Change BloxConnector to Connector

src/Neuroblox.jl

@@ -116,11 +116,11 @@ include("blox/winnertakeall.jl")
 include("blox/subcortical_blox.jl")
 include("blox/stochastic.jl")
 include("blox/discrete.jl")
+include("blox/ping_neuron_examples.jl")
 include("blox/reinforcement_learning.jl")
 include("gui/GUI.jl")
 include("blox/connections.jl")
 include("blox/blox_utilities.jl")
-include("blox/ping_neuron_examples.jl")
 include("GraphDynamicsInterop/GraphDynamicsInterop.jl")
 include("Neurographs.jl")
 include("adjacency.jl")
@@ -256,4 +256,5 @@ export meanfield, meanfield!, rasterplot, rasterplot!, stackplot, stackplot!, fr
 export powerspectrumplot, powerspectrumplot!, welch_pgram, periodogram, hanning, hamming
 export detect_spikes, mean_firing_rate, firing_rate
 export voltage_timeseries, meanfield_timeseries, state_timeseries, get_neurons, get_exci_neurons, get_inh_neurons, get_neuron_color
+export AdjacencyMatrix, Connector, connection_rule, connection_equation
 end

src/Neurographs.jl

@@ -52,30 +52,38 @@ get_dynamics_bloxs(blox::CompositeBlox) = get_parts(blox)
 
 flatten_graph(g::MetaDiGraph) = mapreduce(get_dynamics_bloxs, vcat, get_bloxs(g))
 
-function connector_from_graph(g::MetaDiGraph)
-    bloxs = get_bloxs(g)
-    link = BloxConnector(bloxs)
+function connectors_from_graph(g::MetaDiGraph)
+    conns = get_connector.(get_bloxs(g))
+    for edge in edges(g)
 
-    for v in vertices(g)
-        b = get_prop(g, v, :blox)
-        for vn in inneighbors(g, v)
-            bn = get_prop(g, vn, :blox)
-            kwargs = props(g, vn, v)
-            link(bn, b; kwargs...)
-        end
+        blox_src = get_prop(g, edge.src, :blox)
+        blox_dest = get_prop(g, edge.dst, :blox)
+
+        kwargs = props(g, edge)
+        push!(conns, Connector(blox_src, blox_dest; kwargs...))
     end
-    return link
+
+    filter!(conn -> !isempty(conn), conns)
+
+    return conns
+end
+
+function connector_from_graph(g::MetaDiGraph)
+    conns = connectors_from_graph(g)
+
+    return isempty(conns) ? Connector(:none, :none) : reduce(merge!, conns)
 end
 
 # Helper function to get delays from a graph
 function graph_delays(g::MetaDiGraph)
-    bc = connector_from_graph(g)
-    return bc.delays
+    conn = connector_from_graph(g)
+
+    return conn.delay
 end
 
-generate_discrete_callbacks(blox, ::BloxConnector; t_block = missing) = []
+generate_discrete_callbacks(blox, ::Connector; t_block = missing) = []
 
-function generate_discrete_callbacks(blox::Union{LIFExciNeuron, LIFInhNeuron}, bc::BloxConnector; t_block = missing)
+function generate_discrete_callbacks(blox::Union{LIFExciNeuron, LIFInhNeuron}, bc::Connector; t_block = missing)
     spike_affects = get_spike_affects(bc)
     name_blox = namespaced_nameof(blox)
     sys = get_namespaced_sys(blox)
@@ -116,7 +124,7 @@ function generate_discrete_callbacks(blox::Union{LIFExciNeuron, LIFInhNeuron}, b
     return cb
 end
 
-function generate_discrete_callbacks(blox::HHNeuronExciBlox, ::BloxConnector; t_block = missing)
+function generate_discrete_callbacks(blox::HHNeuronExciBlox, ::Connector; t_block = missing)
     if !ismissing(t_block)
         nn = get_namespaced_sys(blox)
         eq = nn.spikes_window ~ 0
@@ -128,7 +136,7 @@ function generate_discrete_callbacks(blox::HHNeuronExciBlox, ::BloxConnector; t_
     end
 end
 
-function generate_discrete_callbacks(bc::BloxConnector; t_block = missing)
+function generate_discrete_callbacks(bc::Connector; t_block = missing)
     eqs_params = get_equations_with_parameter_lhs(bc)
 
     if !ismissing(t_block) && !isempty(eqs_params)
@@ -139,7 +147,7 @@ function generate_discrete_callbacks(bc::BloxConnector; t_block = missing)
     end 
 end
 
-function generate_discrete_callbacks(g::MetaDiGraph, bc::BloxConnector; t_block = missing)
+function generate_discrete_callbacks(g::MetaDiGraph, bc::Connector; t_block = missing)
     bloxs = flatten_graph(g)
 
     cbs = mapreduce(vcat, bloxs) do blox
@@ -175,30 +183,34 @@ function system_from_graph(g::MetaDiGraph, p::Vector{Num}=Num[]; name=nothing, t
         isempty(p) || error(ArgumentError("The GraphDynamics.jl backend does yet support extra parameter lists. Got $p."))
         GraphDynamicsInterop.graphsystem_from_graph(g; kwargs...)
     else
-        bc = connector_from_graph(g)
         if isnothing(name)
             throw(UndefKeywordError(:name))
         end
+
+        bc = connector_from_graph(g)
+
         return system_from_graph(g, bc, p; name, t_block, simplify, kwargs...)
     end
 end
 
-function system_from_graph(g::MetaDiGraph, bc::BloxConnector, p::Vector{Num}=Num[];
-                           name, t_block=missing, simplify=true, simplify_kwargs...)
-    blox_syss = get_system(g)
+function system_from_graph(g::MetaDiGraph, bc::Connector, p::Vector{Num}=Num[]; name=nothing, t_block=missing, simplify=true, graphdynamics=false, kwargs...)
+    bloxs = get_bloxs(g)
+    blox_syss = get_system.(bloxs)
+
+    accumulate_equations!(bc, bloxs)
+
     connection_eqs = get_equations_with_state_lhs(bc)
 
     discrete_cbs = identity.(generate_discrete_callbacks(g, bc; t_block))
 
     sys = compose(System(connection_eqs, t, [], vcat(params(bc), p); name, discrete_events = discrete_cbs), blox_syss)
     if simplify
-        structural_simplify(sys; simplify_kwargs...)
+        structural_simplify(sys; kwargs...)
     else
         sys
     end
 end
 
-
 function system_from_parts(parts::AbstractVector; name)
     return compose(System(Equation[], t; name), get_system.(parts))
 end

src/adjacency.jl

@@ -3,28 +3,33 @@ struct AdjacencyMatrix
     names::Vector{Symbol}
 end
 
-function AdjacencyMatrix(name)
-    return AdjacencyMatrix(spzeros(1,1), [name])
+function AdjacencyMatrix(names::AbstractVector)
+    return AdjacencyMatrix(spzeros(1,1), names)
 end
 
-function Base.merge(adj1::AdjacencyMatrix, adj2::AdjacencyMatrix)
-    return AdjacencyMatrix(
-        cat(adj1.matrix, adj2.matrix; dims=(1,2)), 
-        vcat(adj1.names, adj2.names)
-    )
-end
+function AdjacencyMatrix(C::Connector)
+    weights = C.weight
+    srcs = C.source
+    dests = C.destination
+    names = unique(vcat(srcs, dests))
+    sort!(names)
 
-get_adjacency(bc::BloxConnector) = bc.adjacency
-get_adjacency(blox::CompositeBlox) = (get_adjacency ∘ get_connector)(blox)
-get_adjacency(blox) = AdjacencyMatrix(namespaced_nameof(blox))
+    ADJ = AdjacencyMatrix(spzeros(length(names), length(names)), names)
+    for i in eachindex(srcs)
+        add_adjacency_edge!(ADJ, srcs[i], dests[i], weights[i])
+    end
 
-function get_adjacency(g::MetaDiGraph)
-    bc = connector_from_graph(g)
-    return get_adjacency(bc)
+    return ADJ
 end
 
-function get_adjacency(bc::BloxConnector, sys::AbstractODESystem, prob::ODEProblem)
-    A = get_adjacency(bc)
+AdjacencyMatrix(blox::CompositeBlox) = AdjacencyMatrix(get_connector(blox))
+
+AdjacencyMatrix(blox) = AdjacencyMatrix(namespaced_nameof(blox))
+
+AdjacencyMatrix(g::MetaDiGraph) = AdjacencyMatrix(connector_from_graph(g))
+
+function AdjacencyMatrix(bc::Connector, sys::AbstractODESystem, prob::ODEProblem)
+    A = AdjacencyMatrix(bc)
     names = A.names
     mat = A.matrix
 
@@ -43,12 +48,29 @@ function get_adjacency(bc::BloxConnector, sys::AbstractODESystem, prob::ODEProbl
     return AdjacencyMatrix(S, names)
 end
 
-function get_adjacency(agent::Agent)
+function AdjacencyMatrix(agent::Agent)
     prob = agent.problem
     sys = get_system(agent)
     bc = get_connector(agent)
 
-    return get_adjacency(bc, sys, prob)
+    return AdjacencyMatrix(bc, sys, prob)
+end
+
+function add_adjacency_edge!(ADJ::AdjacencyMatrix, name_src, name_dest, weight)
+    src_idx = findfirst(x -> isequal(name_src, x), ADJ.names)
+    dest_idx = findfirst(x -> isequal(name_dest, x), ADJ.names)
+
+    weight_def = ModelingToolkit.getdefault(weight)
+    weight_value = substitute(weight_def, map(x -> x => ModelingToolkit.getdefault(x), Symbolics.get_variables(weight_def)))
+
+    ADJ.matrix[src_idx, dest_idx] = weight_value
+end
+
+function Base.merge(adj1::AdjacencyMatrix, adj2::AdjacencyMatrix)
+    return AdjacencyMatrix(
+        cat(adj1.matrix, adj2.matrix; dims=(1,2)), 
+        vcat(adj1.names, adj2.names)
+    )
 end
 
 function adjmatrixfromdigraph(g::MetaDiGraph)

src/blox/DBS_Model_Blox_Adam_Brown.jl

@@ -105,9 +105,9 @@ struct Striatum_MSN_Adam <: CompositeBlox
             end
         end
         parts = n_inh
-
+        
         bc = connector_from_graph(g)
-    
+
         sys = isnothing(namespace) ? system_from_graph(g, bc; name, simplify=false) : system_from_parts(parts; name)
 
         m = if isnothing(namespace) 
@@ -184,9 +184,9 @@ struct Striatum_FSI_Adam  <: CompositeBlox
         end
 
         parts = n_inh
-
+        
         bc = connector_from_graph(g)
-    
+
         sys = isnothing(namespace) ? system_from_graph(g, bc; name, simplify=false) : system_from_parts(parts; name)
 
         m = if isnothing(namespace) 
@@ -254,9 +254,9 @@ struct GPe_Adam <: CompositeBlox
             end
         end
         parts = n_inh
-
+        
         bc = connector_from_graph(g)
-    
+
         sys = isnothing(namespace) ? system_from_graph(g, bc; name, simplify=false) : system_from_parts(parts; name)
 
         m = if isnothing(namespace) 
@@ -323,9 +323,9 @@ struct STN_Adam <: CompositeBlox
             end
         end
         parts = n_exci
-
-        bc = connector_from_graph(g)
 
+        bc = connector_from_graph(g)
+
         sys = isnothing(namespace) ? system_from_graph(g, bc; name, simplify=false) : system_from_parts(parts; name)
 
         m = if isnothing(namespace) 
@@ -334,6 +334,7 @@ struct STN_Adam <: CompositeBlox
             sys_namespace = System(Equation[], t; name=namespaced_name(namespace, name))
             [s for s in unknowns.((sys_namespace,), unknowns(sys)) if contains(string(s), "V(t)")]
         end
+
         new(namespace, parts, sys, bc, m, connection_matrix)
     end
 

src/blox/blox_utilities.jl

@@ -64,6 +64,7 @@ function get_neurons(vn::AbstractVector{<:AbstractBlox})
 end
 
 get_parts(blox::CompositeBlox) = blox.parts
+get_parts(blox::Union{AbstractBlox, ObserverBlox}) = blox
 
 get_components(blox::CompositeBlox) = mapreduce(x -> get_components(x), vcat, get_parts(blox))
 get_components(blox::Vector{<:AbstractBlox}) = mapreduce(x -> get_components(x), vcat, blox)
@@ -97,14 +98,15 @@ end
 get_namespaced_sys(sys::AbstractODESystem) = sys
 
 nameof(blox) = (nameof ∘ get_system)(blox)
+nameof(blox::AbstractActionSelection) = blox.name
 
 namespaceof(blox) = blox.namespace
 
 namespaced_nameof(blox) = namespaced_name(inner_namespaceof(blox), nameof(blox))
 
