Day1/31_julia_ecosystem/31_julia_ecosystem.ipynb

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   "source": [
    "# The Julia Ecosystem\n",
    "\n",
    "I will look at:\n",
    "- the package ecosystem\n",
    "- the community"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "name": "A slide ",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The Package Ecosystem\n",
    "\n",
    "- Packages can be easily installed, into project environments (as explained by Victor before) if desired.\n",
    "- Registered packages are listed on [https://juliahub.com/ui/Packages](https://juliahub.com/ui/Packages)\n",
    "- google / duckduckgo / bing / etc search often works well.  Appending `jl` often helps.\n",
    "- the package ecosystem has not reached the level of the Python ecosystem but is growing fast.  There are currently 9141 registered packages.\n",
    "\n",
    "--> packages work well together, e.g. combine [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) with [Unitful.jl](https://github.com/PainterQubits/Unitful.jl) and [Measurements.jl](https://github.com/JuliaPhysics/Measurements.jl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "name": "A slide ",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Package interoperability\n",
    "\n",
    "Example from [DiffEq docs](https://docs.sciml.ai/SciMLTutorialsOutput/html/type_handling/03-unitful.html), combining a package for units with a differential equation solver:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0 s"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Unitful\n",
    "t = 1.0u\"s\" # has units seconds\n",
    "t + 1.0u\"s\" # works, adding kg would error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "retcode: Success\n",
       "Interpolation: specialized 4th order \"free\" interpolation\n",
       "t: 3-element Vector{Quantity{Float64, 𝐓, Unitful.FreeUnits{(s,), 𝐓, nothing}}}:\n",
       "                 0.0 s\n",
       " 0.14311598261241779 s\n",
       "                 1.0 s\n",
       "u: 3-element Vector{Quantity{Float64, 𝐋 𝐌 𝐓^-2, Unitful.FreeUnits{(N,), 𝐋 𝐌 𝐓^-2, nothing}}}:\n",
       "                1.5 N\n",
       " 1.5362091208988309 N\n",
       " 1.7720406194871123 N"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using OrdinaryDiffEq\n",
    "# dy / dt  = f(y, p, t)\n",
    "f = (y,_,t) -> 0.5*y/3.0u\"s\"\n",
    "u0 = 1.5u\"N\"\n",
    "prob = ODEProblem(f,u0,(0.0u\"s\",1.0u\"s\"))\n",
    "sol = solve(prob,Tsit5())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "name": "A slide ",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Package interoperability\n",
    "\n",
    "Let's plot the solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Plots\n",
    "plot(sol.t, sol[:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "name": "A slide ",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### There are many **cutting-edge** packages\n",
    "\n",
    "- [DifferentialEquations.jl](https://github.com/JuliaDiffEq/DifferentialEquations.jl), probably the best differential equation solver package around\n",
    "- [Flux.jl](https://fluxml.ai/2019/02/07/what-is-differentiable-programming.html) machine learning/differentiable programming package (Victor will have a project using that on Monday)\n",
    "- [JuMP.jl](https://github.com/JuliaOpt/JuMP.jl) mathematical optimisation package, big in, e.g., operations research\n",
    "- automatic differentiation (AD): [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl), [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl), many [others](https://juliadiff.org/#the_big_list)\n",
    "\n",
    "--> above packages can work together to allow, e.g., *Scientific machine learning*, combining differential equation-based, physical models with machine learning: [link to blog](https://fluxml.ai/blog/2019/03/05/dp-vs-rl.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "name": "A slide ",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### My favorite packages\n",
    "\n",
    "Utilities:\n",
    "- BenchmarkTools.jl and ProfileView.jl for performance tweaking\n",
    "- IJulia.jl (Jupyter notebooks)\n",
    "- Literate.jl produces notebooks and markdown from scripts (as I don't like notebooks after all...)\n",
    "  - -> used for the slides and exercises in this workshop\n",
    "- Infiltrator.jl a debugger: not the prettiest but works ok and does not impact performance\n",
    "- Plots.jl basic plotting\n",
    "- Revise.jl makes code updates in packages visible immediately to the current running Julia process\n",
    "\n",
    "*Install for sure into global environment: Revise.jl and BenchmarkTools.jl*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "name": "A slide ",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### My favorite packages\n",
    "\n",
    "- Rasters.jl for raster geo-data\n",
    "- JLSO.jl for storing anything\n",
    "- OrdinaryDiffEq.jl to solve Ordinary Differential Equations (ODEs)\n",
    "\n",
    "A few HPC (Ludovic's favorits):\n",
    "- MPI.jl -- inter-node communication on clusters\n",
    "- CUDA.jl, AMDGPU.jl -- to run Julia code on Nvidia and AMD GPUs\n",
    "- ParallelStencil.jl, ImplicitGlobalGrid.jl -- packages he's developing to solve PDEs on GPUs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "name": "A slide ",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Our packages\n",
    "\n",
    "Victor:\n",
    "- HighDimPDE.jl -- to solve high dimensional PDEs\n",
    "- EvoId.jl -- Evolutionary Individual based modelling, mathematically grounded.\n",
    "- PiecewiseInference.jl -- Suite for parameter inference and model selection with dynamical models characterised by complex dynamics.\n",
    "- EcoEvoModelZoo.jl -- A zoo of eco-evolutionary models with high fitness.\n",
    "\n",
    "Mauro:\n",
    "- Parameters.jl, UnPack.jl and SimpleTraits.jl -- utility packages (slowly getting superseded by Julia language features)\n",
    "- KissMCMC.jl -- an affine invariant MCMC sampler\n",
    "- WhereTheWaterFlows.jl -- a flow routing package, uses the D8 algorithm"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Julia Community\n",
    "\n",
    "The Julia community is very friendly and helpful:\n",
    "\n",
    "- Easy to get help on\n",
    "  - [discourse forum](https://discourse.julialang.org)\n",
    "  - StackOverflow (ok, maybe there it's less easy)\n",
    "  - and on more informal chat-clients: [Slack](https://julialang.slack.com/), [Zulip](https://julialang.zulipchat.com/register/)\n",
    "- Active developer community on GitHub\n",
    "- The yearly [JuliaCon](https://juliacon.org) is as fun and friendly conference (next one in July at MIT and online)"
   ]
  }
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