fixdocs (#66)

* other seed * update version info on docs * rm Plots; StatsPlots does it * update get_estimated_variables docstring
thorek1 · Jan 8, 2024 · 4a16a02 · 4a16a02
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Showing 6 changed files with 16 additions and 15 deletions.
diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml
@@ -16,8 +16,8 @@ jobs:
       contents: write
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
-      - uses: julia-actions/setup-julia@latest
+      - uses: actions/checkout@v4
+      - uses: julia-actions/setup-julia@v1
         with:
           version: '1'
       # - run: pip3 install sympy
@@ -31,6 +31,9 @@ jobs:
           DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # If authenticating with SSH deploy key
         #run: julia --project=docs/ docs/make.jl      
         run: julia --project=docs/ --code-coverage=user docs/make.jl
-      - uses: julia-actions/julia-processcoverage@latest
+      - uses: julia-actions/julia-processcoverage@v1
       - uses: codecov/codecov-action@v3
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}
+          file: lcov.info
 
diff --git a/Project.toml b/Project.toml
@@ -72,7 +72,6 @@ MatrixEquations = "^2"
 NLopt = "0.6, ^1"
 Optim = "^1"
 Pigeons = "^0.2"
-Plots = "^1"
 PrecompileTools = "^1"
 REPL = "^1"
 Random = "^1"
@@ -106,11 +105,10 @@ LineSearches = "d3d80556-e9d4-5f37-9878-2ab0fcc64255"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Pigeons = "0eb8d820-af6a-4919-95ae-11206f830c31"
-Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Aqua", "JET", "CSV", "DataFrames", "DynamicPPL", "MCMCChains", "LineSearches", "Optim", "Test", "Turing", "Pigeons", "FiniteDifferences", "Zygote", "Plots", "StatsPlots"]
+test = ["Aqua", "JET", "CSV", "DataFrames", "DynamicPPL", "MCMCChains", "LineSearches", "Optim", "Test", "Turing", "Pigeons", "FiniteDifferences", "Zygote", "StatsPlots"]
diff --git a/docs/src/how-to/loops.md b/docs/src/how-to/loops.md
@@ -52,7 +52,7 @@ Putting these these elements together we can write the multi-country model equat
 ```@setup howto_loops
 ENV["GKSwstype"] = "100"
 using Random
-Random.seed!(30)
+Random.seed!(3)
 ```
 
 ```@repl howto_loops

diff --git a/docs/src/tutorials/estimation.md b/docs/src/tutorials/estimation.md
@@ -145,7 +145,7 @@ StatsPlots.plot(chain_NUTS);
 Next, we are plotting the posterior loglikelihood along two parameters dimensions, with the other parameters ket at the posterior mean, and add the samples to the visualisation. This visualisation allows us to understand the curvature of the posterior and puts the samples in context.
 
 ```@repl tutorial_2
-using ComponentArrays, MCMCChains, DynamicPPL, Plots
+using ComponentArrays, MCMCChains, DynamicPPL
 
 parameter_mean = mean(chain_NUTS)
 pars = ComponentArray(parameter_mean.nt[2],Axis(parameter_mean.nt[1]))

diff --git a/src/MacroModelling.jl b/src/MacroModelling.jl
@@ -6869,7 +6869,7 @@ if VERSION >= v"1.9"
             # get_SSS(FS2000, silent = true)
             # get_SSS(FS2000, algorithm = :third_order, silent = true)
 
-            # import Plots, StatsPlots
+            # import StatsPlots
             # plot_irf(FS2000)
             # plot_solution(FS2000,:k) # fix warning when there is no sensitivity and all values are the same. triggers: no strict ticks found...
             # plot_conditional_variance_decomposition(FS2000)

diff --git a/src/get_functions.jl b/src/get_functions.jl
@@ -220,7 +220,7 @@ end
 
 """
 $(SIGNATURES)
-Return the estimated variables based on the inversion filter (depending on the `filter` keyword argument), or Kalman filter or smoother (depending on the `smooth` keyword argument) using the provided data and (non-)linear solution of the model. Data is by default assumed to be in levels unless `data_in_levels` is set to `false`.
+Return the estimated variables (in levels by default, see `levels` keyword argument) based on the inversion filter (depending on the `filter` keyword argument), or Kalman filter or smoother (depending on the `smooth` keyword argument) using the provided data and (non-)linear solution of the model. Data is by default assumed to be in levels unless `data_in_levels` is set to `false`.
 
 # Arguments
 - $MODEL
@@ -262,11 +262,11 @@ get_estimated_variables(RBC,simulation([:c],:,:simulate))
 ↓   Variables ∈ 4-element Vector{Symbol}
 →   Periods ∈ 40-element UnitRange{Int64}
 And data, 4×40 Matrix{Float64}:
-        (1)            (2)           (3)            (4)           …  (37)           (38)          (39)           (40)
-  (:c)   -0.000640535    0.00358475    0.000455785    0.00490466        0.0496719      0.055509      0.0477877      0.0436101
-  (:k)   -0.00671639     0.0324867     0.00663736     0.0456383         0.500217       0.548478      0.481045       0.437527
-  (:q)    0.00334817     0.0426535    -0.0247438      0.0440383        -0.0114766      0.113775     -0.00867574     0.00971302
-  (:z)    0.000601617    0.00626684   -0.00393712     0.00632712       -0.00771079     0.0112496    -0.00704709    -0.00366442
+        (1)           (2)           (3)           (4)          …  (37)          (38)            (39)           (40)
+  (:c)    5.92901       5.92797       5.92847       5.92048          5.95845       5.95697         5.95686        5.96173
+  (:k)   47.3185       47.3087       47.3125       47.2392          47.6034       47.5969         47.5954        47.6402
+  (:q)    6.87159       6.86452       6.87844       6.79352          7.00476       6.9026          6.90727        6.95841
+  (:z)   -0.00109471   -0.00208056    4.43613e-5   -0.0123318        0.0162992     0.000445065     0.00119089     0.00863586
 ```
 """
 function get_estimated_variables(𝓂::ℳ,