List of symbols and tunable flag (#466)

* switched from symbolic arrays to list of symbolic parameters. Needed to be able to set tunable flag for individual parameters of the array. Added untune! utility function to set tunable flag to false on list elements.

* added new approach to set tunable flag for the connection matrix also to the tutorial

docs/src/tutorials/spectralDCM.jl

@@ -129,8 +129,15 @@ end
 A_prior = 0.01*randn(nr, nr)
 A_prior -= diagm(diag(A_prior))    # ensure diagonal dominance of matrix
 # Since we want to optimize these weights we turn them into symbolic parameters:
-# Add the symbolic weights to the edges and connect reegions.
-@parameters A[1:nr^2] = vec(A_prior) [tunable = true]
+# Add the symbolic weights to the edges and connect regions.
+A = []
+for (i, a) in enumerate(vec(A_prior))
+    symb = Symbol("A$(i)")
+    push!(A, only(@parameters $symb = a))
+end
+# With the function `untune!`` we can list indices of parameters whose tunable flag should be set to false.
+# For instance the first element in the second row:
+untune!(A, [4])
 for (i, idx) in enumerate(CartesianIndices(A_prior))
     if idx[1] == idx[2]
         add_edge!(g, regions[idx[1]] => regions[idx[2]]; :weight => -exp(A[i])/2)  # -exp(A[i])/2: treatement of diagonal elements in SPM12 to make diagonal dominance (see Gershgorin Theorem) more likely but it is not guaranteed

src/Neuroblox.jl

@@ -236,7 +236,7 @@ export system_from_graph, graph_delays
 export create_adjacency_edges!, adjmatrixfromdigraph
 export get_namespaced_sys, nameof
 export run_experiment!, run_trial!
-export addnontunableparams
+export addnontunableparams, untune!
 export get_weights, get_dynamic_states, get_idx_tagged_vars, get_eqidx_tagged_vars
 export BalloonModel,LeadField, boldsignal_endo_balloon
 export PINGNeuronExci, PINGNeuronInhib

src/blox/blox_utilities.jl

@@ -17,6 +17,12 @@ function paramscoping(;kwargs...)
     return paramlist
 end
 
+function untune!(parlist, nontunable)
+    for i in nontunable
+        parlist[i] = setmetadata(parlist[i], ModelingToolkit.VariableTunable, false)
+    end
+end
+
 get_HH_exci_neurons(n::HHNeuronExciBlox) = [n]
 get_HH_exci_neurons(n) = []
 

test/datafitting.jl

@@ -33,7 +33,12 @@ using MAT
     end
 
     # add symbolic weights
-    @parameters A[1:length(vars["pE"]["A"])] = vec(vars["pE"]["A"]) [tunable = true]
+    A = []
+    for (i, a) in enumerate(vec(vars["pE"]["A"]))
+        symb = Symbol("A$(i)")
+        push!(A, only(@parameters $symb = a))
+    end
+    untune!(A, [])   # list indices of parameters that should be set to tunable=false
     for (i, idx) in enumerate(CartesianIndices(vars["pE"]["A"]))
         if idx[1] == idx[2]
             add_edge!(g, regions[idx[1]], regions[idx[2]], :weight, -exp(A[i])/2)  # treatement of diagonal elements in SPM12, likely to avoid instabilities of the linear model