nixify notebook
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SHA256:pb5NrYg80So5z9hmqQFPmp//sgr+DFeJkKhmGyU2NLk
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@ -1,30 +1,32 @@
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{
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"nodes": {
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"flake-utils": {
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"flake-parts": {
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"inputs": {
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"systems": "systems"
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"nixpkgs-lib": [
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"nixpkgs"
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]
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},
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"locked": {
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"lastModified": 1687709756,
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"narHash": "sha256-Y5wKlQSkgEK2weWdOu4J3riRd+kV/VCgHsqLNTTWQ/0=",
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"owner": "numtide",
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"repo": "flake-utils",
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"rev": "dbabf0ca0c0c4bce6ea5eaf65af5cb694d2082c7",
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"lastModified": 1725234343,
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"narHash": "sha256-+ebgonl3NbiKD2UD0x4BszCZQ6sTfL4xioaM49o5B3Y=",
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"owner": "hercules-ci",
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"repo": "flake-parts",
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"rev": "567b938d64d4b4112ee253b9274472dc3a346eb6",
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"type": "github"
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},
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"original": {
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"owner": "numtide",
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"repo": "flake-utils",
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"owner": "hercules-ci",
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"repo": "flake-parts",
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"type": "github"
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}
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},
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"nixpkgs": {
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"locked": {
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"lastModified": 1687898314,
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"narHash": "sha256-B4BHon3uMXQw8ZdbwxRK1BmxVOGBV4viipKpGaIlGwk=",
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"lastModified": 1725432240,
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"narHash": "sha256-+yj+xgsfZaErbfYM3T+QvEE2hU7UuE+Jf0fJCJ8uPS0=",
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"owner": "NixOS",
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"repo": "nixpkgs",
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"rev": "e18dc963075ed115afb3e312b64643bf8fd4b474",
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"rev": "ad416d066ca1222956472ab7d0555a6946746a80",
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"type": "github"
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},
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"original": {
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@ -36,8 +38,9 @@
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},
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"root": {
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"inputs": {
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"flake-utils": "flake-utils",
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"nixpkgs": "nixpkgs"
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"flake-parts": "flake-parts",
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"nixpkgs": "nixpkgs",
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"systems": "systems"
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}
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},
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"systems": {
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64
flake.nix
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64
flake.nix
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@ -0,0 +1,64 @@
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{
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inputs = {
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nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable";
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flake-parts = {
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url = "github:hercules-ci/flake-parts";
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inputs.nixpkgs-lib.follows = "nixpkgs";
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};
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systems.url = "github:nix-systems/default";
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};
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outputs = {flake-parts, ...} @ inputs:
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flake-parts.lib.mkFlake {inherit inputs;} {
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systems = import inputs.systems;
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perSystem = {
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pkgs,
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system,
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...
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}: rec {
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devShells.default = pkgs.mkShell {
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packages = packages.notebooks.buildInputs;
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};
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packages.notebooks = pkgs.stdenvNoCC.mkDerivation {
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name = "notebooks";
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src = ./julia;
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dontUnpack = true;
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buildInputs = [
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(pkgs.julia.withPackages [
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"Pluto"
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"Plots"
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"Statistics"
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"PlutoUI"
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"LinearAlgebra"
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"StatsPlots"
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"Distributions"
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"MAT"
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"DSP"
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])
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];
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buildPhase = ''
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# copy the notebooks, Pluto needs write permission
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cp $src/notebook.jl index.jl
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cp $src/donnees.mat donnees.mat
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chmod +w index.jl
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# julia needs permission to create .julia directory
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export HOME=$TMPDIR
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# run and export the notebooks
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julia $src/export_html.jl index.jl
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'';
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installPhase = ''
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mkdir -p $out
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cp index.html $out
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cp -r $src/content $out
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'';
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};
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};
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};
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}
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13
julia/.vscode/settings.json
vendored
13
julia/.vscode/settings.json
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{
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"editor.formatOnSave": true,
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"files.insertFinalNewline": true,
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"editor.trimAutoWhitespace": true,
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"files.trimTrailingWhitespace": true,
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"terminal.integrated.env.linux": {
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"JULIA_PROJECT": "@."
