.gitignore

@@ -23,3 +23,4 @@ package.json
 ./theme/package.json
 __pycache__/
 contribute.egg-info/
+.*.swp

2022/2022-06-07_git-trilogy-part-2/slides/workflow.md

@@ -2,7 +2,7 @@
 
 - Create your own copy of the original (also called `upstream`) repository (`fork` in `git` jargon)
 - Download your copy to your computer (`clone` in `git` jargon)
-- Create a new brach
+- Create a new branch
 - Make changes
 - Commit changes
 - <font color="red">Push changes to server</font>

2022/2022-06-08_JuliaForNewcomers/slides/bootstrap.md

+<div class=leader>
+<i class="twa twa-axe"></i><i class="twa twa-carpentry-saw"></i><i class="twa twa-screwdriver"></i><i class="twa twa-wrench"></i><i class="twa twa-hammer"></i><br>
+Bootstrapping Julia
+</div>
+
+
+
+# Installing Julia
+
+Recommended method:
+
+- Download an archive from https://julialang.org/downloads/
+- Execute `julia` or `julia.exe` as-is
+- Link it to your `$PATH`
+
+Distribution packages usually work well too:
+
+- **Debians&Ubuntus**: `apt install julia`
+- **Iris/Aion**: `module add lang/Julia`
+
+
+
+# Life in REPL
+
+```julia
+user@pc $ julia
+
+julia> sqrt(1+1)
+1.4142135623730951
+
+julia> println("Well hello there!")
+Well hello there!
+
+julia> ?
+help?> sqrt
+
+sqrt(x)
+
+Computes the square root .....
+```
+
+
+
+# REPL modes
+
+Julia interprets some additional keys to make our life easier:
+
+- `?`: help mode
+- `;`: shell mode
+- `]`: packaging mode (looks like a box!)
+- `Backspace`: quits special mode
+- `Tab`: autocomplete anything
+- `\`... `Tab`: expand math characters
+
+
+
+
+# Managing packages from the package management environment
+
+-   Install a package
+
+```julia
+] add UnicodePlots
+```
+
+-   Uninstall a package
+
+```julia
+] remove UnicodePlots
+```
+
+
+
+# Loading libraries, modules and packages
+
+-   Load a local file (with shared functions etc.)
+
+```julia
+include("mylibrary.jl")
+```
+
+-   Load a package, add its exports to the global namespace
+
+```julia
+using UnicodePlots
+```
+
+
+
+# <i class="twa twa-light-bulb"> </i> How to write a standalone program?
+
+*Your scripts should communicate well with the environment!*
+(that means, among other, you)
+
+```julia
+#!/usr/bin/env julia
+
+function process_file(filename)
+
+  @info "Processing $filename..."
+
+  # ... do something ...
+
+  if error_detected
+    @error "something terrible has happened"
+    exit(1)
+  end
+end
+
+for file in ARGS
+    process_file(file)
+end
+```
+
+Correct processing of commandline arguments makes your scripts *repurposable*
+and *configurable*.
+
+
+
+
+# <i class="twa twa-light-bulb"></i> Workflow: Make a local environment for your script
+
+
+- Enter a local project with separate package versions
+
+```julia
+] activate path/to/project
+```
+
+- Install dependencies of the local project
+
+```julia
+] instantiate
+```
+
+- Execute a script with the project environment
+
+```sh
+$ julia --project=path/to/project script.jl
+```
+
+(Project data is stored in `Project.toml`, `Manifest.toml`.)

