Remove old component tests

* Deleted `jansen_rit_component_tests.jl`.
* Renamed `jansen_rit_component_tests_new_timing.jl` to `jansen_rit_component_tests.jl` (removing old examples in favor of updated blocks with ms timing defaults).
* Deleted `temp_test.jl`
* Deleted `jansen_rit_hemodynamic_tests.jl`
* Deleted `test/old_component_tests/*`. These were all old neural mass model tests from pre-`BloxConnector` days that I kept around just in case. Seem redundant now.
* @hstrey I think `test/ode_from_graph.jl` can be deleted - looks like old Jansen Rit code that we've incorporated elsewhere or removed entirely.

test/jansen_rit_component_tests.jl

@@ -1,23 +1,19 @@
-"""
-DEPRECATED
-
-This file uses the old blocks before timing update to be consistent. Use jansen_rit_component_tests_new_timing.jl instead.
-"""
-
 using Neuroblox, DifferentialEquations, DataFrames, Test, Distributions, Statistics, LinearAlgebra, Graphs, MetaGraphs, Random
 
+τ_factor = 1000 #needed because the old units were in seconds, and we need ms to be consistent
+
 # Create Regions
-@named Str = jansen_ritC(τ=0.0022, H=20, λ=300, r=0.3)
-@named GPe = jansen_ritC(τ=0.04, H=20, λ=400, r=0.1)
-@named STN = jansen_ritC(τ=0.01, H=20, λ=500, r=0.1)
-@named GPi = jansen_ritSC(τ=0.014, H=20, λ=400, r=0.1)
-@named Th  = jansen_ritSC(τ=0.002, H=10, λ=20, r=5)
-@named EI  = jansen_ritSC(τ=0.01, H=20, λ=5, r=5)
-@named PY  = jansen_ritSC(τ=0.001, H=20, λ=5, r=0.15)
-@named II  = jansen_ritSC(τ=2.0, H=60, λ=5, r=5)
+@named Str = jansen_ritC(τ=0.0022*τ_factor, H=20, λ=300, r=0.3)
+@named gpe = jansen_ritC(τ=0.04*τ_factor, H=20, λ=400, r=0.1)
+@named stn = jansen_ritC(τ=0.01*τ_factor, H=20, λ=500, r=0.1)
+@named gpi = jansen_ritSC(τ=0.014*τ_factor, H=20, λ=400, r=0.1)
+@named Th  = jansen_ritSC(τ=0.002*τ_factor, H=10, λ=20, r=5)
+@named EI  = jansen_ritSC(τ=0.01*τ_factor, H=20, λ=5, r=5)
+@named PY  = jansen_ritSC(τ=0.001*τ_factor, H=20, λ=5, r=0.15)
+@named II  = jansen_ritSC(τ=2.0*τ_factor, H=60, λ=5, r=5)
 
 # Connect Regions through Adjacency Matrix
-blox = [Str, GPe, STN, GPi, Th, EI, PY, II]
+blox = [Str, gpe, stn, gpi, Th, EI, PY, II]
 sys = [s.odesystem for s in blox]
 connect = [s.connector for s in blox]
 
@@ -34,10 +30,10 @@ adj_matrix_lin = [0 0 0 0 0 0 0 0;
 
 @named CBGTC_Circuit_lin = LinearConnections(sys=sys, adj_matrix=adj_matrix_lin, connector=connect)
 
-sim_dur = 10.0 # Simulate for 10 Seconds
+sim_dur = 2000.0 # Simulate for 2 seconds
 mysys = structural_simplify(CBGTC_Circuit_lin)
-sol = simulate(mysys, [], (0.0, sim_dur), [], Vern7(); saveat=0.001)
-@test sol[!, "GPi₊x(t)"][4] ≈ -0.48421810231972134
+sol = simulate(mysys, [], (0.0, sim_dur), [], Vern7(); saveat=1)
+@test sol[!, "gpi₊x(t)"][4] ≈ -2219.2560209502685 #updated to new value in ms
 
