From 892f09962548b65cb271f60fef6ff9a3d6ddad29 Mon Sep 17 00:00:00 2001 From: a-mhamdi Date: Sat, 5 Oct 2024 18:03:23 +0100 Subject: [PATCH] update manifest --- Codes/Julia/Part-1/Project.toml | 4 + Codes/Julia/Part-1/julia-onramp.ipynb | 3935 ++++++++++++------------- 2 files changed, 1882 insertions(+), 2057 deletions(-) diff --git a/Codes/Julia/Part-1/Project.toml b/Codes/Julia/Part-1/Project.toml index 52ae770..b969eed 100644 --- a/Codes/Julia/Part-1/Project.toml +++ b/Codes/Julia/Part-1/Project.toml @@ -1,6 +1,10 @@ [deps] +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" Fuzzy = "a9166f1b-85e5-4df0-9c26-e06b441f12e8" +IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" ImageInTerminal = "d8c32880-2388-543b-8c61-d9f865259254" +Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca" diff --git a/Codes/Julia/Part-1/julia-onramp.ipynb b/Codes/Julia/Part-1/julia-onramp.ipynb index ac8918a..ffa17eb 100644 --- a/Codes/Julia/Part-1/julia-onramp.ipynb +++ b/Codes/Julia/Part-1/julia-onramp.ipynb @@ -75,14 +75,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m\u001b[1m Activating\u001b[22m\u001b[39m project at `~/MEGA/git-repos/infodev/Codes`\n" + "\u001b[32m\u001b[1m Activating\u001b[22m\u001b[39m project at `~/Work/git-repos/AI-ML-DL/jlai/Codes/Julia/Part-1`\n" ] } ], @@ -93,31 +93,20 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32m\u001b[1mStatus\u001b[22m\u001b[39m `~/MEGA/git-repos/infodev/Codes/Project.toml`\n", - " \u001b[90m[336ed68f] \u001b[39mCSV v0.10.12\n", - " \u001b[90m[8f4d0f93] \u001b[39mConda v1.10.0\n", - " \u001b[90m[a93c6f00] \u001b[39mDataFrames v1.6.1\n", - " \u001b[90m[e9467ef8] \u001b[39mGLMakie v0.9.5\n", - "\u001b[32m⌃\u001b[39m \u001b[90m[a59fdf5c] \u001b[39mGenieFramework v1.26.10\n", - " \u001b[90m[cd3eb016] \u001b[39mHTTP v1.10.1\n", - " \u001b[90m[7073ff75] \u001b[39mIJulia v1.24.2\n", - " \u001b[90m[824d6782] \u001b[39mJSServe v2.3.1\n", - " \u001b[90m[ee78f7c6] \u001b[39mMakie v0.20.4\n", - " \u001b[90m[5fb14364] \u001b[39mOhMyREPL v0.5.23\n", - " \u001b[90m[a03496cd] \u001b[39mPlotlyBase v0.8.19\n", - " \u001b[90m[91a5bcdd] \u001b[39mPlots v1.39.0\n", - " \u001b[90m[c3e4b0f8] \u001b[39mPluto v0.19.36\n", - " \u001b[90m[438e738f] \u001b[39mPyCall v1.96.4\n", - " \u001b[90m[276b4fcb] \u001b[39mWGLMakie v0.9.4\n", - " \u001b[90m[d6f4376e] \u001b[39mMarkdown\n", - "\u001b[36m\u001b[1mInfo\u001b[22m\u001b[39m Packages marked with \u001b[32m⌃\u001b[39m have new versions available and may be upgradable.\n" + "\u001b[32m\u001b[1mStatus\u001b[22m\u001b[39m `~/Work/git-repos/AI-ML-DL/jlai/Codes/Julia/Part-1/Project.toml`\n", + " \u001b[90m[587475ba] \u001b[39mFlux v0.14.21\n", + " \u001b[90m[a9166f1b] \u001b[39mFuzzy v0.3.1\n", + " \u001b[90m[7073ff75] \u001b[39mIJulia v1.25.0\n", + " \u001b[90m[d8c32880] \u001b[39mImageInTerminal v0.5.2\n", + " \u001b[90m[91a5bcdd] \u001b[39mPlots v1.40.8\n", + " \u001b[90m[92933f4c] \u001b[39mProgressMeter v1.10.2\n" ] } ], @@ -134,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "tags": [] }, @@ -143,188 +132,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at `~/.julia/registries/General.toml`\n", "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", - "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/MEGA/git-repos/infodev/Codes/Project.toml`\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/MEGA/git-repos/infodev/Codes/Manifest.toml`\n", - " \u001b[90m[b27032c2] \u001b[39m\u001b[95m↓ LibCURL v0.6.4 ⇒ v0.6.3\u001b[39m\n", - " \u001b[90m[44cfe95a] \u001b[39m\u001b[95m↓ Pkg v1.10.0 ⇒ v1.9.2\u001b[39m\n", - " \u001b[90m[2f01184e] \u001b[39m\u001b[93m~ SparseArrays v1.10.0 ⇒ \u001b[39m\n", - " \u001b[90m[10745b16] \u001b[39m\u001b[95m↓ Statistics v1.10.0 ⇒ v1.9.0\u001b[39m\n", - " \u001b[90m[e66e0078] \u001b[39m\u001b[95m↓ CompilerSupportLibraries_jll v1.0.5+1 ⇒ v1.0.5+0\u001b[39m\n", - " \u001b[90m[781609d7] \u001b[39m\u001b[95m↓ GMP_jll v6.2.1+6 ⇒ v6.2.1+2\u001b[39m\n", - 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"\u001b[32m ✓ \u001b[39mPluto\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mPolynomials → PolynomialsChainRulesCoreExt\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mPolynomials → PolynomialsFFTWExt\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mSimplePolynomials\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mMathTeXEngine\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mGenie\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mLinearAlgebraX\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mGeniePackageManager\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mGenieSession\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mGenieAutoReload\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mGenieSessionFileSession\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mSimpleGraphs\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mStipple\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mDelaunayTriangulation\u001b[39m\n", - "\u001b[32m ✓ \u001b[39mDataFrames\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mStipple → StippleJSONExt\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mLatexify → DataFramesExt\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mStipple → StippleOffsetArraysExt\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mStipple → StippleDataFramesExt\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mGenieDevTools\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mStipplePlotly\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mStipplePlotly → StipplePlotlyPlotlyBaseExt\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mStippleUI\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mStippleUI → StippleUIDataFramesExt\u001b[39m\n", - "\u001b[32m ✓ \u001b[39mGenieFramework\n", - "\u001b[32m ✓ \u001b[39mPlots\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mPlots → IJuliaExt\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mPlots → UnitfulExt\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mPlots → FileIOExt\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mPlots → GeometryBasicsExt\u001b[39m\n", - "\u001b[32m ✓ \u001b[39mMakie\n", - "\u001b[32m ✓ \u001b[39mWGLMakie\n", - "\u001b[32m ✓ \u001b[39mGLMakie\n", - " 157 dependencies successfully precompiled in 421 seconds. 