update constructors in examples

examples/Chemotaxis/1_linear_ramp.jl

@@ -84,7 +84,7 @@ for i in 1:n
     p0 = spacesize(model) ./ 2
     delta = rand(abmrng(model), SVector{2}) .* 10
     pos = p0 .+ delta # random scatter around center
-    motility = RunTumble(0.67, [30.0], Isotropic(2), 0.1)
+    motility = RunTumble([30.0], 0.67, Isotropic(2); tumble_duration=0.1)
     add_agent!(pos, model; motility)
 end
 model

examples/Chemotaxis/2_celani_gauss2D.jl

@@ -32,7 +32,7 @@ properties = Dict(
 )
 
 model = StandardABM(Celani{2,2}, space, timestep; properties)
-motility = RunTumble(0.67, [30.0], Isotropic(2), 0.1)
+motility = RunTumble([30.0], 0.67, Isotropic(2); tumble_duration=0.1)
 
 for _ in 1:300
     add_agent!(model; motility,

examples/Chemotaxis/3_xie_response-function.jl

@@ -68,8 +68,8 @@ To keep the microbes in these motile states for the entire experiment duration,
 we suppress their tumbles, and (just for total consistency with experiments)
 we also set their speed to 0.
 =#
-add_agent!(model; motility=RunReverseFlick(Inf, [0], 0.0, [0]))
-add_agent!(model; motility=RunReverseFlick(0.0, [0], Inf, [0]))
+add_agent!(model; motility=RunReverseFlick([0], Inf, [0], 0.0))
+add_agent!(model; motility=RunReverseFlick([0], Inf, [0], 0.0))
 model[2].motility.current_state = 3 # manually set to backward run state
 
 #=

examples/Chemotaxis/4_response_functions.jl

@@ -57,11 +57,11 @@ properties = Dict(
 
 model = StandardABM(Union{BrownBerg{3},Celani{3}}, space, dt; properties)
 
-add_agent!(BrownBerg{3}, model; motility=RunTumble(1.0, [0], Isotropic(3)),
+add_agent!(BrownBerg{3}, model; motility=RunTumble([0], 1.0, Isotropic(3)),
     memory=1,
 )
-add_agent!(Celani{3}, model; motility=RunTumble(1.0, [0], Isotropic(3)), gain=4)
-add_agent!(Celani{3}, model; motility=RunTumble(1.0, [0], Isotropic(3)), gain=4,
+add_agent!(Celani{3}, model; motility=RunTumble([0], 1.0, Isotropic(3)), gain=4)
+add_agent!(Celani{3}, model; motility=RunTumble([0], 1.0, Isotropic(3)), gain=4,
     chemotactic_precision=50.0
 )
 

examples/Chemotaxis/5_drift_exponential.jl

@@ -76,7 +76,7 @@ n = 200
 for Π in Πs
     for i in 1:n
         pos = SVector{3}(0.0, rand()*Ly, rand()*Lz)
-        motility = RunReverseFlick(0.45, [46.5], 0.45, [46.5])
+        motility = RunReverseFlick([46.5], 0.45, [46.5], 0.45)
         add_agent!(pos, model; chemotactic_precision=Π, motility)
     end
 end

examples/RandomWalks/1_randomwalk1D.jl

@@ -34,22 +34,23 @@ from position `(0,)`.
 
 Moreover, it's always required to specify the motility of the microbes via
 the keyword argument `motility`.
-For example, `RunTumble` needs as inputs the average duration of runs,
-the distribution of run speeds and the distribution of reorientation angles.
+For example, `RunTumble` needs as inputs the distribution of run speed,
+the average run duration and the distribution of reorientation angles.
 The latter is irrelevant in 1D, since the bacterium can only revert its
 direction along the line, but we still have to pass it.
 We can just use an empty vector.
 Passing a one-element vector `[U]` as the speed distribution
 means that all runs will have the same velocity `U`.
 We could have also specified the average duration of tumbles
-(equal to reversals in 1 dimension) via an optional 4th argument (try it! e.g. 0.5);
+(equal to reversals in 1 dimension) via a keyword argument
+`tumble_duration`.
 when unspecified, the tumbles are taken to be instantaneous.
 =#
 
 n = 10 # number of microbes to add
 τ_run = 1.0 # average run duration in s
 U = 30.0 # swimming speed in μm/s
-motility = RunTumble(τ_run, [U], [], 0.5)
+motility = RunTumble([U], τ_run, []; tumble_duration=0.2)
 foreach(_ -> add_agent!((0,), model; motility), 1:n)
 
 #=

examples/RandomWalks/2_randomwalk2D_motilepatterns.jl

@@ -21,11 +21,11 @@ The motile pattern can be customized through the `motility` keyword.
 
