nixify notebook

flake.lock

@@ -1,30 +1,32 @@
 {
   "nodes": {
-    "flake-utils": {
+    "flake-parts": {
       "inputs": {
-        "systems": "systems"
+        "nixpkgs-lib": [
+          "nixpkgs"
+        ]
       },
       "locked": {
-        "lastModified": 1687709756,
+        "lastModified": 1725234343,
-        "narHash": "sha256-Y5wKlQSkgEK2weWdOu4J3riRd+kV/VCgHsqLNTTWQ/0=",
+        "narHash": "sha256-+ebgonl3NbiKD2UD0x4BszCZQ6sTfL4xioaM49o5B3Y=",
-        "owner": "numtide",
+        "owner": "hercules-ci",
-        "repo": "flake-utils",
+        "repo": "flake-parts",
-        "rev": "dbabf0ca0c0c4bce6ea5eaf65af5cb694d2082c7",
+        "rev": "567b938d64d4b4112ee253b9274472dc3a346eb6",
         "type": "github"
       },
       "original": {
-        "owner": "numtide",
+        "owner": "hercules-ci",
-        "repo": "flake-utils",
+        "repo": "flake-parts",
         "type": "github"
       }
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1687898314,
+        "lastModified": 1725432240,
-        "narHash": "sha256-B4BHon3uMXQw8ZdbwxRK1BmxVOGBV4viipKpGaIlGwk=",
+        "narHash": "sha256-+yj+xgsfZaErbfYM3T+QvEE2hU7UuE+Jf0fJCJ8uPS0=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "e18dc963075ed115afb3e312b64643bf8fd4b474",
+        "rev": "ad416d066ca1222956472ab7d0555a6946746a80",
         "type": "github"
       },
       "original": {
@@ -36,8 +38,9 @@
     },
     "root": {
       "inputs": {
-        "flake-utils": "flake-utils",
+        "flake-parts": "flake-parts",
-        "nixpkgs": "nixpkgs"
+        "nixpkgs": "nixpkgs",
+        "systems": "systems"
       }
     },
     "systems": {

flake.nix

@@ -0,0 +1,64 @@
+{
+  inputs = {
+    nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable";
+    flake-parts = {
+      url = "github:hercules-ci/flake-parts";
+      inputs.nixpkgs-lib.follows = "nixpkgs";
+    };
+    systems.url = "github:nix-systems/default";
+  };
+
+  outputs = {flake-parts, ...} @ inputs:
+    flake-parts.lib.mkFlake {inherit inputs;} {
+      systems = import inputs.systems;
+
+      perSystem = {
+        pkgs,
+        system,
+        ...
+      }: rec {
+        devShells.default = pkgs.mkShell {
+          packages = packages.notebooks.buildInputs;
+        };
+
+        packages.notebooks = pkgs.stdenvNoCC.mkDerivation {
+          name = "notebooks";
+
+          src = ./julia;
+          dontUnpack = true;
+          buildInputs = [
+            (pkgs.julia.withPackages [
+              "Pluto"
+              "Plots"
+              "Statistics"
+              "PlutoUI"
+              "LinearAlgebra"
+              "StatsPlots"
+              "Distributions"
+              "MAT"
+              "DSP"
+            ])
+          ];
+
+          buildPhase = ''
+            # copy the notebooks, Pluto needs write permission
+            cp $src/notebook.jl index.jl
+            cp $src/donnees.mat donnees.mat
+            chmod +w index.jl
+
+            # julia needs permission to create .julia directory
+            export HOME=$TMPDIR
+
+            # run and export the notebooks
+            julia $src/export_html.jl index.jl
+          '';
+
+          installPhase = ''
+            mkdir -p $out
+            cp index.html $out
+            cp -r $src/content $out
+          '';
+        };
+      };
+    };
+}

julia/.vscode/settings.json

@@ -1,13 +0,0 @@
-{
-  "editor.formatOnSave": true,
-  "files.insertFinalNewline": true,
-  "editor.trimAutoWhitespace": true,
-  "files.trimTrailingWhitespace": true,
-  "terminal.integrated.env.linux": {
-    "JULIA_PROJECT": "@."
-  },
-  "[julia]": {
-    "editor.tabSize": 2,
-    "editor.insertSpaces": true
-  }
-}

julia/export_html.jl

@@ -0,0 +1,42 @@
+using Pluto
+
+function export_html(notebook_path, html_path)
+    # load notebook
+    notebook = Pluto.load_notebook(Pluto.tamepath(notebook_path));
+    topology = Pluto.updated_topology(notebook.topology, notebook, notebook.cells)
+
+    # create offline workspace
+    workspace = Pluto.WorkspaceManager.make_workspace(
+        (
+            Pluto.ServerSession(),
+            notebook,
+        ),
+        is_offline_renderer=true,
+    )
+
+    # run all the cells of the notebook
+    for cell in notebook.cells
+        Pluto.run_single!(
+            workspace,
+            cell,
+            topology.nodes[cell],
+            topology.codes[cell],
+        )
+    end
+
+    # convert notebook outputs to html
+    html_contents = Pluto.generate_html(notebook);
+
+    # write to html file
+    open(html_path, "w") do html_file
+        write(html_file, html_contents);
+    end
+end
+
+# get cli args
+for arg in ARGS
+    filename, _ = splitext(arg)
+    html_path = filename * ".html"
+    println("Exporting $arg to $html_path")
+    export_html(arg, filename * ".html")
+end

julia/flake.nix

@@ -1,19 +0,0 @@
-{
-  inputs = {
-    nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable";
-    flake-utils.url = "github:numtide/flake-utils";
-  };
-
-  outputs = { self, nixpkgs, flake-utils }:
-    flake-utils.lib.eachDefaultSystem (system:
-      let pkgs = nixpkgs.legacyPackages.${system};
-      in {
-        devShell = pkgs.mkShell {
-          buildInputs = with pkgs; [
-            julia
-            ffmpeg-full # https://github.com/JuliaIO/FFMPEG.jl/issues/48#issuecomment-898340527
-            patchelf
-          ];
-        };
-      });
-}

julia/notebook.jl

@@ -382,7 +382,7 @@ begin
 
 		Leave-one-out : d=$(d_estime_loo), σ=$(round(σ_estime_loo, digits=2))
 		""")]
-	)
+	  )
   )
 end
 
@@ -435,7 +435,7 @@ On peut montrer que le vecteur de paramètres estimé en moindres carrés est di
 
 $$\beta \hookrightarrow \mathcal{N} ( \beta^*, \sigma^2 ( A^\intercal A )^{-1})$$
 
-On trace alors sur la figure suivante, avec un tracé continu la distribution théorique des $\beta$, et via un histogramme la distribution que l'on obtient par estimation aux moindres carrés (sur un grand nombre de points, n=$(n))
+On trace alors sur la figure suivante, avec un tracé continu la distribution théorique des $\beta$, et via un histogramme la distribution que l'on obtient par estimation aux moindres carrés (sur un grand nombre de points, n=1000)
 """
 
 # ╔═╡ a8843a99-cd5d-47fa-810b-68b56e405af9
@@ -452,7 +452,6 @@ begin
   λ: $(λ_slider)
 
   On observe que via les moindres carrés classiques, notre régression a souvent tendance à [overfitter](https://fr.wikipedia.org/wiki/Surapprentissage) les points d'apprentissage. Pour remédier à ce problème, nous pouvons introduire un hyperparamètre $\lambda$ qui nous permettra de pénaliser l'overfitting (par régularisation).
-
   """
 end
 
@@ -3953,27 +3952,30 @@ version = "1.4.1+0"
 # ╟─1ae5bc6d-16c4-40cb-846e-3b0e177fd0cf
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-# ╟─d969e49e-0a0c-4bdf-aba2-835586c87e7f
-# ╟─c0615490-3d43-4c42-9ac6-c4fdd59aae7f
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+# ╟─d969e49e-0a0c-4bdf-aba2-835586c87e7f
+# ╟─c0615490-3d43-4c42-9ac6-c4fdd59aae7f
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-# ╠═7960408a-d922-43e2-9a61-4aaf0c9e6a39
+# ╟─7960408a-d922-43e2-9a61-4aaf0c9e6a39
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-# ╠═c3f3a0c3-8523-43aa-8a8a-589f567399c9
+# ╟─c3f3a0c3-8523-43aa-8a8a-589f567399c9
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+# ╟─c563818e-d117-47ce-abeb-5b048edad64e
+# ╟─650ab9f4-581b-4190-8dd0-91c6d4c86b6b
 # ╟─1b88eacd-754b-4b6b-a8e0-dd35ae37f248
 # ╟─acbab47e-2ab5-443a-8d78-f597a4ca08ed
 # ╟─230c1126-6717-4a93-9013-5f0c15616904
-# ╟─c563818e-d117-47ce-abeb-5b048edad64e
-# ╟─650ab9f4-581b-4190-8dd0-91c6d4c86b6b
 # ╟─5974ceb3-1e82-4183-9dca-35ccd6b9a9ba
+# ╟─fac7b990-552e-4353-8264-ba0840a95383
+# ╟─4bcfb185-4499-422d-9439-6ca4e30c4846
+# ╟─174dbb25-12ba-460d-a905-8223f8241964
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@@ -3981,9 +3983,6 @@ version = "1.4.1+0"
 # ╟─8655b8fd-ef08-441f-9186-e2febce6e8f1
 # ╟─e16bb608-d97b-40eb-bf6b-4199ee4d1926
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-# ╟─fac7b990-552e-4353-8264-ba0840a95383
-# ╟─4bcfb185-4499-422d-9439-6ca4e30c4846
-# ╟─174dbb25-12ba-460d-a905-8223f8241964
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