244 already precompiled.\n" + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/Work/git-repos/AI-ML-DL/jlai/Codes/Julia/Part-1/Project.toml`\n", + " \u001b[90m[d6f4376e] \u001b[39m\u001b[92m+ Markdown\u001b[39m\n", + "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/Work/git-repos/AI-ML-DL/jlai/Codes/Julia/Part-1/Manifest.toml`\n" ] } ], @@ -341,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -350,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -367,7 +178,7 @@ " anything. Make other things \u001b[1mbold\u001b[22m" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -413,14 +224,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/mhamdi/MEGA/git-repos/infodev/Codes\n" + "/home/mhamdi/Work/git-repos/AI-ML-DL/jlai/Codes/Julia/Part-1\n" ] } ], @@ -430,32 +241,24 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "total 420\n", - "drwxrwxr-x 6 mhamdi mhamdi 4096 Feb 19 16:30 .\n", - "drwxrwxr-x 8 mhamdi mhamdi 4096 Jan 4 01:01 ..\n", - "-rw-rw-r-- 1 mhamdi mhamdi 2334 Jan 8 18:13 animate-sine.jl\n", - "-rw-rw-r-- 1 mhamdi mhamdi 193921 Jan 8 18:15 🎈 animate-sine.jl — Pluto.png\n", - "-rw-rw-r-- 1 mhamdi mhamdi 817 Jan 11 01:09 anim-graphs.jl\n", - "drwxrwxr-x 2 mhamdi mhamdi 4096 Feb 19 15:32 .ipynb_checkpoints\n", - "-rw------- 2 mhamdi mhamdi 46807 Feb 19 16:30 julia-onramp.ipynb\n", - "-rw------- 1 mhamdi mhamdi 34286 Jan 14 2023 Julia.png\n", - "drwxrwxr-x 2 mhamdi mhamdi 4096 Jan 3 00:00 log\n", - "-rw-rw-r-- 1 mhamdi mhamdi 85079 Feb 19 16:25 Manifest.toml\n", - "-rw-rw-r-- 1 mhamdi mhamdi 162 Jan 3 13:32 precompile.jl\n", - "-rw-rw-r-- 1 mhamdi mhamdi 790 Jan 11 00:53 Project.toml\n", - "-rw-rw-r-- 1 mhamdi mhamdi 20 Nov 19 16:27 README.md\n", - "drwxrwxr-x 2 mhamdi mhamdi 4096 Jan 10 16:04 ros2\n", - "-rw-rw-r-- 1 mhamdi mhamdi 66 Nov 29 21:36 test-file.csv\n", - "-rw-rw-r-- 1 mhamdi mhamdi 7535 Feb 19 15:34 Untitled.ipynb\n", - "-rw------- 1 mhamdi mhamdi 18 Feb 19 16:25 .wakatime-project\n", - "drwxrwxr-x 3 mhamdi mhamdi 4096 Jan 6 19:57 web-app\n" + "total 724\n", + "drwx------ 3 mhamdi mhamdi 4096 Oct 5 16:29 .\n", + "drwx------ 7 mhamdi mhamdi 4096 Apr 20 01:25 ..\n", + "drwxrwxr-x 2 mhamdi mhamdi 4096 Oct 5 16:27 .ipynb_checkpoints\n", + "-rw------- 1 mhamdi mhamdi 646953 Oct 5 16:29 julia-onramp.ipynb\n", + "-rw-rw-r-- 1 mhamdi mhamdi 61142 Oct 5 16:31 Manifest.toml\n", + "-rw-rw-r-- 1 mhamdi mhamdi 357 Oct 5 16:31 Project.toml\n", + "-rw------- 1 mhamdi mhamdi 3203 Jan 22 2023 selection-process.jl\n", + "-rw------- 1 mhamdi mhamdi 1693 Jan 22 2023 tipper.jl\n", + "-rw-r--r-- 1 mhamdi mhamdi 18 Oct 5 15:38 .wakatime-project\n", + "-rw------- 1 mhamdi mhamdi 2122 Jul 4 2023 xor-gate.jl\n" ] } ], @@ -473,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -557,7 +360,7 @@ " compute the cosine. Otherwise, the cosine is determined by calling \u001b[36mexp\u001b[39m.\n", "\n", "\u001b[1m Examples\u001b[22m\n", - "\u001b[1m ≡≡≡≡≡≡≡≡≡≡\u001b[22m\n", + "\u001b[1m ≡≡≡≡≡≡≡≡\u001b[22m\n", "\n", " \u001b[31;1mjulia> \u001b[0m\u001b[38;2;102;217;239mcos\u001b[0m(\u001b[0m\u001b[38;2;102;217;239mfill\u001b[0m(\u001b[0m\u001b[38;2;174;129;255m1.0\u001b[0m\u001b[39m,\u001b[0m \u001b[0m(\u001b[0m\u001b[38;2;174;129;255m2\u001b[0m\u001b[39m,\u001b[0m\u001b[38;2;174;129;255m2\u001b[0m\u001b[39m)\u001b[0m\u001b[39m)\u001b[0m\u001b[39m)\u001b[0m\n", " \u001b[0m2×2 Matrix{Float64}:\n", @@ -565,7 +368,7 @@ " -0.708073 0.291927\n" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -583,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -602,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -628,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "tags": [] }, @@ -642,7 +445,7 @@ " \"Unicycle\" => 1" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -658,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "tags": [] }, @@ -669,7 +472,7 @@ "Dict{String, Int64}" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -680,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "tags": [] }, @@ -694,7 +497,7 @@ " \"Unicycle\" => 1" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -705,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "tags": [] }, @@ -716,7 +519,7 @@ "2" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -727,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "tags": [] }, @@ -743,7 +546,7 @@ " ComplexF64[0.0 + 0.0im, 0.0 + 0.5im]" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -754,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "tags": [] }, @@ -765,7 +568,7 @@ "Vector{Any}\u001b[90m (alias for \u001b[39m\u001b[90mArray{Any, 1}\u001b[39m\u001b[90m)\u001b[39m" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -776,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "tags": [] }, @@ -789,7 +592,7 @@ " 0.0 + 0.5im" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -807,7 +610,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -816,7 +619,7 @@ "(1, 1.5)" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -827,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -846,7 +649,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -862,7 +665,7 @@ " \u001b[36mvarinfo\u001b[39m method allows to display loaded variables." ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -875,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -923,7 +726,7 @@ " showall 0 bytes showall (generic function with 1 method)" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -934,7 +737,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -949,7 +752,7 @@ "data": { "text/latex": [ "\\begin{verbatim}\n", - "varinfo(m::Module=Main, pattern::Regex=r\"\"; all::Bool = false, imported::Bool = false, sortby::Symbol = :name, minsize::Int = 0)\n", + "varinfo(m::Module=Main, pattern::Regex=r\"\"; all=false, imported=false, recursive=false, sortby::Symbol=:name, minsize::Int=0)\n", "\\end{verbatim}\n", "Return a markdown table giving information about exported global variables in a module, optionally restricted to those matching \\texttt{pattern}.\n", "\n", @@ -970,11 +773,13 @@ "\n", "\\item \\texttt{minsize} : only includes objects with size at least \\texttt{minsize} bytes. Defaults to \\texttt{0}.\n", "\n", - "\\end{itemize}\n" + "\\end{itemize}\n", + "The output of \\texttt{varinfo} is intended for display purposes only. See also \\href{@ref}{\\texttt{names}} to get an array of symbols defined in a module, which is suitable for more general manipulations.\n", + "\n" ], "text/markdown": [ "```\n", - "varinfo(m::Module=Main, pattern::Regex=r\"\"; all::Bool = false, imported::Bool = false, sortby::Symbol = :name, minsize::Int = 0)\n", + "varinfo(m::Module=Main, pattern::Regex=r\"\"; all=false, imported=false, recursive=false, sortby::Symbol=:name, minsize::Int=0)\n", "```\n", "\n", "Return a markdown table giving information about exported global variables in a module, optionally restricted to those matching `pattern`.\n", @@ -985,10 +790,12 @@ " * `imported` : also list objects explicitly imported from other modules.\n", " * `recursive` : recursively include objects in sub-modules, observing the same settings in each.\n", " * `sortby` : the column to sort results by. Options are `:name` (default), `:size`, and `:summary`.\n", - " * `minsize` : only includes objects with size at least `minsize` bytes. Defaults to `0`.\n" + " * `minsize` : only includes objects with size at least `minsize` bytes. Defaults to `0`.\n", + "\n", + "The output of `varinfo` is intended for display purposes only. See also [`names`](@ref) to get an array of symbols defined in a module, which is suitable for more general manipulations.\n" ], "text/plain": [ - " \u001b[38;2;102;217;239mvarinfo\u001b[0m(\u001b[0m\u001b[39mm\u001b[0m\u001b[38;2;249;38;114m::\u001b[0m\u001b[39mModule\u001b[0m\u001b[38;2;249;38;114m=\u001b[0m\u001b[39mMain\u001b[0m\u001b[39m,\u001b[0m \u001b[0m\u001b[39mpattern\u001b[0m\u001b[38;2;249;38;114m::\u001b[0m\u001b[39mRegex\u001b[0m\u001b[38;2;249;38;114m=\u001b[0m\u001b[39mr\u001b[0m\u001b[39m\"\u001b[0m\u001b[38;2;230;219;116m\u001b[0m\u001b[39m\"\u001b[0m\u001b[39m;\u001b[0m \u001b[0m\u001b[39mall\u001b[0m\u001b[38;2;249;38;114m::\u001b[0m\u001b[39mBool\u001b[0m \u001b[0m\u001b[38;2;249;38;114m=\u001b[0m \u001b[0m\u001b[38;2;249;38;114mfalse\u001b[0m\u001b[39m,\u001b[0m \u001b[0m\u001b[39mimported\u001b[0m\u001b[38;2;249;38;114m::\u001b[0m\u001b[39mBool\u001b[0m \u001b[0m\u001b[38;2;249;38;114m=\u001b[0m \u001b[0m\u001b[38;2;249;38;114mfalse\u001b[0m\u001b[39m,\u001b[0m \u001b[0m\u001b[39msortby\u001b[0m\u001b[38;2;249;38;114m::\u001b[0m\u001b[39mSymbol\u001b[0m \u001b[0m\u001b[38;2;249;38;114m=\u001b[0m \u001b[0m\u001b[38;2;174;129;255m:\u001b[0m\u001b[38;2;174;129;255mname\u001b[0m\u001b[39m,\u001b[0m \u001b[0m\u001b[39mminsize\u001b[0m\u001b[38;2;249;38;114m::\u001b[0m\u001b[39mInt\u001b[0m \u001b[0m\u001b[38;2;249;38;114m=\u001b[0m \u001b[0m\u001b[38;2;174;129;255m0\u001b[0m\u001b[39m)\u001b[0m\n", + " \u001b[38;2;102;217;239mvarinfo\u001b[0m(\u001b[0m\u001b[39mm\u001b[0m\u001b[38;2;249;38;114m::\u001b[0m\u001b[39mModule\u001b[0m\u001b[38;2;249;38;114m=\u001b[0m\u001b[39mMain\u001b[0m\u001b[39m,\u001b[0m \u001b[0m\u001b[39mpattern\u001b[0m\u001b[38;2;249;38;114m::\u001b[0m\u001b[39mRegex\u001b[0m\u001b[38;2;249;38;114m=\u001b[0m\u001b[39mr\u001b[0m\u001b[39m\"\u001b[0m\u001b[38;2;230;219;116m\u001b[0m\u001b[39m\"\u001b[0m\u001b[39m;\u001b[0m \u001b[0m\u001b[39mall\u001b[0m\u001b[38;2;249;38;114m=\u001b[0m\u001b[38;2;249;38;114mfalse\u001b[0m\u001b[39m,\u001b[0m \u001b[0m\u001b[39mimported\u001b[0m\u001b[38;2;249;38;114m=\u001b[0m\u001b[38;2;249;38;114mfalse\u001b[0m\u001b[39m,\u001b[0m \u001b[0m\u001b[39mrecursive\u001b[0m\u001b[38;2;249;38;114m=\u001b[0m\u001b[38;2;249;38;114mfalse\u001b[0m\u001b[39m,\u001b[0m \u001b[0m\u001b[39msortby\u001b[0m\u001b[38;2;249;38;114m::\u001b[0m\u001b[39mSymbol\u001b[0m\u001b[38;2;249;38;114m=\u001b[0m\u001b[38;2;174;129;255m:\u001b[0m\u001b[38;2;174;129;255mname\u001b[0m\u001b[39m,\u001b[0m \u001b[0m\u001b[39mminsize\u001b[0m\u001b[38;2;249;38;114m::\u001b[0m\u001b[39mInt\u001b[0m\u001b[38;2;249;38;114m=\u001b[0m\u001b[38;2;174;129;255m0\u001b[0m\u001b[39m)\u001b[0m\n", "\n", "\n", " Return a markdown table giving information about exported global variables\n", @@ -1010,10 +817,14 @@ " (default), \u001b[36m:size\u001b[39m, and \u001b[36m:summary\u001b[39m.\n", "\n", " • \u001b[36mminsize\u001b[39m : only includes objects with size at least \u001b[36mminsize\u001b[39m bytes.\n", - " Defaults to \u001b[36m0\u001b[39m." + " Defaults to \u001b[36m0\u001b[39m.\n", + "\n", + " The output of \u001b[36mvarinfo\u001b[39m is intended for display purposes only. See also \u001b[36mnames\u001b[39m\n", + " to get an array of symbols defined in a module, which is suitable for more\n", + " general manipulations." ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1024,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1048,7 +859,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1057,7 +868,7 @@ "(2.5, -0.5, 1.5, 0.0, 1.0)" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1068,7 +879,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1110,7 +921,7 @@ " 4. ..." ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1127,7 +938,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1136,7 +947,7 @@ "true" ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1147,7 +958,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1156,7 +967,7 @@ "Float64" ] }, - "execution_count": 27, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1167,7 +978,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1176,7 +987,7 @@ "Any" ] }, - "execution_count": 28, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1187,7 +998,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1199,7 +1010,7 @@ " Unsigned" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1210,7 +1021,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1225,7 +1036,7 @@ " Int8" ] }, - "execution_count": 30, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1236,7 +1047,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1245,7 +1056,7 @@ "true" ] }, - "execution_count": 31, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1256,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1265,7 +1076,7 @@ "true" ] }, - "execution_count": 32, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1276,7 +1087,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1285,7 +1096,7 @@ "Int64" ] }, - "execution_count": 33, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1296,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1305,7 +1116,7 @@ "UInt8" ] }, - "execution_count": 34, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1324,7 +1135,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1343,7 +1154,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1362,7 +1173,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1381,7 +1192,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1400,7 +1211,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1427,7 +1238,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1439,7 +1250,7 @@ " 0.0 0.0" ] }, - "execution_count": 40, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1450,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1468,7 +1279,7 @@ " 1.0 1.0 1.0" ] }, - "execution_count": 41, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1479,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1490,7 +1301,7 @@ " π π" ] }, - "execution_count": 42, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1501,7 +1312,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1512,7 +1323,7 @@ " 3.14159 3.14159" ] }, - "execution_count": 43, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1523,7 +1334,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1539,7 +1350,7 @@ " Creates a \u001b[36mBitArray\u001b[39m with all values set to \u001b[36mtrue\u001b[39m" ] }, - "execution_count": 44, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1550,7 +1361,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1566,7 +1377,7 @@ "BitMatrix\u001b[90m (alias for \u001b[39m\u001b[90mBitArray{2}\u001b[39m\u001b[90m)\u001b[39m" ] }, - "execution_count": 45, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1579,7 +1390,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1595,7 +1406,7 @@ " Creates a \u001b[36mBitArray\u001b[39m with all values set to \u001b[36mfalse\u001b[39m" ] }, - "execution_count": 46, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1606,7 +1417,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1622,7 +1433,7 @@ "BitMatrix\u001b[90m (alias for \u001b[39m\u001b[90mBitArray{2}\u001b[39m\u001b[90m)\u001b[39m" ] }, - "execution_count": 47, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1635,7 +1446,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1651,7 +1462,7 @@ " \u001b[1mComprehension\u001b[22m" ] }, - "execution_count": 48, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1662,7 +1473,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1698,7 +1509,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -1715,7 +1526,7 @@ " (https://docs.julialang.org/en/v1/manual/functions/)" ] }, - "execution_count": 50, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -1728,7 +1539,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -1744,7 +1555,7 @@ " \u001b[1mSpreading Arguments\u001b[22m" ] }, - "execution_count": 51, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -1762,7 +1573,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -1771,7 +1582,7 @@ "foo (generic function with 4 methods)" ] }, - "execution_count": 52, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -1782,7 +1593,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1791,7 +1602,7 @@ "(0, 6)" ] }, - "execution_count": 53, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1802,7 +1613,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -1811,7 +1622,7 @@ "6" ] }, - "execution_count": 54, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -1829,7 +1640,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -1838,7 +1649,7 @@ "bar (generic function with 1 method)" ] }, - "execution_count": 55, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -1849,7 +1660,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -1858,7 +1669,7 @@ "0" ] }, - "execution_count": 56, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1869,7 +1680,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -1878,7 +1689,7 @@ "7.2" ] }, - "execution_count": 57, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1889,14 +1700,14 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MethodError(bar, (1, 2, 3), 0x00000000000082db)\n" + "MethodError(bar, (1, 2, 3), 0x0000000000007b01)\n" ] } ], @@ -1910,7 +1721,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -1926,7 +1737,7 @@ " \u001b[1mMultiple Dispatch\u001b[22m" ] }, - "execution_count": 59, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -1937,7 +1748,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -1946,7 +1757,7 @@ "f (generic function with 1 method)" ] }, - "execution_count": 60, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -1960,7 +1771,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -1969,7 +1780,7 @@ "f (generic function with 2 methods)" ] }, - "execution_count": 61, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -1981,7 +1792,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -1990,7 +1801,7 @@ "f (generic function with 4 methods)" ] }, - "execution_count": 62, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -2004,27 +1815,27 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "# 4 methods for generic function f from \u001b[35mMain\u001b[39m:" + "# 4 methods for generic function f from \u001b[35mMain\u001b[39m:" ], "text/plain": [ "# 4 methods for generic function \"f\" from \u001b[35mMain\u001b[39m:\n", - " [1] f(\u001b[90mx\u001b[39m::\u001b[1mInt64\u001b[22m)\n", - "\u001b[90m @\u001b[39m \u001b[90m\u001b[4mIn[60]:2\u001b[24m\u001b[39m\n", - " [2] f(\u001b[90mx\u001b[39m::\u001b[1mFloat64\u001b[22m)\n", - "\u001b[90m @\u001b[39m \u001b[90m\u001b[4mIn[61]:2\u001b[24m\u001b[39m\n", - " [3] f(\u001b[90mx\u001b[39m::\u001b[1mChar\u001b[22m)\n", + " [1] f(\u001b[90mx\u001b[39m::\u001b[1mString\u001b[22m)\n", + "\u001b[90m @\u001b[39m \u001b[90m\u001b[4mIn[63]:4\u001b[24m\u001b[39m\n", + " [2] f(\u001b[90mx\u001b[39m::\u001b[1mChar\u001b[22m)\n", + "\u001b[90m @\u001b[39m \u001b[90m\u001b[4mIn[63]:2\u001b[24m\u001b[39m\n", + " [3] f(\u001b[90mx\u001b[39m::\u001b[1mFloat64\u001b[22m)\n", "\u001b[90m @\u001b[39m \u001b[90m\u001b[4mIn[62]:2\u001b[24m\u001b[39m\n", - " [4] f(\u001b[90mx\u001b[39m::\u001b[1mString\u001b[22m)\n", - "\u001b[90m @\u001b[39m \u001b[90m\u001b[4mIn[62]:4\u001b[24m\u001b[39m" + " [4] f(\u001b[90mx\u001b[39m::\u001b[1mInt64\u001b[22m)\n", + "\u001b[90m @\u001b[39m \u001b[90m\u001b[4mIn[61]:2\u001b[24m\u001b[39m" ] }, - "execution_count": 63, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -2035,7 +1846,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -2044,7 +1855,7 @@ "(1, 2.0, \"xyz\", \"abcabc\")" ] }, - "execution_count": 64, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -2055,7 +1866,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -2064,7 +1875,7 @@ "mycos (generic function with 2 methods)" ] }, - "execution_count": 65, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -2076,7 +1887,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 67, "metadata": { "scrolled": true }, @@ -2084,17 +1895,17 @@ { "data": { "text/html": [ - "# 2 methods for generic function mycos from \u001b[35mMain\u001b[39m:" + "# 2 methods for generic function mycos from \u001b[35mMain\u001b[39m:" ], "text/plain": [ "# 2 methods for generic function \"mycos\" from \u001b[35mMain\u001b[39m:\n", - " [1] mycos(\u001b[90mx\u001b[39m)\n", - "\u001b[90m @\u001b[39m \u001b[90m\u001b[4mIn[65]:1\u001b[24m\u001b[39m\n", - " [2] mycos(\u001b[90madj\u001b[39m, \u001b[90mhyp\u001b[39m)\n", - "\u001b[90m @\u001b[39m \u001b[90m\u001b[4mIn[65]:2\u001b[24m\u001b[39m" + " [1] mycos(\u001b[90madj\u001b[39m, \u001b[90mhyp\u001b[39m)\n", + "\u001b[90m @\u001b[39m \u001b[90m\u001b[4mIn[66]:2\u001b[24m\u001b[39m\n", + " [2] mycos(\u001b[90mx\u001b[39m)\n", + "\u001b[90m @\u001b[39m \u001b[90m\u001b[4mIn[66]:1\u001b[24m\u001b[39m" ] }, - "execution_count": 66, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -2105,7 +1916,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 68, "metadata": { "scrolled": true }, @@ -2113,14 +1924,14 @@ { "data": { "text/html": [ - "mycos(x) in Main at In[65]:1" + "mycos(x) in Main at In[66]:1" ], "text/plain": [ "mycos(\u001b[90mx\u001b[39m)\n", - "\u001b[90m @\u001b[39m \u001b[90mMain\u001b[39m \u001b[90m\u001b[4mIn[65]:1\u001b[24m\u001b[39m" + "\u001b[90m @\u001b[39m \u001b[90mMain\u001b[39m \u001b[90m\u001b[4mIn[66]:1\u001b[24m\u001b[39m" ] }, - "execution_count": 67, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -2131,7 +1942,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 69, "metadata": { "scrolled": true }, @@ -2139,14 +1950,14 @@ { "data": { "text/html": [ - "mycos(adj, hyp) in Main at In[65]:2" + "mycos(adj, hyp) in Main at In[66]:2" ], "text/plain": [ "mycos(\u001b[90madj\u001b[39m, \u001b[90mhyp\u001b[39m)\n", - "\u001b[90m @\u001b[39m \u001b[90mMain\u001b[39m \u001b[90m\u001b[4mIn[65]:2\u001b[24m\u001b[39m" + "\u001b[90m @\u001b[39m \u001b[90mMain\u001b[39m \u001b[90m\u001b[4mIn[66]:2\u001b[24m\u001b[39m" ] }, - "execution_count": 68, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -2157,7 +1968,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -2166,7 +1977,7 @@ "mycos (generic function with 2 methods)" ] }, - "execution_count": 69, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -2177,20 +1988,20 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "mycos(adj) in Main at In[69]:1" + "mycos(adj) in Main at In[70]:1" ], "text/plain": [ "mycos(\u001b[90madj\u001b[39m)\n", - "\u001b[90m @\u001b[39m \u001b[90mMain\u001b[39m \u001b[90m\u001b[4mIn[69]:1\u001b[24m\u001b[39m" + "\u001b[90m @\u001b[39m \u001b[90mMain\u001b[39m \u001b[90m\u001b[4mIn[70]:1\u001b[24m\u001b[39m" ] }, - "execution_count": 70, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -2208,7 +2019,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -2217,7 +2028,7 @@ "9" ] }, - "execution_count": 71, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -2230,7 +2041,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -2246,7 +2057,7 @@ " Another pssible way is t use \u001b[36m∘\u001b[39m\u001b[4m\\circ{tab}\u001b[24m symbol" ] }, - "execution_count": 72, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -2257,7 +2068,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -2266,7 +2077,7 @@ "9" ] }, - "execution_count": 73, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -2277,7 +2088,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -2293,7 +2104,7 @@ " Definition of a function can be done on the fly" ] }, - "execution_count": 74, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -2304,7 +2115,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -2313,7 +2124,7 @@ "5.0" ] }, - "execution_count": 75, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -2324,7 +2135,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -2340,7 +2151,7 @@ " \u001b[1mMetaprogramming:\u001b[22m Code is optimized by nature in \u001b[35m{\\tt Julia}\u001b[39m" ] }, - "execution_count": 76, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -2353,7 +2164,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -2362,7 +2173,7 @@ "Foo (generic function with 1 method)" ] }, - "execution_count": 77, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -2379,19 +2190,19 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[90m; @ In[77]:1 within `Foo`\u001b[39m\n", - "\u001b[95mdefine\u001b[39m \u001b[36mi64\u001b[39m \u001b[93m@julia_Foo_4407\u001b[39m\u001b[33m(\u001b[39m\u001b[36mi64\u001b[39m \u001b[95msignext\u001b[39m \u001b[0m%0\u001b[33m)\u001b[39m \u001b[0m#0 \u001b[33m{\u001b[39m\n", + "\u001b[90m; @ In[78]:1 within `Foo`\u001b[39m\n", + "\u001b[95mdefine\u001b[39m \u001b[36mi64\u001b[39m \u001b[93m@julia_Foo_3998\u001b[39m\u001b[33m(\u001b[39m\u001b[36mi64\u001b[39m \u001b[95msignext\u001b[39m \u001b[0m%0\u001b[33m)\u001b[39m \u001b[0m#0 \u001b[33m{\u001b[39m\n", "\u001b[91mtop:\u001b[39m\n", - "\u001b[90m; @ In[77]:3 within `Foo`\u001b[39m\n", + "\u001b[90m; @ In[78]:3 within `Foo`\u001b[39m\n", " \u001b[0m%1 \u001b[0m= \u001b[96m\u001b[1madd\u001b[22m\u001b[39m \u001b[36mi64\u001b[39m \u001b[0m%0\u001b[0m, \u001b[33m338350\u001b[39m\n", - "\u001b[90m; @ In[77]:6 within `Foo`\u001b[39m\n", + "\u001b[90m; @ In[78]:6 within `Foo`\u001b[39m\n", " \u001b[96m\u001b[1mret\u001b[22m\u001b[39m \u001b[36mi64\u001b[39m \u001b[0m%1\n", "\u001b[33m}\u001b[39m\n" ] @@ -2403,7 +2214,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -2455,7 +2266,7 @@ " function." ] }, - "execution_count": 79, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -2474,7 +2285,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -2482,8 +2293,8 @@ "output_type": "stream", "text": [ "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", - "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/MEGA/git-repos/infodev/Codes/Project.toml`\n", - "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/MEGA/git-repos/infodev/Codes/Manifest.toml`\n" + "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/Work/git-repos/AI-ML-DL/jlai/Codes/Julia/Part-1/Project.toml`\n", + "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/Work/git-repos/AI-ML-DL/jlai/Codes/Julia/Part-1/Manifest.toml`\n" ] } ], @@ -2493,7 +2304,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -2510,114 +2321,114 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 83, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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"execute_result" } @@ -3231,7 +3042,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -3247,7 +3058,7 @@ " \u001b[1mNormal Distribution\u001b[22m" ] }, - "execution_count": 88, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -3258,7 +3069,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -3267,384 +3078,374 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 91, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"execution_count": 92, + "execution_count": 93, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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`~/MEGA/git-repos/infodev/Codes/Manifest.toml`\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m StringManipulation ─ v0.4.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m PrettyTables ─────── v2.4.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m DataFrames ───────── v1.7.0\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/Work/git-repos/AI-ML-DL/jlai/Codes/Julia/Part-1/Project.toml`\n", + " \u001b[90m[a93c6f00] \u001b[39m\u001b[92m+ DataFrames v1.7.0\u001b[39m\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/Work/git-repos/AI-ML-DL/jlai/Codes/Julia/Part-1/Manifest.toml`\n", + " \u001b[90m[a93c6f00] \u001b[39m\u001b[92m+ DataFrames v1.7.0\u001b[39m\n", + " \u001b[90m[842dd82b] \u001b[39m\u001b[92m+ InlineStrings v1.4.2\u001b[39m\n", + " \u001b[90m[41ab1584] \u001b[39m\u001b[92m+ InvertedIndices v1.3.0\u001b[39m\n", + " \u001b[90m[2dfb63ee] \u001b[39m\u001b[92m+ PooledArrays v1.4.3\u001b[39m\n", + " \u001b[90m[08abe8d2] \u001b[39m\u001b[92m+ PrettyTables v2.4.0\u001b[39m\n", + " \u001b[90m[91c51154] \u001b[39m\u001b[92m+ SentinelArrays v1.4.5\u001b[39m\n", + " \u001b[90m[892a3eda] \u001b[39m\u001b[92m+ StringManipulation v0.4.0\u001b[39m\n", + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mStringManipulation\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mPrettyTables\u001b[39m\n", + "\u001b[32m ✓ \u001b[39mDataFrames\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mLatexify → DataFramesExt\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mBangBang → BangBangDataFramesExt\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mTransducers → TransducersDataFramesExt\u001b[39m\n", + " 6 dependencies successfully precompiled in 72 seconds. 287 already precompiled.\n", "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", - "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/MEGA/git-repos/infodev/Codes/Project.toml`\n", - "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/MEGA/git-repos/infodev/Codes/Manifest.toml`\n" + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/Work/git-repos/AI-ML-DL/jlai/Codes/Julia/Part-1/Project.toml`\n", + " \u001b[90m[336ed68f] \u001b[39m\u001b[92m+ CSV v0.10.14\u001b[39m\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/Work/git-repos/AI-ML-DL/jlai/Codes/Julia/Part-1/Manifest.toml`\n", + " \u001b[90m[336ed68f] \u001b[39m\u001b[92m+ CSV v0.10.14\u001b[39m\n", + " \u001b[90m[48062228] \u001b[39m\u001b[92m+ FilePathsBase v0.9.22\u001b[39m\n", + " \u001b[90m[ea10d353] \u001b[39m\u001b[92m+ WeakRefStrings v1.4.2\u001b[39m\n", + " \u001b[90m[76eceee3] \u001b[39m\u001b[92m+ WorkerUtilities v1.6.1\u001b[39m\n", + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mFilePathsBase → FilePathsBaseTestExt\u001b[39m\n", + " 1 dependency successfully precompiled in 1 seconds. 298 already precompiled.\n" ] } ], @@ -4676,7 +4502,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -4692,7 +4518,7 @@ " Create new CSV file" ] }, - "execution_count": 94, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -4703,7 +4529,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -4712,7 +4538,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -4729,7 +4555,7 @@ " changes the file timestamps." ] }, - "execution_count": 96, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -4740,7 +4566,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -4749,7 +4575,7 @@ "\"test-file.csv\"" ] }, - "execution_count": 97, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -4760,14 +4586,14 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 99, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-rw-rw-r-- 1 mhamdi mhamdi 66 Feb 19 16:32 test-file.csv\n" + "-rw-rw-r-- 1 mhamdi mhamdi 0 Oct 5 16:33 test-file.csv\n" ] } ], @@ -4777,7 +4603,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -4786,7 +4612,7 @@ "IOStream()" ] }, - "execution_count": 99, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" } @@ -4797,7 +4623,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 101, "metadata": {}, "outputs": [ { @@ -4813,7 +4639,7 @@ " Let's create some imaginary data" ] }, - "execution_count": 100, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -4824,7 +4650,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -4855,7 +4681,7 @@ " 4 │ Ala 4 5.5" ] }, - "execution_count": 101, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -4870,7 +4696,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 103, "metadata": {}, "outputs": [ { @@ -4886,7 +4712,7 @@ " Write \u001b[36mdf\u001b[39m to file" ] }, - "execution_count": 102, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -4897,7 +4723,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -4906,7 +4732,7 @@ "\"test-file.csv\"" ] }, - "execution_count": 103, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } @@ -4917,7 +4743,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 105, "metadata": {}, "outputs": [ { @@ -4934,7 +4760,7 @@ " again." ] }, - "execution_count": 104, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -4945,7 +4771,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 106, "metadata": {}, "outputs": [ { @@ -4976,7 +4802,7 @@ " 4 │ Ala 4 5.5" ] }, - "execution_count": 105, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -4995,7 +4821,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -5011,7 +4837,7 @@ "Vector{ComplexF64}\u001b[90m (alias for \u001b[39m\u001b[90mArray{Complex{Float64}, 1}\u001b[39m\u001b[90m)\u001b[39m" ] }, - "execution_count": 106, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -5024,7 +4850,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -5042,21 +4868,21 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5×4 Matrix{Float64}:\n", - " -2.00391e-255 -1.17454e-223 5.0e-324 6.93707e-310\n", - " -9.17617e-67 5.0e-324 6.93707e-310 -1.98921e-38\n", - " 5.0e-324 6.937e-310 -7.11175e-71 3.15894e121\n", - " 6.93702e-310 -1.89771e-64 2.39753e-135 5.0e-324\n", - " -5.93036e-164 3.08246e25 5.0e-324 6.93707e-310" + " 6.47602e-310 6.47602e-310 6.47602e-310 6.47602e-310\n", + " 6.47602e-310 6.47602e-310 6.47602e-310 6.47602e-310\n", + " 6.47602e-310 6.47602e-310 6.47602e-310 6.47602e-310\n", + " 6.47602e-310 6.47602e-310 6.47602e-310 6.47602e-310\n", + " 6.47602e-310 6.47602e-310 6.47602e-310 6.47602e-310" ] }, - "execution_count": 108, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -5067,7 +4893,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 110, "metadata": {}, "outputs": [], "source": [ @@ -5077,19 +4903,19 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3×4 Matrix{Float64}:\n", - " -2.00391e-255 -1.17454e-223 5.0e-324 6.93707e-310\n", - " 5.0e-324 6.937e-310 -7.11175e-71 3.15894e121\n", - " 6.93702e-310 -1.89771e-64 2.39753e-135 5.0e-324" + " 6.47602e-310 6.47602e-310 6.47602e-310 6.47602e-310\n", + " 6.47602e-310 6.47602e-310 6.47602e-310 6.47602e-310\n", + " 6.47602e-310 6.47602e-310 6.47602e-310 6.47602e-310" ] }, - "execution_count": 110, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -5100,21 +4926,21 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5×2 Matrix{Float64}:\n", - " -1.17454e-223 5.0e-324\n", - " 5.0e-324 6.93707e-310\n", - " 6.937e-310 -7.11175e-71\n", - " -1.89771e-64 2.39753e-135\n", - " 3.08246e25 5.0e-324" + " 6.47602e-310 6.47602e-310\n", + " 6.47602e-310 6.47602e-310\n", + " 6.47602e-310 6.47602e-310\n", + " 6.47602e-310 6.47602e-310\n", + " 6.47602e-310 6.47602e-310" ] }, - "execution_count": 111, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -5125,19 +4951,19 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 113, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3×2 Matrix{Float64}:\n", - " -1.17454e-223 5.0e-324\n", - " 6.937e-310 -7.11175e-71\n", - " -1.89771e-64 2.39753e-135" + " 6.47602e-310 6.47602e-310\n", + " 6.47602e-310 6.47602e-310\n", + " 6.47602e-310 6.47602e-310" ] }, - "execution_count": 112, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -5156,7 +4982,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 114, "metadata": {}, "outputs": [ { @@ -5172,7 +4998,7 @@ " \u001b[1mConditional Evaluation\u001b[22m" ] }, - "execution_count": 113, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -5183,7 +5009,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -5207,7 +5033,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -5223,7 +5049,7 @@ " \u001b[1m\u001b[36mWhile\u001b[39m Loop\u001b[22m" ] }, - "execution_count": 115, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -5234,7 +5060,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 117, "metadata": {}, "outputs": [ { @@ -5260,7 +5086,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -5276,7 +5102,7 @@ " \u001b[1m\u001b[36mFor\u001b[39m Loop\u001b[22m" ] }, - "execution_count": 117, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -5287,7 +5113,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -5335,7 +5161,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 120, "metadata": {}, "outputs": [ { @@ -5351,7 +5177,7 @@ " Here is an example of how the basic calculator function could look like:" ] }, - "execution_count": 119, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } @@ -5362,7 +5188,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 121, "metadata": {}, "outputs": [ { @@ -5371,7 +5197,7 @@ "calculator (generic function with 1 method)" ] }, - "execution_count": 120, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -5394,7 +5220,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 122, "metadata": {}, "outputs": [ { @@ -5426,7 +5252,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 123, "metadata": { "tags": [] }, @@ -5435,25 +5261,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Julia Version 1.9.3\n", - "Commit bed2cd540a (2023-08-24 14:43 UTC)\n", + "Julia Version 1.10.4\n", + "Commit 48d4fd48430 (2024-06-04 10:41 UTC)\n", "Build Info:\n", - "\n", - " Note: This is an unofficial build, please report bugs to the project\n", - " responsible for this build and not to the Julia project unless you can\n", - " reproduce the issue using official builds available at https://julialang.org/downloads\n", - "\n", + " Official https://julialang.org/ release\n", "Platform Info:\n", " OS: Linux (x86_64-linux-gnu)\n", " CPU: 8 × Intel(R) Core(TM) i7-8565U CPU @ 1.80GHz\n", " WORD_SIZE: 64\n", " LIBM: libopenlibm\n", - " LLVM: libLLVM-14.0.6 (ORCJIT, skylake)\n", - " Threads: 2 on 8 virtual cores\n", + " LLVM: libLLVM-15.0.7 (ORCJIT, skylake)\n", + "Threads: 1 default, 0 interactive, 1 GC (on 8 virtual cores)\n", "Environment:\n", - " DYLD_LIBRARY_PATH = /home/mhamdi/torch/install/lib:/home/mhamdi/torch/install/lib:/home/mhamdi/torch/install/lib:\n", - " LD_LIBRARY_PATH = /home/mhamdi/torch/install/lib:/home/mhamdi/torch/install/lib:/home/mhamdi/torch/install/lib:\n", - " JULIA_IMAGE_THREADS = 1\n" + " LD_LIBRARY_PATH = /home/mhamdi/torch/install/lib:\n", + " DYLD_LIBRARY_PATH = /home/mhamdi/torch/install/lib:\n" ] } ], @@ -5464,7 +5285,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 124, "metadata": { "tags": [] }, @@ -5483,7 +5304,7 @@ " arguments" ] }, - "execution_count": 123, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -5495,7 +5316,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 125, "metadata": { "tags": [] }, @@ -5521,7 +5342,7 @@ "df & 915 bytes & 4×3 DataFrame \\\\\n", "dict & 503 bytes & Dict\\{String, Int64\\} with 3 entries \\\\\n", "f & 0 bytes & f (generic function with 4 methods) \\\\\n", - "file & 372 bytes & IOStream \\\\\n", + "file & 396 bytes & IOStream \\\\\n", "foo & 0 bytes & foo (generic function with 4 methods) \\\\\n", "fruits & 160 bytes & 5-element Vector\\{String\\} \\\\\n", "g & 0 bytes & g (generic function with 1 method) \\\\\n", @@ -5559,7 +5380,7 @@ "| df | 915 bytes | 4×3 DataFrame |\n", "| dict | 503 bytes | Dict{String, Int64} with 3 entries |\n", "| f | 0 bytes | f (generic function with 4 methods) |\n", - "| file | 372 bytes | IOStream |\n", + "| file | 396 bytes | IOStream |\n", "| foo | 0 bytes | foo (generic function with 4 methods) |\n", "| fruits | 160 bytes | 5-element Vector{String} |\n", "| g | 0 bytes | g (generic function with 1 method) |\n", @@ -5596,7 +5417,7 @@ " df 915 bytes 4×3 DataFrame \n", " dict 503 bytes Dict{String, Int64} with 3 entries \n", " f 0 bytes f (generic function with 4 methods) \n", - " file 372 bytes IOStream \n", + " file 396 bytes IOStream \n", " foo 0 bytes foo (generic function with 4 methods) \n", " fruits 160 bytes 5-element Vector{String} \n", " g 0 bytes g (generic function with 1 method) \n", @@ -5617,7 +5438,7 @@ " z 768 bytes 91-element Vector{Float64} " ] }, - "execution_count": 124, + "execution_count": 125, "metadata": {}, "output_type": "execute_result" } @@ -5638,7 +5459,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 126, "metadata": { "tags": [] }, @@ -5649,7 +5470,7 @@ "Main.MyModule" ] }, - "execution_count": 125, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -5664,7 +5485,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 127, "metadata": { "tags": [] }, @@ -5676,24 +5497,24 @@ "{l | r | l}\n", "name & size & summary \\\\\n", "\\hline\n", - "MyModule & 2.304 KiB & Module \\\\\n", + "MyModule & 2.207 KiB & Module \\\\\n", "a & 8 bytes & Int64 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "| name | size | summary |\n", "|:-------- | ---------:|:------- |\n", - "| MyModule | 2.304 KiB | Module |\n", + "| MyModule | 2.207 KiB | Module |\n", "| a | 8 bytes | Int64 |\n" ], "text/plain": [ " name size summary\n", " –––––––– ––––––––– –––––––\n", - " MyModule 2.304 KiB Module \n", + " MyModule 2.207 KiB Module \n", " a 8 bytes Int64 " ] }, - "execution_count": 126, + "execution_count": 127, "metadata": {}, "output_type": "execute_result" } @@ -5704,7 +5525,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 128, "metadata": { "tags": [] }, @@ -5715,7 +5536,7 @@ "π = 3.1415926535897..." ] }, - "execution_count": 127, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -5726,7 +5547,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 129, "metadata": { "tags": [] }, @@ -5737,7 +5558,7 @@ "0" ] }, - "execution_count": 128, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -5748,7 +5569,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 130, "metadata": { "tags": [] }, @@ -5759,7 +5580,7 @@ "true" ] }, - "execution_count": 129, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } @@ -5778,15 +5599,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.9.3", + "display_name": "Julia 1.10.4", "language": "julia", - "name": "julia-1.9" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.9.3" + "version": "1.10.4" } }, "nbformat": 4,