Rename all variables and files with model -> simulation

Author: jondea

README.md

@@ -37,25 +37,25 @@ particles = [p1,p2]
 Specify the angular frequency of the defualt incident plane wave ![incident plane wave](https://latex.codecogs.com/gif.latex?%5Cdpi%7B120%7D%20e%5E%7Bi%20%28k%20x%20-%20%5Comega%20t%29%7D) and calculate the response
 ```julia
 w_arr = collect(0.1:0.01:1.)
-model = FrequencySimulation(particles, w_arr; source_direction = [1.0,0.0])
+simulation = FrequencySimulation(particles, w_arr; source_direction = [1.0,0.0])
 ```
 
 ### Plot
 The package also provides recipes to be used with the `Plots` package for
-plotting models after they have been run.
-In our above model we ran the simulation for 100 different wavenumbers, and
+plotting simulations after they have been run.
+In our above simulation we ran the simulation for 100 different wavenumbers, and
 measured the response at the default location (-10.,0.).
 We can plot the time-harmonic response across these wavenumbers by typing:
 ```julia
 using Plots
 pyplot()
-plot(model)
+plot(simulation)
 ```
-![Plot of response against wavenumber](example/intro/plot_model.png)
+![Plot of response against wavenumber](example/intro/plot_simulation.png)
 
 For a better overview you can plot the whole field for a specific k by typing:
 ```julia
-plot(model,0.8)
+plot(simulation,0.8)
 ```
 ![Plot real part of acoustic field](example/intro/plot_field.png)
 
@@ -64,7 +64,7 @@ This way we can get an understanding of what is happening for one particular
 wavenumber.
 
 Note: most things in the package can be plotted by typing `plot(thing)` if you
-need an insight into a specific part of your model.
+need an insight into a specific part of your simulation.
 
 ## More examples
 There are a lot of defaults implicit in this basic example.

example/box_size/box_size.jl

@@ -10,26 +10,26 @@ function box_size_convergence(m=4, volfrac = 0.05,
     seed = MersenneTwister(1).seed
     allparticles = random_particles(volfrac, radius, bigshape; seed = seed)
 
-    models = map(times) do t
+    simulations = map(times) do t
         println("Calculating response with particles at a maximum of $t away")
         shape = TimeOfFlight(listener_position,t)
         particles = filter(p->inside(shape,p),allparticles)
         return FrequencySimulation(particles, k_arr; seed=seed, hankel_order=m)
     end
 
-    return map(m->TimeSimulation(m;time_arr=linspace(0.0,100.0,201)),models)
+    return map(m->TimeSimulation(m;time_arr=linspace(0.0,100.0,201)),simulations)
 end
 
-function plot_box_size_convergence(models;times = [20,30,40,50,60,70])
+function plot_box_size_convergence(simulations;times = [20,30,40,50,60,70])
 
     colors = linspace(RGB(0.6,1,0.6),RGB(0,0.4,0),length(times))
 
     plot(xlab="Time (t)",ylab="Response")
-    for m in eachindex(models)
-        plot!(models[m],color=colors[m],label="Box cut at t=$(times[m])")
+    for s in eachindex(simulations)
+        plot!(simulations[s],color=colors[s],label="Box cut at t=$(times[s])")
     end
     plot!(title="Time response from random particles from increasingly large boxes")
 end
 
-models = box_size_convergence()
-plot_box_size_convergence(models)
+simulations = box_size_convergence()
+plot_box_size_convergence(simulations)

example/convergence/convergence.jl

@@ -10,41 +10,41 @@ function hankel_order_convergence(m=[0,1,2,3,4,5,6,7,8,9,10], volfrac = 0.1,
     seed = MersenneTwister(1).seed
     particles = random_particles(volfrac, radius, shape; seed = seed)
 
