refactor: initialise_model -> init_model and initialise_data -> init_…

…data

examples/basic_example.jl

@@ -15,7 +15,7 @@ initial_conditions = Bit.AUSTRIA2010Q1.initial_conditions
 # We can now initialise the model, by specifying in advance the maximum number of epochs.
 
 T = 16
-model = Bit.initialise_model(parameters, initial_conditions, T)
+model = Bit.init_model(parameters, initial_conditions, T)
 
 
 # Note that the it is very simple to inspect the model by typing
@@ -28,7 +28,7 @@ fieldnames(typeof(model.bank))
 
 # We can now define a data tracker, which will store the time series of the model.
 
-data = Bit.initialise_data(model);
+data = Bit.init_data(model);
 
 # We can run now the model for a number of epochs and progressively update the data tracker.
 
@@ -53,11 +53,12 @@ p7 = plot(data.wages, title = "wages", titlefont = 10)
 p8 = plot(data.euribor, title = "euribor", titlefont = 10)
 p9 = plot(data.nominal_gdp ./ data.real_gdp, title = "gdp deflator", titlefont = 10)
 
+
 plot(p1, p2, p3, p4, p5, p6, p7, p8, p9, layout = (3, 3), legend = false)
 
 # To run multiple monte-carlo repetitions in parallel we can use
 
-model = Bit.initialise_model(parameters, initial_conditions, T)
+model = Bit.init_model(parameters, initial_conditions, T)
 data_vector = Bit.run_n_sims(model, 4)
 
 # Note that this will use the number of threads specified when activating the Julia environment.
@@ -96,3 +97,10 @@ p9 = errorline(
     titlefont = 10,
 )
 plot(p1, p2, p3, p4, p5, p6, p7, p8, p9, layout = (3, 3), legend = false)
+
+plot(p1, p4, p5, p3, p8, p9, layout = (3, 2), legend = false, size = (400, 600), dpi = 300, left_margin = 3Plots.mm) 
+
+plot(p1, p4, p5, p3, p8, p9, layout = (2, 3), legend = false, size = (600, 400), dpi = 300)#, left_margin = 3Plots.mm) 
+
+
+savefig("output.png")

examples/benchmark_w_matlab.jl

@@ -11,8 +11,8 @@ T = 12*2
 
 # We will run the model without any output to avoid the overhead of printing the results.
 function run_no_output(;multi_threading = false)
-    model = BeforeIT.initialise_model(parameters, initial_conditions, T)
-    data = BeforeIT.initialise_data(model);
+    model = BeforeIT.init_model(parameters, initial_conditions, T)
+    data = BeforeIT.init_data(model);
 
     for _ in 1:T
         BeforeIT.one_epoch!(model; multi_threading = multi_threading)
@@ -25,7 +25,7 @@ end
 @time run_no_output(;multi_threading = true) 
 
 # time taken by the MATLAB code, computed independently on an Apple M1 chip
-matlab_times = [3.1465, 3.0795, 3.0274, 3.0345, 3.0873]
+matlab_times = [3.1919, 3.2454, 3.1501, 3.1074, 3.1551]
 matlab_time = mean(matlab_times)
 matlab_time_std = std(matlab_times)
 
@@ -50,11 +50,16 @@ julia_time_multi_thread_std = std(julia_times_multi_thread)
 # get the number of threads used
 n_threads = Threads.nthreads()
 
+theme(:default, bg = :white)
 # bar chart of the time taken vs the time taken by the MATLAB code, also plot the stds as error bars
-bar(["MATLAB", "Julia, 1 thread", "Julia, $n_threads threads"], [matlab_time, julia_time_1_thread, julia_time_multi_thread], yerr = [matlab_time_std, julia_time_1_thread_std, julia_time_multi_thread_std], legend = false, dpi=300)
-ylabel!("Time for one simulation (s)")
+# make a white background with no grid
+bar(["MATLAB", "Julia, 1 thread", "Julia, $n_threads threads"], [matlab_time, julia_time_1_thread, julia_time_multi_thread], 
+yerr = [matlab_time_std, julia_time_1_thread_std, julia_time_multi_thread_std],
+legend = false, dpi=300, size=(400, 300), grid = false, ylabel = "Time for one simulation (s)")
 
 # the Julia implementation is faster than the MATLAB implementation, and the multi-threaded version is faster than the single-threaded version.
 
+# increase
+
 # save the image
 savefig("benchmark_w_matlab.png")

examples/change_expectations.jl

@@ -15,7 +15,7 @@ init = Bit.AUSTRIA2010Q1.initial_conditions
 
 Random.seed!(1234)
 T = 40
-model = Bit.initialise_model(par, init, T)
+model = Bit.init_model(par, init, T)
 data = Bit.run_one_sim!(model)
 
 # Now we can experiment with changing expectations of the agents in the model.
@@ -30,7 +30,7 @@ end
 # run the model again, with the same seed
 
 Random.seed!(1234)
-model = Bit.initialise_model(par, init, T)
+model = Bit.init_model(par, init, T)
 data_back = Bit.run_one_sim!(model)
 
 # plot the results, comparing the two cases as different lines

examples/get_simulations.jl

@@ -12,7 +12,7 @@ for year in 2010:2019
         parameters = load("data/italy/parameters/" * string(year) * "Q" * string(quarter) * ".jld2")
         initial_conditions = load("data/italy/initial_conditions/" * string(year) * "Q" * string(quarter) * ".jld2")
         T = 12
-        model = BeforeIT.initialise_model(parameters, initial_conditions, T)
+        model = BeforeIT.init_model(parameters, initial_conditions, T)
         n_sims = 4
         data_vector = BeforeIT.run_n_sims(model, n_sims)
         save("data/italy/simulations/" * string(year) * "Q" * string(quarter) * ".jld2", "data_vector", data_vector)

examples/multithreading_speedup.jl

@@ -13,7 +13,7 @@ using FileIO, Plots, StatsPlots
 parameters = Bit.ITALY2010Q1.parameters
 initial_conditions = Bit.ITALY2010Q1.initial_conditions
 T = 50
-model = Bit.initialise_model(parameters, initial_conditions, T);
+model = Bit.init_model(parameters, initial_conditions, T);
 
 
 # The model is in scale 1:2000, so it has around 30,000 households
@@ -31,7 +31,7 @@ println(Threads.nthreads())
 
 @time data = Bit.run_one_sim!(model; multi_threading = false);
 
-model = Bit.initialise_model(parameters, initial_conditions, T);
+model = Bit.init_model(parameters, initial_conditions, T);
 @time data = Bit.run_one_sim!(model; multi_threading = true);
 
 # Is the speedup in line to what we would expect?

examples/scenario_analysis_via_overload.jl

@@ -12,8 +12,8 @@ initial_conditions = Bit.AUSTRIA2010Q1.initial_conditions
 
 # initialise the model and the data collector
 T = 20
-model = Bit.initialise_model(parameters, initial_conditions, T);
-data = Bit.initialise_data(model);
+model = Bit.init_model(parameters, initial_conditions, T);
+data = Bit.init_data(model);
 
 # Simulate the model for T quarters
 data_vec_baseline = Bit.run_n_sims(model, 4)

examples/scenario_analysis_via_shock.jl

@@ -12,7 +12,7 @@ initial_conditions = Bit.AUSTRIA2010Q1.initial_conditions
 
 # initialise the model and the data collector
 T = 20
-model = Bit.initialise_model(parameters, initial_conditions, T);
+model = Bit.init_model(parameters, initial_conditions, T);
 
 # Simulate the model for T quarters
 data_vec_baseline = Bit.run_n_sims(model, 4)

main.jl

@@ -5,8 +5,8 @@ parameters = BeforeIT.AUSTRIA2010Q1.parameters
 initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
 
 T = 20
-model = BeforeIT.initialise_model(parameters, initial_conditions, T)
-data = BeforeIT.initialise_data(model)
+model = BeforeIT.init_model(parameters, initial_conditions, T)
+data = BeforeIT.init_data(model)
 
 for t in 1:T
     println("Epoch: ", t)

src/model_init/init.jl

@@ -3,7 +3,7 @@ recursive_namedtuple(x::Any) = x
 recursive_namedtuple(d::Dict) = MutableNamedTuple(; Dict(k => recursive_namedtuple(v) for (k, v) in d)...)
 
 """
-    initialise_model(parameters, initial_conditions, T, typeInt = Int64, typeFloat = Float64)
+    init_model(parameters, initial_conditions, T, typeInt = Int64, typeFloat = Float64)
 
 Initializes the model with given parameters and initial conditions.
 
@@ -18,7 +18,7 @@ Returns:
 - model::Model: The initialized model.
 
 """
-function initialise_model(parameters::Dict{String, Any}, initial_conditions::Dict{String, Any}, T, typeInt::DataType = Int64, typeFloat::DataType = Float64)
+function init_model(parameters::Dict{String, Any}, initial_conditions::Dict{String, Any}, T, typeInt::DataType = Int64, typeFloat::DataType = Float64)
 
     # properties
     properties = BeforeIT.init_properties(parameters, T; typeInt = typeInt, typeFloat = typeFloat)

src/one_simulation.jl

@@ -11,7 +11,7 @@ The simulation runs for a number of epochs specified by `model.prop.T`.
 - `data::Data`: The data collected during the simulation.
 
 # Details
-The function initializes the data using `BeforeIT.initialise_data(model)`, then iteratively updates the model and data
+The function initializes the data using `BeforeIT.init_data(model)`, then iteratively updates the model and data
 for each epoch using `BeforeIT.one_epoch!(model)` and `BeforeIT.update_data!(data, model)` respectively.
 
 # Example
@@ -21,7 +21,7 @@ data = run_one_sim!(model)
 """
 function run_one_sim!(model; multi_threading = false, shock = NoShock())
 
-    data = BeforeIT.initialise_data(model)
+    data = BeforeIT.init_data(model)
 
     T = model.prop.T
 

src/utils/data.jl

@@ -32,7 +32,7 @@ end
 """
 Initialise the data arrays
 """
-function initialise_data(m)
+function init_data(m)
     p = m.prop
     T = p.T
     d = Data([zeros(T + 1) for _ in 1:25]..., zeros(T + 1, p.G), zeros(T + 1, p.G))

test/accounting_identities.jl

@@ -8,8 +8,8 @@ using Test
     initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
 
     T = 1
-    model = BeforeIT.initialise_model(parameters, initial_conditions, T)
-    data = BeforeIT.initialise_data(model)
+    model = BeforeIT.init_model(parameters, initial_conditions, T)
+    data = BeforeIT.init_data(model)
 
     for t in 1:T
         BeforeIT.one_epoch!(model; multi_threading = false)

test/deterministic/deterministic_ouput_t1_t5.jl

@@ -4,8 +4,8 @@
     T = 1
     parameters = BeforeIT.AUSTRIA2010Q1.parameters
     initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
-    model = BeforeIT.initialise_model(parameters, initial_conditions, T)
-    data = BeforeIT.initialise_data(model)
+    model = BeforeIT.init_model(parameters, initial_conditions, T)
+    data = BeforeIT.init_data(model)
 
     BeforeIT.one_epoch!(model; multi_threading = false)
     BeforeIT.update_data!(data, model)
@@ -30,8 +30,8 @@
     T = 5
     parameters = BeforeIT.AUSTRIA2010Q1.parameters
     initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
-    model = BeforeIT.initialise_model(parameters, initial_conditions, T)
-    data = BeforeIT.initialise_data(model)
+    model = BeforeIT.init_model(parameters, initial_conditions, T)
+    data = BeforeIT.init_data(model)
     for t in 1:T
         BeforeIT.one_epoch!(model; multi_threading = false)
         BeforeIT.update_data!(data, model)

test/deterministic/initialize_deterministic.jl

@@ -4,7 +4,7 @@
 
     parameters = BeforeIT.AUSTRIA2010Q1.parameters
     initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
-    model = BeforeIT.initialise_model(parameters, initial_conditions, 1)
+    model = BeforeIT.init_model(parameters, initial_conditions, 1)
 
     properties = model.prop
 

test/deterministic/one_epoch_deterministic.jl

@@ -5,7 +5,7 @@
     initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
 
     T = 1
-    model = BeforeIT.initialise_model(parameters, initial_conditions, T;)
+    model = BeforeIT.init_model(parameters, initial_conditions, T;)
 
     gov = model.gov # government
     cb = model.cb # central bank

test/deterministic/one_run_deterministic.jl

@@ -11,16 +11,16 @@
     initial_conditions1 = deepcopy(initial_conditions)
     initial_conditions2 = deepcopy(initial_conditions)
 
-    model = BeforeIT.initialise_model(parameters1, initial_conditions1, T;)
-    data = BeforeIT.initialise_data(model)
+    model = BeforeIT.init_model(parameters1, initial_conditions1, T;)
+    data = BeforeIT.init_data(model)
     for t in 1:(T - 1)
         BeforeIT.one_epoch!(model; multi_threading = false)
         BeforeIT.update_data!(data, model)
     end
 
 
-    model2 = BeforeIT.initialise_model(parameters2, initial_conditions2, T;)
-    data2 = BeforeIT.initialise_data(model2)
+    model2 = BeforeIT.init_model(parameters2, initial_conditions2, T;)
+    data2 = BeforeIT.init_data(model2)
 
     for t in 1:(T - 1)
         BeforeIT.one_epoch!(model2; multi_threading = false)

test/markets/search_and_matching.jl

@@ -10,7 +10,7 @@ using Random
     initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
 
     T = 1
-    model = BeforeIT.initialise_model(parameters, initial_conditions, T;)
+    model = BeforeIT.init_model(parameters, initial_conditions, T;)
 
 
     gov = model.gov # government

test/model_init/initialise_model.jl

@@ -5,7 +5,7 @@ dir = @__DIR__
 
 parameters = BeforeIT.STEADY_STATE2010Q1.parameters
 initial_conditions = BeforeIT.STEADY_STATE2010Q1.initial_conditions
-model = BeforeIT.initialise_model(parameters, initial_conditions, 1)
+model = BeforeIT.init_model(parameters, initial_conditions, 1)
 
 properties = model.prop
 

test/monte_carlo_evaluations.jl

@@ -6,8 +6,8 @@ parameters = matread(joinpath(dir, "../data/austria/parameters/2010Q1.mat"))
 initial_conditions = matread(joinpath(dir, "../data/austria/initial_conditions/2010Q1.mat"))
 
 T = 20
-model = BeforeIT.initialise_model(parameters, initial_conditions, T)
-data = BeforeIT.initialise_data(model)
+model = BeforeIT.init_model(parameters, initial_conditions, T)
+data = BeforeIT.init_data(model)
 
 n_sims = 3
 data_vector = BeforeIT.run_n_sims(model, n_sims)

test/one_epoch_consistency.jl

@@ -9,8 +9,8 @@ parameters = matread(joinpath(dir, "../data/steady_state/parameters/2010Q1.mat")
 initial_conditions = matread(joinpath(dir, "../data/steady_state/initial_conditions/2010Q1.mat"))
 
 T = 1
-model = BeforeIT.initialise_model(parameters, initial_conditions, T;)
-data = BeforeIT.initialise_data(model)
+model = BeforeIT.init_model(parameters, initial_conditions, T;)
+data = BeforeIT.init_data(model)
 
 
 println(BeforeIT.get_accounting_identity_banks(model))

test/steady_state.jl

@@ -5,8 +5,8 @@ parameters = BeforeIT.STEADY_STATE2010Q1.parameters
 initial_conditions = BeforeIT.STEADY_STATE2010Q1.initial_conditions
 
 T = 5
-model = BeforeIT.initialise_model(parameters, initial_conditions, T)
-data = BeforeIT.initialise_data(model)
+model = BeforeIT.init_model(parameters, initial_conditions, T)
+data = BeforeIT.init_data(model)
 
 for t in 1:T
     println(t)

test/utils/estimations.jl

@@ -6,7 +6,7 @@ using MAT, Random
 
     parameters = BeforeIT.AUSTRIA2010Q1.parameters
     initial_conditions = BeforeIT.AUSTRIA2010Q1.initial_conditions
-    model = BeforeIT.initialise_model(parameters, initial_conditions, 1)
+    model = BeforeIT.init_model(parameters, initial_conditions, 1)