2022/2022-06-08_JuliaForNewcomers/slides/distributed.md

+
+<div class=leader>
+<i class="twa twa-rocket"></i>
+<i class="twa twa-rocket"></i>
+<i class="twa twa-rocket"></i><br>
+Parallel Julia
+</div>
+
+
+
+# Julia model of distributed computation
+
+<center>
+<img src="slides/img/distrib.svg" width="50%">
+</center>
+
+
+
+# Basic parallel processing
+
+**Using `Threads`:**
+
+1. start Julia with parameter `-t N`
+2. parallelize (some) loops with `Threads.@threads`
+
+```julia
+a = zeros(100000)
+Threads.@threads for i = eachindex(a)
+    a[i] = hardfunction(i)
+end
+```
+
+**Using `Distributed`:**
+
+```julia
+using Distributed
+addprocs(N)
+newVector = pmap(myFunction, myVector)
+```
+
+We will use the `Distributed` approach.
+
+
+
+# Managing your workers
+
+```julia
+using Distributed
+addprocs(4)
+
+myid()
+workers()
+```
+
+Running commands on workers:
+```julia
+@spawnat 3 @info "Message from worker"
+
+@spawnat :any myid()
+```
+
+Getting results from workers:
+```julia
+job = @spawnat :any begin sleep(10); return 123+321; end
+
+fetch(job)
+```
+
+Cleaning up:
+```julia
+rmprocs(workers())
+```
+
+
+
+# Processing lots of data items in parallel
+
+```julia
+datafiles = ["file$i.csv" for i=1:20]
+
+@everywhere function process_file(name)
+	println("Processing file $name")
+	# ... do something ...
+end
+
+pmap(process_file, datafiles)
+```
+
+<i class="twa twa-light-bulb"></i><i class="twa twa-light-bulb"></i> Doing it manually:
+```julia
+@sync for f in datafiles
+	@async @spawnat :any process_file(f)
+end
+```
+
+
+
+# Gathering results from workers
+
+```julia
+items = collect(1:1000)
+
+@everywhere compute_item(i) = 123 + 321*i
+
+pmap(compute_item, items)
+```
+
+<i class="twa twa-light-bulb"></i><i class="twa twa-light-bulb"></i><i class="twa twa-light-bulb"></i> Doing manually with `@spawnat`:
+```julia
+futures = [@spawnat :any compute_item(item) for item in items]
+
+fetch.(futures)
+```
+
+
+
+# How to design for parallelization?
+
+**Recommended way:** *Utilize the high-level looping primitives!*
+  - use `map`, parallelize by just switching to `pmap`
+  - use `reduce` or `mapreduce`, parallelize by just switching to `dmapreduce` (DistributedData.jl)
+
+
+
+# <i class="twa twa-light-bulb"></i> Parallel → distributed processing
+
+It is very easy to organize *multiple computers* to work for you!
+
+You need a working `ssh` connection:
+
+```sh
+user@pc1 $ ssh server1
+Last login: Wed Jan 13 15:29:34 2021 from 2001:a18:....
+user@server $ _
+```
+
+Spawning remote processes on remote machines:
+
+```julia
+julia> using Distributed
+julia> addprocs([("server1", 10), ("pc2", 2)])
+```
+
+**Benefit:** No additional changes to the parallel programs!
+
+
+
+<div class=leader>
+<i class="twa twa-abacus"></i>
+<i class="twa twa-laptop"></i>
+<i class="twa twa-desktop-computer"></i>
+<i class="twa twa-flag-luxembourg"></i><br>
+Utilizing ULHPC <i class="twa twa-light-bulb"></i>
+</div>
+
+
+
+# What does the cluster look like? (Iris)
+
+<center>
+<img src="slides/img/iris.png" width="30%">
+<br>
+<tt>hpc-docs.uni.lu/systems/iris</tt>
+</center>
+
+
+
+# Running Julia on the computing nodes
+
+Start an allocation and connect to it:
+```sh
+0 [mkratochvil@access1 ~]$ srun -p interactive -t 30 --pty bash -i
+```
+
+(You can also use `si`.)
+
+After some brief time, you should get a shell on a compute node. There you can install and start Julia as usual:
+
+```
+0 [mkratochvil@iris-131 ~](2696005 1N/T/1CN)$ module add lang/Julia
+0 [mkratochvil@iris-131 ~](2696005 1N/T/1CN)$ julia
+               _
+   _       _ _(_)_     |  Documentation: https://docs.julialang.org
+  (_)     | (_) (_)    |
+   _ _   _| |_  __ _   |  Type "?" for help, "]?" for Pkg help.
+  | | | | | | |/ _` |  |
+  | | |_| | | | (_| |  |  Version 1.6.2 (2021-07-14)
+ _/ |\__'_|_|_|\__'_|  |  Official https://julialang.org/ release
+|__/                   |
+
+julia> 
+```
+
+
+
+# Making a HPC-compatible Julia script
+
+Main challenges:
+1. discover the available resources
+2. spawn worker processes at the right place
+
+```julia
+using ClusterManagers
+
+addprocs_slurm(parse(Int, ENV["SLURM_NTASKS"]))
+
+# ... continue as usual
+```
+
+
+
+# Scheduling an analysis script
+
+Normally, you write a "batch script" and add it to a queue using `sbatch`.
+
+Script in `runAnalysis.sbatch`:
+```sh
+#!/bin/bash
+# SBATCH -J MyAnalysisInJulia
+# SBATCH -n 10
+# SBATCH -c 1
+# SBATCH -t 30
+# SBATCH --mem-per-cpu 4G
+
+julia runAnalysis.jl
+```
+
+You start the script using:
+```sh
+ $ sbatch runAnalysis.sbatch
+```
+
+
+
+<div class=leader>
+<i class="twa twa-blueberries"></i>
+<i class="twa twa-red-apple"></i>
+<i class="twa twa-melon"></i>
+<i class="twa twa-grapes"></i><br>
+Questions?
+</div>
+
+Lets do some hands-on problem solving (expected around 15 minutes)

2022/2022-06-08_JuliaForNewcomers/slides/img/distrib.svg

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2022/2022-06-08_JuliaForNewcomers/slides/img/julia.svg

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2022/2022-06-08_JuliaForNewcomers/slides/index.md