-    models = Vector{FrequencySimulation{Float64}}(length(m))
+    simulations = Vector{FrequencySimulation{Float64}}(length(m))
 
     for i = eachindex(m)
-        models[i] = FrequencySimulation(particles, k_arr; seed=seed, hankel_order=m[i])
+        simulations[i] = FrequencySimulation(particles, k_arr; seed=seed, hankel_order=m[i])
     end
 
-    return models
+    return simulations
 end
 
-function plot_hankel_order_convergence(models)
-    responses = Vector{Vector{Complex{Float64}}}(length(models))
-    m = Vector{Int64}(length(models))
+function plot_hankel_order_convergence(simulations)
+    responses = Vector{Vector{Complex{Float64}}}(length(simulations))
+    m = Vector{Int64}(length(simulations))
 
     labels = Matrix{String}(1,0)
-    for i = eachindex(models)
-        responses[i] = reshape(models[i].response, size(models[i].response,1))
-        m[i] = models[i].hankel_order
+    for i = eachindex(simulations)
+        responses[i] = reshape(simulations[i].response, size(simulations[i].response,1))
+        m[i] = simulations[i].hankel_order
         labels = [labels "m = $(m[i])"]
     end
 
     error = [r .- responses[end] for r in responses[1:end-1]]
-    integrated_error = norm.(error).*map(m->((m.k_arr[end]-m.k_arr[1])/length(m.k_arr)),models[1:end-1])
+    integrated_error = norm.(error).*map(m->((m.k_arr[end]-m.k_arr[1])/length(m.k_arr)),simulations[1:end-1])
 
     colors = reshape(linspace(RGB(0.6,1,0.6),RGB(0,0.4,0),length(m)),1,length(m))
     realcolors = reshape(linspace(RGB(0.6,0.6,1),RGB(0,0,0.4),length(m)),1,length(m))
     imagcolors = reshape(linspace(RGB(1,0.6,0.6),RGB(0.4,0,0),length(m)),1,length(m))
 
     absvec(v) = abs.(v)
     plot(
-        plot(models[end],0.5),
-        plot(models[1].k_arr, [real(responses),imag(responses)],
+        plot(simulations[end],0.5),
+        plot(simulations[1].k_arr, [real(responses),imag(responses)],
              labels=[ map(c -> "real "*c,labels)  map(c -> "imag "*c,labels)],
              xlab="Wavenumber (k)", ylab="Response", linecolor=[realcolors imagcolors]
         ),
-        plot(models[1].k_arr, absvec.(error),
+        plot(simulations[1].k_arr, absvec.(error),
              yscale=:log10, labels=labels, linecolor=colors,
              xlab="Wavenumber (k)", ylab="Absolute error",
         ),
@@ -56,5 +56,5 @@ function plot_hankel_order_convergence(models)
 
 end
 
-models = hankel_order_convergence()
-plot_hankel_order_convergence(models)
+simulations = hankel_order_convergence()
+plot_hankel_order_convergence(simulations)

example/intro/README.md

@@ -10,25 +10,25 @@ particles = [p1,p2]
 Specify the angular frequency of the defualt incident plane wave ![incident plane wave](https://latex.codecogs.com/gif.latex?%5Cdpi%7B120%7D%20e%5E%7Bi%20%28k%20x%20-%20%5Comega%20t%29%7D) and calculate the response
 ```julia
 w_arr = collect(0.1:0.01:1.)
-model = FrequencySimulation(particles, w_arr; source_direction = [1.0,0.0])
+simulation = FrequencySimulation(particles, w_arr; source_direction = [1.0,0.0])
 ```
 
 ## Plot
 The package also provides recipes to be used with the `Plots` package for
-plotting models after they have been run.
-In our above model we ran the simulation for 100 different wavenumbers, and
+plotting simulations after they have been run.
+In our above simulation we ran the simulation for 100 different wavenumbers, and
 measured the response at the default location (-10.,0.).
 We can plot the time-harmonic response across these wavenumbers by typing:
 ```julia
 using Plots
 pyplot()
-plot(model)
+plot(simulation)
 ```
-![Plot of response against wavenumber](plot_model.png)
+![Plot of response against wavenumber](plot_simulation.png)
 
 For a better overview you can plot the whole field for a specific k by typing:
 ```julia
-plot(model,0.8)
+plot(simulation,0.8)
 ```
 ![Plot real part of acoustic field](plot_field.png)
 
@@ -37,4 +37,4 @@ This way we can get an understanding of what is happening for one particular
 wavenumber.
 
 Note: most things in the package can be plotted by typing `plot(thing)` if you
-need an insight into a specific part of your model.
+need an insight into a specific part of your simulation.

example/intro/intro.jl

@@ -5,13 +5,13 @@ p2 = Particle([-2.,-2.])
 particles = [p1,p2]
 
 w_arr = collect(0.1:0.01:1.)
-model = FrequencySimulation(particles, w_arr)
+simulation = FrequencySimulation(particles, w_arr)
 
 using Plots
 pyplot()
-plot(model)
+plot(simulation)
 
-savefig("plot_model.png")
+savefig("plot_simulation.png")
 
-plot(model,0.8)
+plot(simulation,0.8)
 savefig("plot_field.png")

example/intro/plot_model.png renamed to example/intro/plot_simulation.png

@@ -19,16 +19,16 @@ function run_lens(;
     innershape = TimeOfFlight(listener_position,innertime)
     particles = filter(p -> !inside(innershape,p), outerparticles)
 
-    freqmodel = FrequencySimulation(particles, k_arr; listener_positions=listener_position)
+    freqsimulation = FrequencySimulation(particles, k_arr; listener_positions=listener_position)
 
-    return freqmodel, TimeSimulation(freqmodel; impulse = get_gaussian_freq_impulse(1.0, 3./maximum(k_arr)^2))
+    return freqsimulation, TimeSimulation(freqsimulation; impulse = get_gaussian_freq_impulse(1.0, 3./maximum(k_arr)^2))
 end
 
 function plot_lens()
-    freqmodel, TimeSimulation = run_lens()
+    freqsimulation, TimeSimulation = run_lens()
 
     plot(
-        plot(freqmodel.particles),
+        plot(freqsimulation.particles),
         plot(TimeSimulation)
     )
 end

example/lens/lens.jl

@@ -9,12 +9,12 @@ function moments_example()
     radius = 1.0
     k_arr = collect(linspace(0.01,1.0,100))
 
-    # Holder for our models
-    models = Vector{FrequencySimulation{Float64}}(10)
+    # Holder for our simulations
+    simulations = Vector{FrequencySimulation{Float64}}(10)
     for i=1:10
-        models[i] = FrequencySimulation(volfrac,radius,k_arr;seed=[0x7f5def91, 0x82255da3, 0xc5e461c7, UInt32(i)])
+        simulations[i] = FrequencySimulation(volfrac,radius,k_arr;seed=[0x7f5def91, 0x82255da3, 0xc5e461c7, UInt32(i)])
     end
 
-    moments = StatisticalMoments(models)
+    moments = StatisticalMoments(simulations)
     return moments
 end

example/moments/moments.jl

@@ -16,20 +16,20 @@ which wavenumbers (k) to evaluate at
 volfrac = 0.01
 radius = 1.0
 k_arr = collect(linspace(0.01,1.0,100))
-model = FrequencySimulation(volfrac,radius,k_arr)
+simulation = FrequencySimulation(volfrac,radius,k_arr)
 ```
 
 We use the `Plots` package to plot both the response at the listener position
 and the whole field for a specific wavenumber (k=0.8)
 ```julia
 using Plots
 plot(
-    plot(model),
-    plot(model,0.8;res=100)
+    plot(simulation),
+    plot(simulation,0.8;res=100)
 )
 ```
 
-![Plot of response against wavenumber](plot_model.png)
+![Plot of response against wavenumber](plot_simulation.png)
 
 ![Plot real part of acoustic field](plot_field.png)