Updates to sequential wealth on julia 1.8.3 update (#204)

jlperla · web-flow · commit ed2475c74f69 · 2023-01-03T12:21:05.000-08:00
* Updates to CI and loop vectorization for wealth_dynamics

* Modernized the unpacking notation

* Fix for julia 1.8.3
diff --git a/.github/workflows/cache.yml b/.github/workflows/cache.yml
@@ -30,7 +30,7 @@ jobs:
       - name: Set up Julia
         uses: julia-actions/setup-julia@v1
         with:
-          version: 1.8
+          version: 1.8.3
       - name: Install IJulia and Setup Project
         shell: bash
         run: |
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -27,7 +27,7 @@ jobs:
       - name: Set up Julia
         uses: julia-actions/setup-julia@v1
         with:
-          version: 1.8
+          version: 1.8.3
       - name: Install IJulia and Setup Project
         shell: bash
         run: |
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
@@ -31,7 +31,7 @@ jobs:
       - name: Set up Julia
         uses: julia-actions/setup-julia@v1
         with:
-          version: 1.8
+          version: 1.8.3
       - name: Install IJulia and Setup Project
         shell: bash
         run: |
diff --git a/lectures/Manifest.toml b/lectures/Manifest.toml
diff --git a/lectures/Project.toml b/lectures/Project.toml
@@ -1,9 +1,8 @@
 name = "quantecon-notebooks-julia"
 authors = ["quantecon <jesseperla@gmail.com>"]
-version = "0.8.0"
+version = "0.8.1"
 
 [deps]
-ApproxFun = "28f2ccd6-bb30-5033-b560-165f7b14dc2f"
 Arpack = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97"
 BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
@@ -30,6 +29,7 @@ LaTeXStrings = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
 LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearMaps = "7a12625a-238d-50fd-b39a-03d52299707e"
+LoopVectorization = "bdcacae8-1622-11e9-2a5c-532679323890"
 NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
 NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
@@ -53,5 +53,4 @@ StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
 StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-VegaLite = "112f6efa-9a02-5b7d-90c0-432ed331239a"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
diff --git a/lectures/continuous_time/covid_sde.md b/lectures/continuous_time/covid_sde.md
@@ -231,7 +231,7 @@ First, construct our $F$ from {eq}`dfcvsde` and $G$ from {eq}`dG`
 ```{code-cell} julia
 function F(x, p, t)
     s, i, r, d, R₀, δ = x
-    @unpack γ, R̄₀, η, σ, ξ, θ, δ_bar = p
+    (;γ, R̄₀, η, σ, ξ, θ, δ_bar) = p
 
     return [-γ*R₀*s*i;        # ds/dt
             γ*R₀*s*i - γ*i;   # di/dt
@@ -244,7 +244,7 @@ end
 
 function G(x, p, t)
     s, i, r, d, R₀, δ = x
-    @unpack γ, R̄₀, η, σ, ξ, θ, δ_bar = p
+    (;γ, R̄₀, η, σ, ξ, θ, δ_bar) = p
 
     return [0; 0; 0; 0; σ*sqrt(R₀); ξ*sqrt(δ * (1-δ))]
 end
@@ -536,7 +536,7 @@ We will redo the "Ending Lockdown" simulation from above, where the only differe
 ```{code-cell} julia
 function F_reinfect(x, p, t)
     s, i, r, d, R₀, δ = x
-    @unpack γ, R̄₀, η, σ, ξ, θ, δ_bar, ν = p
+    (;γ, R̄₀, η, σ, ξ, θ, δ_bar, ν) = p
 
     return [-γ*R₀*s*i + ν*r;  # ds/dt
             γ*R₀*s*i - γ*i;   # di/dt
diff --git a/lectures/continuous_time/seir_model.md b/lectures/continuous_time/seir_model.md
@@ -290,7 +290,7 @@ First, construct our $F$ from {eq}`dfcv`
 ```{code-cell} julia
 function F(x, p, t)
     s, e, i, r, R₀, c, d = x
-    @unpack σ, γ, R̄₀, η, δ = p
+    (;σ, γ, R̄₀, η, δ) = p
 
     return [-γ*R₀*s*i;        # ds/dt
             γ*R₀*s*i -  σ*e;  # de/dt
diff --git a/lectures/dynamic_programming/coleman_policy_iter.md b/lectures/dynamic_programming/coleman_policy_iter.md
@@ -513,7 +513,7 @@ end
 ```{code-cell} julia
 function verify_true_policy(m, shocks, c_star)
     # compute (Kc_star)
-    @unpack grid, β, ∂u∂c, f, f′ = m
+    (;grid, β, ∂u∂c, f, f′) = m
     c_star_new = K(c_star, grid, β, ∂u∂c, f, f′, shocks)
 
     # plot c_star and Kc_star
@@ -549,7 +549,7 @@ The initial condition we'll use is the one that eats the whole pie: $c(y) = y$
 
 ```{code-cell} julia
 function check_convergence(m, shocks, c_star, g_init; n_iter = 15)
-    @unpack grid, β, ∂u∂c, f, f′ = m
+    (;grid, β, ∂u∂c, f, f′) = m
     g = g_init;
     plot(m.grid, g, lw = 2, alpha = 0.6, label = L"intial condition $c(y) = y$")
     for i in 1:n_iter
@@ -595,7 +595,7 @@ function iterate_updating(func, arg_init; sim_length = 20)
 end
 
 function compare_error(m, shocks, g_init, w_init; sim_length = 20)
-    @unpack grid, β, u, ∂u∂c, f, f′ = m
+    (;grid, β, u, ∂u∂c, f, f′) = m
     g, w = g_init, w_init
 
     # two functions for simplification
@@ -742,7 +742,7 @@ m_ex = Model(γ = 1.5);
 ```{code-cell} julia
 function exercise2(m, shocks, g_init = m.grid, w_init = m.u.(m.grid); sim_length = 20)
 
-    @unpack grid, β, u, ∂u∂c, f, f′ = m
+    (;grid, β, u, ∂u∂c, f, f′) = m
     # initial policy and value
     g, w = g_init, w_init
     # iteration
@@ -773,12 +773,12 @@ It assumes that you've just run the code from the previous exercise
 
 ```{code-cell} julia
 function bellman(m, shocks)
-    @unpack grid, β, u, ∂u∂c, f, f′ = m
+    (;grid, β, u, ∂u∂c, f, f′) = m
     bellman_single_arg(w) = T(w, grid, β, u, f, shocks)
     iterate_updating(bellman_single_arg, u.(grid), sim_length = 20)
 end
 function coleman(m, shocks)
-    @unpack grid, β, ∂u∂c, f, f′ = m
+    (;grid, β, ∂u∂c, f, f′) = m
     coleman_single_arg(g) = K(g, grid, β, ∂u∂c, f, f′, shocks)
     iterate_updating(coleman_single_arg, grid, sim_length = 20)
 end
diff --git a/lectures/dynamic_programming/discrete_dp.md b/lectures/dynamic_programming/discrete_dp.md
@@ -435,7 +435,7 @@ using BenchmarkTools, Plots, QuantEcon, Parameters
 SimpleOG = @with_kw (B = 10, M = 5, α = 0.5, β = 0.9)
 
 function transition_matrices(g)
-    @unpack B, M, α, β = g
+    (;B, M, α, β) = g
     u(c) = c^α
     n = B + M + 1
     m = M + 1
@@ -468,7 +468,7 @@ In case the preceding code was too concise, we can see a more verbose form
 tags: [output_scroll]
 ---
 function verbose_matrices(g)
-    @unpack B, M, α, β = g
+    (;B, M, α, β) = g
     u(c) = c^α
 
     #Matrix dimensions. The +1 is due to the 0 state.
diff --git a/lectures/dynamic_programming/ifp.md b/lectures/dynamic_programming/ifp.md
@@ -403,7 +403,7 @@ end
 function T!(cp, V, out; ret_policy = false)
 
     # unpack input, set up arrays
-    @unpack R, Π, β, b, asset_grid, z_vals = cp
+    (;R, Π, β, b, asset_grid, z_vals) = cp
     z_idx = 1:length(z_vals)
 
     # value function when the shock index is z_i
@@ -444,7 +444,7 @@ get_greedy(cp, V) =
 
 function K!(cp, c, out)
     # simplify names, set up arrays
-    @unpack R, Π, β, b, asset_grid, z_vals = cp
+    (;R, Π, β, b, asset_grid, z_vals) = cp
     z_idx = 1:length(z_vals)
     gam = R * β
 
@@ -474,7 +474,7 @@ K(cp, c) = K!(cp, c, similar(c))
 
 function initialize(cp)
     # simplify names, set up arrays
-    @unpack R, β, b, asset_grid, z_vals = cp
+    (;R, β, b, asset_grid, z_vals) = cp
     shape = length(asset_grid), length(z_vals)
     V, c = zeros(shape...), zeros(shape...)
 
@@ -743,7 +743,7 @@ end
 
 ```{code-cell} julia
 function compute_asset_series(cp, T = 500_000; verbose = false)
-    @unpack Π, z_vals, R = cp  # simplify names
+    (;Π, z_vals, R) = cp  # simplify names
     z_idx = 1:length(z_vals)
     v_init, c_init = initialize(cp)
     c = compute_fixed_point(x -> K(cp, x), c_init,
diff --git a/lectures/dynamic_programming/jv.md b/lectures/dynamic_programming/jv.md
@@ -218,7 +218,7 @@ function T!(jv,
                            new_V::AbstractVector)
 
     # simplify notation
-    @unpack G, π_func, F, β, E, ϵ = jv
+    (;G, π_func, F, β, E, ϵ) = jv
 
     # prepare interpoland of value function
     Vf = LinearInterpolation(jv.x_grid, V, extrapolation_bc=Line())
@@ -259,7 +259,7 @@ function T!(jv,
                            out::Tuple{AbstractVector, AbstractVector})
 
     # simplify notation
-    @unpack G, π_func, F, β, E, ϵ = jv
+    (;G, π_func, F, β, E, ϵ) = jv
 
     # prepare interpoland of value function
     Vf = LinearInterpolation(jv.x_grid, V, extrapolation_bc=Line())
@@ -476,7 +476,7 @@ Here's code to produce the 45 degree diagram
 ```{code-cell} julia
 wp = JvWorker(grid_size=25)
 # simplify notation
-@unpack G, π_func, F = wp
+(;G, π_func, F) = wp
 
 v_init = collect(wp.x_grid) * 0.5
 f2(x) = T(wp, x)
diff --git a/lectures/dynamic_programming/mccall_model.md b/lectures/dynamic_programming/mccall_model.md
@@ -369,7 +369,7 @@ between successive iterates is below tol
 ```{code-cell} julia
 function compute_reservation_wage_direct(params; v_iv = collect(w ./(1-β)), max_iter = 500,
                                          tol = 1e-6)
-    @unpack c, β, w = params
+    (;c, β, w) = params
 
     # create a closure for the T operator
     T(v) = max.(w/(1 - β), c + β * E*v) # (5) fixing the parameter values
@@ -408,7 +408,7 @@ In this case, we can use the `fixedpoint` algorithm discussed in {doc}`our Julia
 ```{code-cell} julia
 function compute_reservation_wage(params; v_iv = collect(w ./(1-β)), iterations = 500,
                                   ftol = 1e-6, m = 6)
-    @unpack c, β, w = params
+    (;c, β, w) = params
     T(v) = max.(w/(1 - β), c + β * E*v) # (5) fixing the parameter values
 
     v_star = fixedpoint(T, v_iv, iterations = iterations, ftol = ftol,
diff --git a/lectures/dynamic_programming/mccall_model_with_separation.md b/lectures/dynamic_programming/mccall_model_with_separation.md
@@ -237,8 +237,7 @@ using Distributions, LinearAlgebra, Expectations, Parameters, NLsolve, Plots
 
 function solve_mccall_model(mcm; U_iv = 1.0, V_iv = ones(length(mcm.w)), tol = 1e-5,
                             iter = 2_000)
-    # α, β, σ, c, γ, w = mcm.α, mcm.β, mcm.σ, mcm.c, mcm.γ, mcm.w
-    @unpack α, β, σ, c, γ, w, dist, u = mcm
+    (; α, β, σ, c, γ, w, dist, u) = mcm
 
     # parameter validation
     @assert c > 0.0
@@ -303,7 +302,7 @@ McCallModel = @with_kw (α = 0.2,
 
 
 mcm = McCallModel()
-@unpack V, U = solve_mccall_model(mcm)
+(; V, U) = solve_mccall_model(mcm)
 U_vec = fill(U, length(mcm.w))
 
 plot(mcm.w, [V U_vec], lw = 2, α = 0.7, label = [L"V" L"U"])
diff --git a/lectures/dynamic_programming/odu.md b/lectures/dynamic_programming/odu.md
@@ -248,7 +248,7 @@ end
 function T!(sp, v, out;
                            ret_policy = false)
     # simplify names
-    @unpack f, g, β, c = sp
+    (;f, g, β, c) = sp
     nodes, weights = sp.quad_nodes, sp.quad_weights
 
     vf = extrapolate(interpolate((sp.w_grid, sp.π_grid), v,
@@ -290,7 +290,7 @@ get_greedy(sp, v) = T(sp, v, ret_policy = true)
 
 function res_wage_operator!(sp, ϕ, out)
     # simplify name
-    @unpack f, g, β, c = sp
+    (;f, g, β, c) = sp
 
     # Construct interpolator over π_grid, given ϕ
     ϕ_f = LinearInterpolation(sp.π_grid, ϕ, extrapolation_bc = Line())
diff --git a/lectures/dynamic_programming/optgrowth.md b/lectures/dynamic_programming/optgrowth.md
@@ -499,7 +499,7 @@ Here's a function that implements the Bellman operator using linear interpolatio
 
 ```{code-cell} julia
 function T(w;p, tol = 1e-10)
-    @unpack β, u, f, ξ, y = p # unpack parameters
+    (;β, u, f, ξ, y) = p # unpack parameters
     w_func = LinearInterpolation(y, w)
 
     Tw = similar(w)
@@ -573,7 +573,7 @@ OptimalGrowthModel = @with_kw (α = 0.4, β = 0.96, μ = 0.0, s = 0.1,
 
 # True value and policy function
 function v_star(y;p)
-    @unpack α, μ, β = p
+    (;α, μ, β) = p
     c1 = log(1 - α * β) / (1 - β)
     c2 = (μ + α * log(α * β)) / (1 - α)
     c3 = 1 / (1 - β)
diff --git a/lectures/dynamic_programming/smoothing.md b/lectures/dynamic_programming/smoothing.md
@@ -369,7 +369,7 @@ ConsumptionProblem = @with_kw (β = 0.96,
 
 function consumption_complete(cp)
 
-    @unpack β, P, y, b0 = cp   # Unpack
+    (;β, P, y, b0) = cp
 
     y1, y2 = y                              # extract income levels
     b1 = b0                                 # b1 is known to be equal to b0
@@ -386,7 +386,7 @@ end
 
 function consumption_incomplete(cp; N_simul = 150)
 
-    @unpack β, P, y, b0 = cp  # unpack
+    (;β, P, y, b0) = cp
 
     # for the simulation use the MarkovChain type
     mc = MarkovChain(P)
diff --git a/lectures/dynamic_programming/wald_friedman.md b/lectures/dynamic_programming/wald_friedman.md
@@ -470,7 +470,7 @@ We can simulate an agent facing a problem and the outcome with the following fun
 
 ```{code-cell} julia
 function simulation(problem)
-    @unpack d0, d1, L0, L1, c, p, n, return_output = problem
+    (;d0, d1, L0, L1, c, p, n, return_output) = problem
     α, β = decision_rule(d0, d1, L0, L1, c)
     outcomes = fill(false, n)
     costs = fill(0.0, n)
@@ -516,7 +516,7 @@ tags: [remove-cell]
 ---
 @testset "Verifying Output" begin
     Random.seed!(0)
-    @unpack d0, d1, L0, L1, c = Problem()
+    (;d0, d1, L0, L1, c) = Problem()
     α, β, outcomes, costs, trials = simulation(Problem(return_output = true))
     #test α ≈ 0.57428237
     #test β ≈ 0.352510338
@@ -543,7 +543,7 @@ tags: [remove-cell]
 ---
 @testset "Comparative Statics" begin
     Random.seed!(0)
-    @unpack d0, d1, L0, L1, c = Problem()
+    (;d0, d1, L0, L1, c) = Problem()
     α, β, outcomes, costs, trials = simulation(Problem(c = 2c, return_output = true))
     #test α ≈ 0.53551172 atol = 1e-3
     #test β ≈ 0.41244737 atol = 1e-3
diff --git a/lectures/getting_started_julia/fundamental_types.md b/lectures/getting_started_julia/fundamental_types.md
@@ -952,7 +952,7 @@ As well as **named tuples**, which extend tuples with names for each argument.
 ```{code-cell} julia
 t = (val1 = 1.0, val2 = "test")
 t.val1      # access by index
-# a, b = t  # bad style, better to unpack by name with @unpack
+# a, b = t  # bad style, better to unpack by name
 println("val1 = $(t.val1) and val1 = $(t.val1)") # access by name
 ```
 
@@ -975,21 +975,19 @@ parameters = (α = 0.1, β = 0.2)
 f(parameters)
 ```
 
-This functionality is aided by the `Parameters.jl` package and the `@unpack` macro
+This functionality is aided by the unpacking notation
 
 ```{code-cell} julia
-using Parameters
-
 function f(parameters)
-    @unpack α, β = parameters  # good style, less sensitive to errors
+    (;α, β) = parameters  # good style, less sensitive to errors
     return α + β
 end
 
 parameters = (α = 0.1, β = 0.2)
 f(parameters)
 ```
 
-In order to manage default values, use the `@with_kw` macro
+In order to manage default values, use the `@with_kw` from the `Parameters.jl` package
 
 ```{code-cell} julia
 using Parameters
diff --git a/lectures/getting_started_julia/getting_started.md b/lectures/getting_started_julia/getting_started.md
@@ -91,6 +91,10 @@ After Conda is installed, you can install Julia.
 The semi-official installation method for Windows is to use [Juliaup](https://github.com/JuliaLang/juliaup), which makes it easier to upgrade and manage concurrent Julia versions.  Support on Mac and Linux is prerelease.  See [here](https://github.com/JuliaLang/juliaup#using-juliaup) for a list of commands, such as `juliaup update` to upgrade to the latest available Julia version after installation, or ways to switch to newer Julia versions after they are released.
 ```
 
+```{warning}
+Juila 1.8.4, which is currently the most recent release, has broken plotting on the Windows operating system.  Until the bug is fixed or 1.8.5 is released, you should use Julia 1.8.3.  With `juliaup` this is straightforward, as `juliaup add 1.8.3` will install that version, and then `juliaup default 1.8.3` will set it as the default.  If you have additional issues, see the docs for fully removing the 1.8.4 version
+```
+
 1. Download and install Julia following the [Juliaup instructions](https://github.com/JuliaLang/juliaup#installation) or manually installing from [download page](http://julialang.org/downloads/), accepting all default options.  That is,
     - Easiest method may be executing `winget install julia -s msstore` in a Windows terminal, or
     - `curl -fsSL https://install.julialang.org | sh` on a Mac terminal.
diff --git a/lectures/getting_started_julia/introduction_to_types.md b/lectures/getting_started_julia/introduction_to_types.md
@@ -488,7 +488,6 @@ remedied with the `@kwdef` macro from `Base`.
 
 ```{code-cell} julia
 using Base: @kwdef
-using Parameters
 
 @kwdef struct Foo5
     a::Float64 = 2.0     # adds default value
@@ -503,7 +502,7 @@ foo2 = Foo5(c = [1.0, 2.0, 3.0], b = 2)  # rearrange order, uses default values
 @show foo2
 
 function f(x)
-    @unpack a, b, c = x     # can use `@unpack` on any struct
+    (;a, b, c) = x # unpacks any struct or named tuple
     return a + b + sum(c)
 end
 
diff --git a/lectures/introduction_dynamics/wealth_dynamics.md b/lectures/introduction_dynamics/wealth_dynamics.md
diff --git a/lectures/more_julia/data_statistical_packages.md b/lectures/more_julia/data_statistical_packages.md
diff --git a/lectures/multi_agent_models/aiyagari.md b/lectures/multi_agent_models/aiyagari.md
diff --git a/lectures/multi_agent_models/lake_model.md b/lectures/multi_agent_models/lake_model.md
diff --git a/lectures/multi_agent_models/lucas_model.md b/lectures/multi_agent_models/lucas_model.md
diff --git a/lectures/multi_agent_models/markov_asset.md b/lectures/multi_agent_models/markov_asset.md
diff --git a/lectures/tools_and_techniques/iterative_methods_sparsity.md b/lectures/tools_and_techniques/iterative_methods_sparsity.md
diff --git a/lectures/tools_and_techniques/numerical_linear_algebra.md b/lectures/tools_and_techniques/numerical_linear_algebra.md