+
+# Julia for newcomers
+
+## June 8th, 2022
+
+<div style="top: 6em; left: 0%; position: absolute;">
+    <img src="theme/img/lcsb_bg.png">
+</div>
+
+<div style="top: 1em; left: 60%; position: absolute;">
+    <img src="slides/img/r3-training-logo.png" height="200px">
+    <img src="slides/img/julia.svg" height="200px">
+    <h1 style="margin-top:3ex; margin-bottom:3ex;">Julia for newcomers</h1>
+    <h4>
+        Laurent Heirendt, Ph.D.<br>
+        Miroslav Kratochvíl, Ph.D.<br><br>
+        R3 Team - <a href="mailto:lcsb-r3@uni.lu">lcsb-r3@uni.lu</a><br>
+        <i>Luxembourg Centre for Systems Biomedicine</i>
+    </h4>
+</div>
+
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/SebastianAigner/twemoji-amazing@1.0.0/twemoji-amazing.css">
+<style>
+	code {border: 2pt dotted #f80; padding: .4ex; border-radius: .7ex; color:#444; }
+	.reveal pre code {border: 0; font-size: 18pt; line-height:27pt;}
+	em {color: #e02;}
+	li {margin-bottom: 1ex;}
+	div.leader {font-size:400%; line-height:120%; font-weight:bold; margin: 1em;}
+	section {padding-bottom: 10em;}
+</style>

2022/2022-06-08_JuliaForNewcomers/slides/intro.md

+# Motivation first!
+
+*Why is it good to work in compiled language?*
+
+- Programs become much faster for free.
+
+*What do we gain by having types in the language?*
+
+- Generic programming, and lots of optimization possibilities for the compiler.
+
+*Is Julia ecosystem ready for my needs? <i class="twa twa-thinking-face"></i>*
+
+- Likely. If not, extending the packages is super easy.
+- Base includes most of the functionality of Matlab, R and Python with numpy,
+  and many useful bits of C++
+
+
+
+# Why Julia?
+
+<center><img src="slides/img/whyjulia.png" width="80%"></center>

2022/2022-06-08_JuliaForNewcomers/slides/io.md

+
+<div class=leader>
+<i class="twa twa-bar-chart"></i>
+<i class="twa twa-blue-book"></i>
+<i class="twa twa-computer-disk"></i>
+<i class="twa twa-chart-increasing"></i><br>
+Working with data
+</div>
+
+
+
+# What do we usually need?
+
+- we need a comfortable abstraction over "long" tabular data → *data frames*
+- we need to get the data in and out → *IO functions*
+- we need to make pictures → *plotting packages*
+
+
+
+# Writing files
+
+All at once:
+```julia
+write("file.txt", "What a string!\n")
+write("data.bin", UInt32[1,2,3])
+```
+
+By parts (with streaming, etc.):
+```julia
+f = open("file.txt", "w")
+println(f, "This is my string!")
+# ...
+close(f)
+```
+
+Better:
+```julia
+open("file.txt", "a") do f
+    println(f, "The string again!")
+    # ...
+end
+``` 
+
+
+
+# Reading files
+
+All at once:
+```julia
+read("file.txt", String)
+open(x -> collect(readeach(x, UInt32)), "data.bin", "r")
+```
+
+Process all lines:
+```julia
+for line in eachline("file.txt")
+    println("got a line: " * line)
+end
+```
+
+Manually:
+```julia
+open("inputs.txt", "r") do io
+    a = parse(Int, readline(io))
+    b = parse(Int, readline(io))
+    println("$a * $b = $(a*b)")
+end
+```
+
+
+
+# Extremely useful: string interpolation
+
+Pasting strings manually is _boring_.
+
+```julia
+"String contains $val and $otherval."
+
+do_something("input$i.txt", "output$i.txt")
+
+"$str <<-->> $(reverse(str))"
+```
+
+The conversion to actual strings is done using `show()`. (Customize by overloading!)
+
+
+
+# Reading and writing structured data
+
+```julia
+using DelimitedFiles
+
+mtx = readdlm("matrix.tsv", '\t', Int, '\n')   # returns a Matrix{Int}
+
+writedlm("matrix.csv", ',')
+```
+
+
+
+# Data frames
+
+Package `DataFrames.jl` provides a work-alike of the data frames from
+other environments (pandas, `data.frame`, tibbles, ...)
+
+```julia
+using DataFrames
+
+mydata = DataFrame(id = [32,10,5], text = ["foo", "bar", "baz"])
+
+mydata.text
+
+mydata.text[mydata.id .>= 10]
+```
+
+Main change from `Matrix`: *columns are labeled and their types differ*, also entries may be missing
+
+
+
+# DataFrames
+
+Popular way of importing data:
+```julia
+using CSV
+df = CSV.read("database.csv", DataFrame)       # can also do a Matrix
+
+CSV.write("backup.csv", df)
+```
+
+Popular among computer users:
+```julia
+using XLSX
+x = XLSX.readxlsx("important_results.xls")
+
+XLSX.sheetnames(x)
+
+DataFrame(XLSX.gettable(x["Results sheet"])...)
+```
+
+<small>(Please do not export data to XLSX.)</small>
+
+
+
+# Plotting
+
+<center>
+<img src="slides/img/unicodeplot.png" width="40%" />
+</center>
+
+
+
+# