Simplify CellState

Project.toml

@@ -1,12 +1,11 @@
 name = "CellularPotts"
 uuid = "11b7dee7-cd95-48a5-a457-717aae94d0bd"
 authors = ["Robert <[email protected]> and contributors"]
-version = "0.3.4"
+version = "0.3.5"
 
 [deps]
 ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
-DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -17,22 +16,17 @@ Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [compat]
 ColorSchemes = "3"
 Colors = "0.12"
-DataFrames = "1"
 Documenter = "1"
 Graphs = "1"
 Literate = "2"
 Metis = "1"
 OffsetArrays = "1"
 Plots = "1"
 PrettyTables = "2"
-StatsBase = "0.34"
-Tables = "1"
 julia = "1"
 
 [extras]

docs/src/ExampleGallery/BringingODEsToLife/BringingODEsToLife.jl

@@ -12,14 +12,12 @@ space = CellSpace(200,200)
 
 # In the CellState we specify one epithelial cell with a volume of 200 pixels
 # The cell will be positioned at the halfway point within the space. 
-initialCellState = CellState(:Epithelial, 200, 1, positions = size(space) .÷ 2);
-
-# From the DifferentialEquations example, a theoretical protein X was created for each cell that increases linearly in time with rate parameter α
-const α = 0.3;
-
-# ProteinX needs an initial condition which we set to 0.2. Note that for :Medium (grid locations without a cell) we give a concentration of zero. 
-u0 = [0.0, 0.2]
-initialCellState = addcellproperty(initialCellState, :ProteinX, u0)
+state = CellState(
+    names = :Epithelial,
+    volumes = 200, 
+    counts = 1, 
+    positions = size(space) .÷ 2
+    );
 
 # Keeping the model simple, we'll only include an Adhesion and Volume penalty
 penalties = [
@@ -29,12 +27,15 @@ penalties = [
     ]
 
 # Now that we have all the pieces, we can generate a new CPM model.
-cpm = CellPotts(space, initialCellState, penalties);
+cpm = CellPotts(space, state, penalties);
+
+# By default, CellularPotts models to not record states as they change overtime to increase computional speed. To have the model record past states we can toggle the appropriate keyword.
+cpm.recordHistory = true;
 
 # ## DifferentialEquations.jl setup
 
-# Currently by default CellularPotts models to not record states as they change overtime to increase computional speed. To have the model record past states we can toggle the appropriate keyword.
-cpm.recordHistory = true;
+# From the DifferentialEquations example, a theoretical protein X was created for each cell that increases linearly in time with rate parameter α
+const α = 0.3;
 
 # As ProteinX evolves over time for each cell, the CPM model also needs to step forward in time to try and minimize its energy. To facilitate this, we can use the callback feature from DifferentialEquations.jl. Here specifically we use the `PeriodicCallback` function which will stop the ODE solve at regular time intervals and run some other function for us (Here it will be the `ModelStep!` function). 
 
@@ -69,18 +70,19 @@ function affect!(integrator,cpm)
     u[end] = 1-Θ
 
     #Adding a call to divide the cells in the CPM
-    CellDivision!(cpm, cellID-1)
+    CellDivision!(cpm, cellID)
     return nothing
 end
 
 # This will instantiate the ContinuousCallback triggering cell division
 ccb = ContinuousCallback(condition,integrator -> affect!(integrator, cpm));
 
-# To pass multiple callback into DifferentialEquations we need to collect them into a set.
+# To pass multiple callbacks into DifferentialEquations, we need to collect them into a set.
 callbacks = CallbackSet(pcb, ccb);
 
 
 # Define the ODE model and solve
+u0 = [0.2]
 tspan = (0.0,20.0)
 prob = ODEProblem(f,u0,tspan)
 sol = solve(prob, Tsit5(), callback=callbacks);
@@ -91,12 +93,13 @@ sol = solve(prob, Tsit5(), callback=callbacks);
 using Plots, Printf
 
 # Plot the total cell count over time
-plot(sol.t,map((x)->length(x),sol[:]),lw=3,
-     ylabel="Number of Cells",xlabel="Time",legend=nothing)
+plot(sol.t, map((x)->length(x),sol[:]), lw=3,
+     ylabel="Number of Cells", xlabel="Time", legend=nothing)
 
 # Plot ProteinX dynamics for a specific cell
-ts = range(0, stop=20, length=100)
-plot(ts,map((x)->x[2],sol.(ts)),lw=3, ylabel="Amount of X in Cell 1",xlabel="Time",legend=nothing)
+ts = range(0, stop=20, length=1000)
+plot(ts, first.(sol.(ts)), lw=3,
+    ylabel="Amount of X in Cell 1", xlabel="Time", legend=nothing)
 
 # Finally, we can create an animation of the CPM to see the cells dividing. I've dropped the first few frames because the first cell takes a while to divide.
 proteinXConc = zeros(size(space)...)
@@ -105,11 +108,11 @@ anim = @animate for t in Iterators.drop(1:cpm.step.counter,5*timeScale)
     currTime = @sprintf "Time: %.2f" t/timeScale
 
     cpmt = cpm(t)
-    currSol = sol((t+1)/timeScale )
+    currSol = sol((t+1)/timeScale)
 
-    #Map protein concentrations to space
-    for i in CartesianIndices(proteinXConc)
-        proteinXConc[i] = currSol[cpmt.space.nodeIDs[i]+1]
+    #Map protein concentrations to space, set :Medium to zero
+    for (i,cellID) in enumerate(cpmt.space.nodeIDs)
+        proteinXConc[i] = iszero(cellID) ? 0.0 : currSol[cellID]
     end
 
     plt = heatmap(