 The `RunTumble` motility consists of straight runs interspersed with
 reorientations (tumbles).
-With the first argument, we set the average duration of tumbles, 1 s in this case.
-We can then define the speed to follow a a `Normal` distribution
+With the first argument, we set the speed to follow a a `Normal` distribution
 (from Distributions.jl) with mean 30 μm/s and standard deviation 6 μm/s.
 This means that, after every tumble, the microbe will change its speed following
 this distribution.
+With the second argument, we set the average duration of tumbles, 1 s in this case.
 We should then specify the distribution of angular reorientations.
 For convenience, MicrobeAgents implements an `Isotropic` function
 which produces the appropriate distribution to have isotropic reorientations
@@ -34,7 +34,7 @@ In this case, using `Isotropic(2)` produces a `Uniform` distribution
 of angles between -π and +π.
 Finally, we can set tumbles to also have a finite duration, let's say 0.1 s.
 =#
-add_agent!(model; motility=RunTumble(1.0, Normal(30,6), Isotropic(2), 0.1))
+add_agent!(model; motility=RunTumble(Normal(30,6), 1.0, Isotropic(2); tumble_duration=0.1))
 
 #=
 The `RunReverse` motility consists of alternating straight runs and 180-degree reversals.
@@ -48,7 +48,7 @@ Further, we set the `rotational_diffusivity` of the microbe to 0.2 rad²/s; in a
 rotational diffusion, the run reverse motility is pathologically incapable of
 exploring space efficiently.
 =#
-add_agent!(model; motility=RunReverse(1.0, [30.0], 0.7, [20.0]), rotational_diffusivity=0.2)
+add_agent!(model; motility=RunReverse([30.0], 1.0, [20.0], 0.7), rotational_diffusivity=0.2)
 
 #=
 The `RunReverseFlick` motility consists of a straight run, a 180-degree reversal, then another
@@ -58,7 +58,7 @@ a `RunReverse` where the reorientation after the "backward" run is 90 instead of
 Again, we will set only run durations and speeds.
 We also set the rotational diffusivity to 0.1 rad²/s.
 =#
-add_agent!(model; motility=RunReverseFlick(2.0, [25.0], 0.5, [25.0]), rotational_diffusivity=0.1)
+add_agent!(model; motility=RunReverseFlick([25.0], 2.0, [25.0], 0.5), rotational_diffusivity=0.1)
 
 #=
 Now we can run (collecting the microbe positions at each timestep), unfold the trajectories,

examples/RandomWalks/3_randomwalk3D.jl

@@ -13,9 +13,9 @@ space = ContinuousSpace((L,L,L))
 dt = 0.1
 model = StandardABM(Microbe{3}, space, dt)
 
-add_agent!(model; motility=RunReverse(1.0, [55], 1.0, [55]), rotational_diffusivity=0.2)
-add_agent!(model; motility=RunTumble(2.0, Normal(30,6), Isotropic(3)))
-add_agent!(model; motility=RunReverseFlick(1.0, [30], 1.0, [6]), rotational_diffusivity=0.1)
+add_agent!(model; motility=RunReverse([55], 1.0, [55], 1.0), rotational_diffusivity=0.2)
+add_agent!(model; motility=RunTumble(Normal(30,6), 2.0, Isotropic(3)))
+add_agent!(model; motility=RunReverseFlick([30], 1.0, [6], 1.0), rotational_diffusivity=0.1)
 
 nsteps = 600
 adata = [position]

examples/Validation/msd_runtumble.jl

@@ -36,7 +36,7 @@ U = 30.0 # μm/s
 models = map(_ -> StandardABM(Microbe{3}, space, dt; container=Vector), θs)
 nmicrobes = 100
 for (i,θ) in enumerate(θs)
-    motility = RunTumble(τ, [U], [θ,-θ])
+    motility = RunTumble([U], τ, [θ,-θ])
     foreach(_ -> add_agent!(models[i]; motility), 1:nmicrobes)
 end
 

examples/Validation/velocity_autocorrelations.jl

@@ -32,9 +32,9 @@ model = StandardABM(Microbe{3}, space, Δt; container=Vector)
 n = 200
 for Mot in (RunTumble, RunReverse, RunReverseFlick), i in 1:n
     if Mot == RunTumble
-        motility = RunTumble(τ_run, [U], Isotropic(3), 0.0)
+        motility = RunTumble([U], τ_run, Isotropic(3))
     else
-        motility = Mot(τ_run, [U], τ_run, [U])
+        motility = Mot([U], τ_run, [U], τ_run)
     end
     add_agent!(model; motility)
 end