualPlantLab

struct A <: Node end

struct C <: Node

val::Float64

end

struct B <: Node

ID::Int

end

end

import .Queries as Q

motif(n, i = 0) = Q.A() + (Q.C(45.0) + Q.A() + (Q.C(45.0) + Q.A() + Q.B(i + 1),

Q.C(-45.0) + Q.A() + Q.B(i + 2),

Q.A() + Q.B(i + 3)),

Q.C(- 45.0) + sum(Q.A() for i in 1:n) + Q.B(i + 4))

axiom = motif(3, 0) + motif(2, 4) + motif(1, 8) + Q.A() + Q.A() + Q.B(13)

graph = Graph(axiom = axiom);

draw(graph)

VirtualPlantLab.node_label(n::Q.B, id) = "B-$(n.ID)"

VirtualPlantLab.node_label(n::Q.A, id) = "A"

VirtualPlantLab.node_label(n::Q.C, id) = "C"

draw(graph)

Q1 = Query(Q.B)

A1 = apply(graph, Q1)

function Q2_fun(n)

check, steps = has_ancestor(n, condition = x -> data(x) isa Q.C)

!check

end

Q2 = Query(Q.B, condition = Q2_fun)

A2 = apply(graph, Q2)

function branch_motif(p)

data(p) isa Q.A &&

has_descendant(p, condition = x -> data(x) isa Q.C && data(x).val < 0.0)[1] &&

has_ancestor(p, condition = x -> data(x) isa Q.C && data(x).val > 0.0)[1]

end

function Q3_fun(n, nsteps)

# Condition 1

check, steps = has_ancestor(n, condition = branch_motif)

!check && return false

# Condition 2

p = parent(n, nsteps = steps)

check, steps = has_ancestor(p, condition = is_root)

steps != nsteps && return false

return true

end

Q3 = Query(Q.B, condition = n -> Q3_fun(n, 2))

A3 = apply(graph, Q3)

function Q4_fun(n)

!has_ancestor(n, condition = x -> is_root(x) && length(children(x)) > 1)[1]

end

Q4 = Query(Q.B, condition = Q4_fun)

A4 = apply(graph, Q4)

function Q5_fun(n)

check, steps = has_ancestor(n, condition = is_root)

steps == 4

end

Q5 = Query(Q.B, condition = Q5_fun)

A5 = apply(graph, Q5)

function Q6_fun(n, nA)

check = Q3_fun(n, nA)

!check && return false

p2 = parent(n, nsteps = 2)

data(p2) isa Q.A

end

Q6 = Query(Q.B, condition = n -> Q6_fun(n, 3))

A6 = apply(graph, Q6)

Q7 = Query(Q.B, condition = n -> Q6_fun(n, 4) || Q2_fun(n))

A7 = apply(graph, Q7)

function Q8_fun(n)

p1 = parent(n)

p2 = parent(n, nsteps = 2)

p3 = parent(n, nsteps = 3)

data(p1) isa Q.A && data(p2) isa Q.C && data(p2).val > 0.0 && data(p3) isa Q.A

end

Q8 = Query(Q.B, condition = Q8_fun)

A8 = apply(graph, Q8)

function Q9_fun(n)

p1 = parent(n)

p2 = parent(n, nsteps = 2)

p3 = parent(n, nsteps = 3)

p4 = parent(n, nsteps = 4)

data(p1) isa Q.A && data(p2) isa Q.C && data(p2).val < 0.0 &&

data(p3) isa Q.A && data(p4) isa Q.C

end

Q9 = Query(Q.B, condition = Q9_fun)

A9 = apply(graph, Q9)

function Q10_fun(n)

Q6_fun(n, 3) && return true ## Check node 7

Q9_fun(n) && has_ancestor(n, condition = is_root)[2] == 6 && return true ## Check node 6

has_ancestor(n, condition = is_root)[2] == 5 && data(parent(n, nsteps = 3)) isa Q.C && return true ## Check node 8 (and not 4!)

end

Q10 = Query(Q.B, condition = Q10_fun)

A10 = apply(graph, Q10)

function Q11_fun(n)

Q5_fun(n) && return true ## 3

Q6_fun(n, 3) && return true ## 7

Q6_fun(n, 4) && return true ## 11

has_ancestor(n, condition = is_root)[2] == 5 && data(parent(n, nsteps = 2)) isa Q.C &&

data(parent(n, nsteps = 4)) isa Q.A && return true ## 12

end

Q11 = Query(Q.B, condition = Q11_fun)

A11 = apply(graph, Q11)

function Q12_fun(n)

Q6_fun(n, 3) && return true ## 7

has_ancestor(n, condition = is_root)[2] == 5 && data(parent(n, nsteps = 2)) isa Q.C &&

data(parent(n, nsteps = 4)) isa Q.A && return true ## 12

end

Q12 = Query(Q.B, condition = Q12_fun)

A12 = apply(graph, Q12)

# This file was generated using Literate.jl, https://github.com/fredrikekre/Literate.jl