Pluto Catalyst ensemble test

pluto_ensemble_test_cairo.jl

### A Pluto.jl notebook ###
# v0.20.4

using Markdown
using InteractiveUtils

# ╔═╡ af7bcb2c-0757-11f0-10ea-af37b4a77c5e
using Pkg

# ╔═╡ 36875680-4d09-44fd-9c55-e6f3b47dc2df
Pkg.activate(".")

# ╔═╡ be3e028a-bb08-4aaf-974b-8b22fd33319e
using Catalyst, DifferentialEquations, CairoMakie

# ╔═╡ 1a5a9378-3393-4f3c-98a1-6464050dcc23
html"""
<style>main {max-width: 100%; overflow:hidden; padding-right: 5%;}</style>
<style>pluto-editor main {margin-right: 0;}</style>
<style>pluto-output.scroll_y {max-height: 2000px;}</style>
"""

# ╔═╡ 76892d6d-3530-4073-9518-7a8e00c5a1ac
@info "Loaded packages"

# ╔═╡ d7155949-de4b-4d11-b6b7-21fc6d377e31
begin
	simple_switch = @reaction_network begin
		@species M0(t) M1(t)
		@parameters t_on t_off l_on
		@default_noise_scaling 0.1
		@discrete_events begin
			(t==t_on) => [l ~ l_on]
			(t==t_off) => [l ~ 0.0]
		end
		(k_f1,k_b1), M0 <--> M1
		l, M0 --> M1
	end
end

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@info "Defined CRN"

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begin
	t_on=25.0
	t_off=150.0
end

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begin
	u0=[:M0=>100.0,:M1=>0.0]
	ps=[:k_f1=>0.01,:k_b1=>0.5,:l=>0.0,:t_on=>t_on,:t_off=>t_off,:l_on=>0.1]
	tspan=(0.0,200.0)
	u0_int=[:M0=>100,:M1=>0]
end

# ╔═╡ 65bca955-6949-4448-9e83-cf761c7de6df
begin
	ode=ODEProblem(simple_switch,u0,tspan,ps)
	sde=SDEProblem(simple_switch,u0,tspan,ps)
	jinput = JumpInputs(simple_switch,u0_int,tspan,ps)
	jprob=JumpProblem(jinput)
end

# ╔═╡ 149c9bcb-4277-46de-94c4-3d2fd96b32e8
begin
	probs=[ode,sde,jprob]
	eprobs=[EnsembleProblem(probs[i]) for i in 1:3]
end

# ╔═╡ 13eb7625-4aec-4807-b968-88cfd34c8b67
@info "Made problems"

# ╔═╡ baec605a-2cdd-4582-ad2a-955aa8d870fc
begin
	f1=Figure(size=(1500,500))
	ax1=[Axis(f1[1,i]) for i in 1:3]
	[ax1[i].title=["ODE","SDE","Jump"][i] for i in 1:3]
	lab1=Label(f1[0,:], "Single simulation plots",tellwidth=false,fontsize=24)
end

# ╔═╡ bc799ae0-e094-42d2-bc9c-457efd14cf27
md"""
### Single simulation works fine 
"""

# ╔═╡ 29a5ab4b-cb03-4e3d-9709-b5a8e9ffdffa
for j in 1:3
    sol=solve(probs[j];tstops=[t_on,t_off])
    lines!(ax1[j],0:1:200,sol.(0:1:200,idxs=2),color=:blue)
end

# ╔═╡ 1aa9535f-4a10-4de8-bf11-4735f42f3a83
f1

# ╔═╡ 6dde4dc4-3b3a-405c-9217-150b2834e890
save("single_plot.png", f1)

# ╔═╡ 9c93c8ae-e101-49ec-92eb-d65993c7d676
md"""
### Ensemble simulation introduces random perturbations
For example, the ODE simlations should be identical across the ensemble
"""

# ╔═╡ 711948cb-473e-41b3-89b4-314187113614
begin
	f2=Figure(size=(1500,500))
	ax2=[Axis(f2[1,i]) for i in 1:3]
	[ax2[i].title=["ODE","SDE","Jump"][i] for i in 1:3]
	lab2=Label(f2[0,:], "Ensemble simulation plots",tellwidth=false,fontsize=24)
end

# ╔═╡ 54d34ca1-7eaf-47a6-99c8-220b37bf985b
for j in 1:3
    if j==3
        sol=solve(eprobs[j],SSAStepper();trajectories=100,tstops=[t_on,t_off])
    else
        sol=solve(eprobs[j];trajectories=100,tstops=[t_on,t_off])
    end
    for k in 1:100
        lines!(ax2[j],0:1:200,sol[k].(0:1:200,idxs=2),color=:blue,alpha=0.1)
    end
end

# ╔═╡ bccff2a4-cad8-4d4e-a6e3-6d009795b85f
f2

# ╔═╡ 751ed39b-fefa-4780-bbf3-2b0c72ec90e3
save("ensemble_plot.png", f2)

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