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},
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"[julia]": {
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"editor.tabSize": 2,
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"editor.insertSpaces": true
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}
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}
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42
julia/export_html.jl
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42
julia/export_html.jl
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@ -0,0 +1,42 @@
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using Pluto
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function export_html(notebook_path, html_path)
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# load notebook
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notebook = Pluto.load_notebook(Pluto.tamepath(notebook_path));
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topology = Pluto.updated_topology(notebook.topology, notebook, notebook.cells)
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# create offline workspace
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workspace = Pluto.WorkspaceManager.make_workspace(
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(
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Pluto.ServerSession(),
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notebook,
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),
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is_offline_renderer=true,
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)
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# run all the cells of the notebook
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for cell in notebook.cells
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Pluto.run_single!(
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workspace,
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cell,
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topology.nodes[cell],
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topology.codes[cell],
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)
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end
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# convert notebook outputs to html
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html_contents = Pluto.generate_html(notebook);
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# write to html file
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open(html_path, "w") do html_file
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write(html_file, html_contents);
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end
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end
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# get cli args
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for arg in ARGS
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filename, _ = splitext(arg)
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html_path = filename * ".html"
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println("Exporting $arg to $html_path")
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export_html(arg, filename * ".html")
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end
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@ -1,19 +0,0 @@
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{
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inputs = {
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nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable";
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flake-utils.url = "github:numtide/flake-utils";
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};
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outputs = { self, nixpkgs, flake-utils }:
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flake-utils.lib.eachDefaultSystem (system:
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let pkgs = nixpkgs.legacyPackages.${system};
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in {
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devShell = pkgs.mkShell {
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buildInputs = with pkgs; [
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julia
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ffmpeg-full # https://github.com/JuliaIO/FFMPEG.jl/issues/48#issuecomment-898340527
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patchelf
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];
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};
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});
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}
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@ -435,7 +435,7 @@ On peut montrer que le vecteur de paramètres estimé en moindres carrés est di
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$$\beta \hookrightarrow \mathcal{N} ( \beta^*, \sigma^2 ( A^\intercal A )^{-1})$$
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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))
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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)
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"""
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# ╔═╡ a8843a99-cd5d-47fa-810b-68b56e405af9
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@ -452,7 +452,6 @@ begin
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λ: $(λ_slider)
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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).
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"""
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end
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@ -3953,27 +3952,30 @@ version = "1.4.1+0"
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# ╟─1ae5bc6d-16c4-40cb-846e-3b0e177fd0cf
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# ╟─b3634cc1-fb5f-4b7b-bfa3-08d1fb787c5f
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# ╟─73ed5f0e-4298-4308-bcb8-7c1245ebf0f5
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# ╟─cc938f64-41f9-478b-a136-ae0cc55b5f48
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# ╟─aec0ff6e-ac6a-4104-83c1-f70e254602b9
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# ╟─dbffcc35-ba67-4468-b1b5-1c38a0fd971e
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# ╟─d969e49e-0a0c-4bdf-aba2-835586c87e7f
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# ╟─c0615490-3d43-4c42-9ac6-c4fdd59aae7f
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# ╟─ae8a3f9b-12cf-4802-8ae5-31546d82516d
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# ╠═7960408a-d922-43e2-9a61-4aaf0c9e6a39
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# ╟─7960408a-d922-43e2-9a61-4aaf0c9e6a39
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# ╟─b56c3502-4dd9-4446-acd9-dcb0c667803f
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# ╟─76279a22-3afe-46e1-829f-88f5dddfd55c
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# ╟─cde13c7c-fbc2-4a8d-9b82-7607d71bb4b1
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# ╟─a8843a99-cd5d-47fa-810b-68b56e405af9
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# ╠═c3f3a0c3-8523-43aa-8a8a-589f567399c9
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# ╟─c3f3a0c3-8523-43aa-8a8a-589f567399c9
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# ╟─f60fbae0-a6bd-4791-b38b-9e29ae801a07
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# ╟─b95121b9-4b58-4086-bbd8-ecd2893060dd
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# ╟─3ff18976-beb4-45ad-aa29-d511e5d3ad0a
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# ╟─c563818e-d117-47ce-abeb-5b048edad64e
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# ╟─650ab9f4-581b-4190-8dd0-91c6d4c86b6b
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# ╟─1b88eacd-754b-4b6b-a8e0-dd35ae37f248
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# ╟─acbab47e-2ab5-443a-8d78-f597a4ca08ed
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# ╟─230c1126-6717-4a93-9013-5f0c15616904
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# ╟─c563818e-d117-47ce-abeb-5b048edad64e
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# ╟─650ab9f4-581b-4190-8dd0-91c6d4c86b6b
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# ╟─5974ceb3-1e82-4183-9dca-35ccd6b9a9ba
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# ╟─fac7b990-552e-4353-8264-ba0840a95383
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# ╟─4bcfb185-4499-422d-9439-6ca4e30c4846
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# ╟─174dbb25-12ba-460d-a905-8223f8241964
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# ╟─27a1f0cc-a6bc-417e-9c58-6b63172f753f
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# ╟─c9e530b7-f5f6-4905-8c6c-69ea2440f57d
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# ╟─67a5b4cc-00dd-4f8f-a408-27f1d8c60e7c
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@ -3981,9 +3983,6 @@ version = "1.4.1+0"
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# ╟─8655b8fd-ef08-441f-9186-e2febce6e8f1
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# ╟─e16bb608-d97b-40eb-bf6b-4199ee4d1926
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# ╟─475aecd7-123f-4e4a-b004-90c2a8a951d8
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# ╟─fac7b990-552e-4353-8264-ba0840a95383
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# ╟─4bcfb185-4499-422d-9439-6ca4e30c4846
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# ╟─174dbb25-12ba-460d-a905-8223f8241964
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# ╟─59be55be-5847-4fa1-a4f6-ef8e0658da13
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# ╟─2e002135-2a74-47bb-92a6-520d84e85bf3
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# ╟─764c0716-5dd2-4d3c-9489-1dd92d29d7c7
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