 """
 Testing new Jansen-Rit blox
@@ -50,15 +46,15 @@ same thing as the old simulate call with AutoVern7(Rodas4() since there are no d
 """
 
 # test new Jansen-Rit blox
-@named Str = JansenRit(τ=0.0022, H=20, λ=300, r=0.3)
-@named GPe = JansenRit(τ=0.04, cortical=false) # all default subcortical except τ
-@named STN = JansenRit(τ=0.01, H=20, λ=500, r=0.1)
-@named GPi = JansenRit(cortical=false) # default parameters subcortical Jansen Rit blox
-@named Th  = JansenRit(τ=0.002, H=10, λ=20, r=5)
-@named EI  = JansenRit(τ=0.01, H=20, λ=5, r=5)
+@named Str = JansenRit(τ=0.0022*τ_factor, H=20, λ=300, r=0.3)
+@named gpe = JansenRit(τ=0.04*τ_factor, cortical=false) # all default subcortical except τ
+@named stn = JansenRit(τ=0.01*τ_factor, H=20, λ=500, r=0.1)
+@named gpi = JansenRit(cortical=false) # default parameters subcortical Jansen Rit blox
+@named Th  = JansenRit(τ=0.002*τ_factor, H=10, λ=20, r=5)
+@named EI  = JansenRit(τ=0.01*τ_factor, H=20, λ=5, r=5)
 @named PY  = JansenRit(cortical=true) # default parameters cortical Jansen Rit blox
-@named II  = JansenRit(τ=2.0, H=60, λ=5, r=5)
-blox = [Str, GPe, STN, GPi, Th, EI, PY, II]
+@named II  = JansenRit(τ=2.0*τ_factor, H=60, λ=5, r=5)
+blox = [Str, gpe, stn, gpi, Th, EI, PY, II]
 
 # test graphs
 g = MetaDiGraph()
@@ -99,16 +95,16 @@ add_edge!(g, 8, 8, Dict(:weight => 3.3*C_Cor))
 
 @named final_system = system_from_graph(g, params)
 final_delays = graph_delays(g)
-sim_dur = 10.0 # Simulate for 10 Seconds
+sim_dur = 2000.0 # Simulate for 2 Seconds
 final_system_sys = structural_simplify(final_system)
 prob = DDEProblem(final_system_sys,
     [],
     (0.0, sim_dur),
     constant_lags = final_delays)
 alg = MethodOfSteps(Vern7())
-sol_dde_no_delays = solve(prob, alg, saveat=0.001)
+sol_dde_no_delays = solve(prob, alg, saveat=1)
 sol2 = DataFrame(sol_dde_no_delays)
-@test isapprox(sol2[!, "GPi₊x(t)"][500:1000], sol[!, "GPi₊x(t)"][500:1000], rtol=1e-8)
+@test isapprox(sol2[!, "gpi₊x(t)"][500:1000], sol[!, "gpi₊x(t)"][500:1000], rtol=1e-8)
 
 
 # Alternative version using adjacency matrix
@@ -118,13 +114,13 @@ create_adjacency_edges!(g2, adj_matrix_lin)
 
 @named final_system = system_from_graph(g2, params)
 final_delays = graph_delays(g2)
-sim_dur = 600.0 # Simulate for 10 Seconds
+sim_dur = 2000.0 # Simulate for 2 Seconds
 final_system_sys = structural_simplify(final_system)
 prob = DDEProblem(final_system_sys,
     [],
     (0.0, sim_dur),
     constant_lags = final_delays)
 alg = MethodOfSteps(Vern7())
-sol_dde_no_delays = solve(prob, alg, saveat=0.001)
+sol_dde_no_delays = solve(prob, alg, saveat=1)
 sol3 = DataFrame(sol_dde_no_delays)
-@test isapprox(sol3[!, "GPi₊x(t)"][500:1000], sol[!, "GPi₊x(t)"][500:1000], rtol=1e-8)
+@test isapprox(sol3[!, "gpi₊x(t)"][500:1000], sol[!, "gpi₊x(t)"][500:1000], rtol=1e-8)