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testJulia4.jl~
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using DifferentialEquations, Phylo, Plots, Distributions, Distances, JLD2, LinearAlgebra, GpABC; pyplot();
#####################################################################################
#####################################################################################
function menura!(tree)
function diffusion(x0, tspan, p, mat, dt=0.001)
function drift(du, u, p, t)
alpha = p.alpha;
mu = p.mu;
## du .= alpha .* u would be BM with drift
du .= alpha .* (mu .- u); ## OU-like
end # drift
function diff(du, u, p, t)
sigma = p.sigma;
du .= sigma; ## would be OU
## du .= sqrt.(u) .* sigma ## Cox-Ingersoll-Ross Gamma model
## du .= sqrt.(abs.(u .* (ones(length(sigma)) .- u))) .* sigma ## Beta model
end # diff
cor1 = cor(mat);
noise = CorrelatedWienerProcess(cor1, tspan[1],
zeros(dim(cor1)),
zeros(dim(cor1)));
prob = SDEProblem(drift, diff, x0, tspan, p=p, noise=noise);
solve(prob, EM(), dt=dt, p=p, adaptive=false);
end # diffusion
################################################################################
function Recurse!(tree, node, t0 = 0.0)
if ismissing(node.inbound) ## the root node, to get started
node.data["trace"]= [x0];
node.data["timebase"] = [t0];
else
ancestor = getancestors(tree, node)[1]
evol = diffusion(ancestor.data["trace"][end],
(getheight(tree, ancestor), getheight(tree, node)),
ancestor.data["parameters"], ancestor.data["matrix"]);
node.data["trace"] = evol.u
node.data["timebase"] = evol.t
end # else
if !isleaf(tree, node)
Recurse!(tree, node.other[1].inout[2]);
Recurse!(tree, node.other[2].inout[2]);
end
end # Recurse!
root = getroot(tree)
Recurse!(tree, root) # do the recursive simulations
tree
end # menura!
###############################################################################
################################################################################
function menuramat!(tree) ## Only call after putp!
function Recurse!(tree, node)
if ismissing(node.inbound) ## if root
node.data["matrix"] = node.data["parameters"].mat ## starting matrix
else
ancestor = getancestors(tree, node)[1]
node.data["matrix"] =
gen_cov_mat(ancestor.data["matrix"],
ancestor.data["parameters"],
(getheight(tree, ancestor),
getheight(tree, node)));
end
if !isleaf(tree, node)
Recurse!(tree, node.other[1].inout[2]);
Recurse!(tree, node.other[2].inout[2]);
end
end
root = getroot(tree)
Recurse!(tree, root) # do the recursive simulations
tree
end # menuramat!
############################################################################33
#############################################################################
function predictTraitTree(tree)
#### get the last multivariate trait value in a branch
testtips = getleaves(tree);
res = Array{Vector{Float64}}(undef, length(testtips));
tipnames = Array{String}(undef, length(testtips));
tiptimes = Vector{Float64}();
## finaltraitvals = Dict();
## tiptimesdict = Dict();
for i in 1:length(testtips) ## could maybe use heightstoroot() for this computation
res[i] = testtips[i].data["trace"][end];
tipnames[i] = testtips[i].name;
push!(tiptimes, getheight(tree, testtips[i]));
end
collect(Iterators.flatten(res))
end # predictTraitTree
##########################################################################33
###########################################################################3
function putp!(tree, p1, key)
for i in 1:length(tree.nodes) ## 'other' means branches
tree.nodes[i].data[key] = p1;
end
tree;
end # putp!
####################################################################33
######################################################################3
function gen_cov_mat(mat, p, tspan, u0=zeros(size(mat)), dt = 0.001)
function drift(du, u, p, t) ## drift function for the SDE
du .= p.a .* t .* p.A
end # drift
function diffusion(du, u, p, t) ## diffusion function for the SDE
du .= p.b .* t .* p.B
end # diffusion
lowertri = LowerTriangular(mat);
uppertri = - UpperTriangular(mat);
skewsymm = lowertri + uppertri;
W = WienerProcess(0.0, 0.0, 0.0)
pp = (A=skewsymm, B=skewsymm, a=p.a, b=p.b); ## skew symmetric matrices not necessarily the same.