 """
     Returns the complete namespace EXCLUDING the outermost (highest) level.
-    This is useful for manually preparing equations (e.g. connections, see BloxConnector),
+    This is useful for manually preparing equations (e.g. connections, see Connector),
     that will later be composed and will automatically get the outermost namespace.
 """ 
 function inner_namespaceof(blox)
@@ -119,7 +121,7 @@ end
 namespaced_name(parent_name, name) = Symbol(parent_name, :₊, name)
 namespaced_name(::Nothing, name) = Symbol(name)
 
-function find_eq(eqs::AbstractVector{<:Equation}, lhs)
+function find_eq(eqs::Union{AbstractVector{<:Equation}, Equation}, lhs)
     findfirst(eqs) do eq
         lhs_vars = get_variables(eq.lhs)
         length(lhs_vars) == 1 && isequal(only(lhs_vars), lhs)
@@ -136,7 +138,7 @@ end
     the higher-level namespaces will be added to them.
 
     If blox isa AbstractComponent, it is assumed that it contains a `connector` field,
-    which holds a `BloxConnector` object with all relevant connections 
+    which holds a `Connector` object with all relevant connections 
     from lower levels and this level.
 """
 function get_input_equations(blox::Union{AbstractBlox, ObserverBlox})
@@ -162,28 +164,28 @@ function get_input_equations(blox::Union{AbstractBlox, ObserverBlox})
 end
 
 get_connector(blox::Union{CompositeBlox, Agent}) = blox.connector
+get_connector(blox) = Connector(namespaced_nameof(blox), namespaced_nameof(blox))
 
-get_input_equations(bc::BloxConnector) = bc.eqs
-get_input_equations(blox::Union{CompositeBlox, AbstractComponent}) = (get_input_equations ∘ get_connector)(blox)
+get_input_equations(bc::Connector) = bc.equation
 get_input_equations(blox) = []
 
-get_weight_parameters(bc::BloxConnector) = bc.weights
+get_weight_parameters(bc::Connector) = bc.weights
 get_weight_parameters(blox::Union{CompositeBlox, AbstractComponent}) = (get_weight_parameters ∘ get_connector)(blox)
 get_weight_parameters(blox) = Num[]
 
-get_delay_parameters(bc::BloxConnector) = bc.delays
+get_delay_parameters(bc::Connector) = bc.delays
 get_delay_parameters(blox::Union{CompositeBlox, AbstractComponent}) = (get_delay_parameters ∘ get_connector)(blox)
 get_delay_parameters(blox) = Num[]
 
-get_discrete_callbacks(bc::BloxConnector) = bc.discrete_callbacks
+get_discrete_callbacks(bc::Connector) = bc.discrete_callbacks
 get_discrete_callbacks(blox::Union{CompositeBlox, AbstractComponent}) = (get_discrete_callbacks ∘ get_connector)(blox)
 get_discrete_callbacks(blox) = []
 
-get_spike_affects(bc::BloxConnector) = bc.spike_affects
+get_spike_affects(bc::Connector) = bc.spike_affects
 get_spike_affects(blox::Union{CompositeBlox, AbstractComponent}) = (get_spike_affects ∘ get_connector)(blox)
 get_spike_affects(blox) = Dict{Symbol, Tuple{Vector{Num}, Vector{Num}}}()
 
-get_weight_learning_rules(bc::BloxConnector) = bc.learning_rules
+get_weight_learning_rules(bc::Connector) = bc.learning_rules
 get_weight_learning_rules(blox::Union{CompositeBlox, AbstractComponent}) = (get_weight_learning_rules ∘ get_connector)(blox)
 get_weight_learning_rules(blox) = Dict{Num, AbstractLearningRule}()
 
@@ -253,6 +255,14 @@ function get_connection_matrix(kwargs, name_out, name_in, N_out, N_in)
     connection_matrix
 end
 
+function get_learning_rule(kwargs, name_src, name_dest)
+    if haskey(kwargs, :learning_rule)
+        return deepcopy(kwargs[:learning_rule])
+    else
+        return NoLearningRule()
+    end
+end
+
 function get_weights(agent::Agent, blox_out, blox_in)
     ps = parameters(agent.odesystem)
     pv = agent.problem.p
@@ -622,3 +632,16 @@ function get_sampling_info(sol::SciMLBase.AbstractSolution; sampling_rate=nothin
         return nothing, 1000 / sampling_rate
     end
 end
+
+function narrowtype_union(d::Dict)
+    types = unique(typeof.(values(d)))
+    U = Union{types...}
+
+    return U
+end
+
+function narrowtype(d::Dict)
+    U = narrowtype_union(d)
+
+    return Dict{Num, U}(d)
+end