prob = SDEProblem(drift, diffusion, u0, tspan, p=pp, noise=W,
noise_rate_prototype=zeros(size(mat))); ## setup SDE problem
sol = solve(prob, EM(), p=pp, dt=dt);
Omega1 = exp(last(sol.u)) ## get the final matrix
Omega1 * mat * Omega1' ## reconstruct P_1
end # gen_cov_mat
######################################################################3
##################################################################3n
a1=1.0;
b1= 1.0;
x0 = repeat([0.0], 8);
tree1 = Ultrametric(20);
tree = rand(tree1);
time_tot = 1.0
tspan = (0.0, time_tot)
# P0 = [0.6856935 -0.0424340 1.2743154 0.5162707;
# -0.0424340 0.1899794 -0.3296564 -0.1216394;
# 1.2743154 -0.3296564 3.1162779 1.2956094;
# 0.5162707 -0.1216394 1.2956094 0.5810063];
##P0 = Diagonal([1, 1, 1, 1])
P0 = [0.329 0.094 -0.083 -0.089 0.293 0.079 0.208 0.268;
0.094 0.449 0.349 0.24 0.071 0.075 0.03 0.009;
-0.083 0.349 1.426 0.487 -0.371 -0.098 -0.053 -0.172;
-0.089 0.24 0.487 0.546 -0.168 0.017 -0.051 -0.081;
0.293 0.071 -0.371 -0.168 1.441 1.008 0.904 0.945;
0.079 0.075 -0.098 0.017 1.008 1.087 0.731 0.78;
0.208 0.03 -0.053 -0.051 0.904 0.731 0.809 0.783;
0.268 0.009 -0.172 -0.081 0.945 0.78 0.783 0.949]
# P0 = [1.54 0.26 1.14 0.26;
# 0.26 0.46 0.26 0.06;
# 1.14 0.26 1.54 0.26;
# 0.26 0.06 0.26 0.46]
mat = P0
u0=zeros(size(mat))
alpha1 = repeat([1.0], 8);
mu1 = repeat([0.0], 8) ## randn(8); ## Start at the trait means
sigma1 = repeat([1.0], 8)
# function simulate((alpha, mu, sigma))
# p1 = (alpha=alpha, mu=mu, sigma=sigma, mat=P0, a=a1, b=b1)
# putp!(tree, p1, "parameters");
# menuramat!(tree);
# menura!(tree);
# (tree, predictTraitTree(tree))
# end
function simulate(tree, alpha=alpha1, sigma=sigma1, mu=mu1, mat=P0, a=a1, b=b1)
p1 = (alpha=alpha, mu=mu, sigma=sigma, mat=P0, a=a, b=b)
putp!(tree, p1, "parameters");
menuramat!(tree);
menura!(tree);
(tree, predictTraitTree(tree))
end
exampledat = simulate(tree, alpha1, sigma1, mu1);
# priordists = [Truncated(Normal(0,3), 0, Inf)]
# priors = Factored(repeat(priordists, 12)...,)
# function cost((alpha, mu, sigma))
# x=simulate((alpha, mu, sigma))[2]
# y=exampledat[2]
# euclidean(x, y)
# end #cost
# approx_density = ApproxKernelizedPosterior(priors,cost,0.005)
# ## res = sample(approx_density, AIS(25), 1000, ntransitions=100, discard_initial = 10)
## save_object("ABCResults.jld2", res)
########################################################################
#########################################################################
plot(tree,
size = (400, 800),
## markersize = 20,
series_annotations = text.(1:nnodes(tree), 15, :center, :center,
:white), linewidth=5, showtips=false)
current()
testnodes = getnodes(exampledat[1]);
plot(xlim = (0.0,1.0), ylim = (-2.0, 2.0), zlim=(-2.0, 2.0),
legend=nothing,
reuse=false)
for i in testnodes
u1= i.data["trace"]
uu1 = transpose(reshape(collect(Iterators.flatten(u1)), 8, length(u1)))
myt = i.data["timebase"]
plot!(myt, uu1[:, 1], uu1[:,3])
end
current()
# plot(xlim=(0.0,1.0), ylim= (2.0, 6.0), legend=nothing, reuse=false)
# for i in testnodes
# u1= i.data["trace"]
# uu1 = transpose(reshape(collect(Iterators.flatten(u1)), 8, length(u1)))
# myt = i.data["timebase"]
# plot!(myt, uu1[:, 3])
# end
# current()
plot(ylim=(-2.0, 2.0), xlim= (-2.0, 2.0), legend=nothing, reuse=false)
for i in testnodes
u1= i.data["trace"]
uu1 = transpose(reshape(collect(Iterators.flatten(u1)), 8, length(u1)))
plot!(uu1[:,1], uu1[:, 3])
end
current()
# plot(xlim=(2.0,6.0), ylim= (4.0, 8.0), zlim=(0.0, 5.0), legend=nothing,
# reuse=false)
# for i in testnodes
# u1= i.data["trace"]
# uu1 = transpose(reshape(collect(Iterators.flatten(u1)), 8, length(u1)))
# myt = i.data["timebase"]
# plot!(uu1[:,3], uu1[:, 1], uu1[:,2])
# end
# current()
######################################################################333
#########################################################################
##########################################################################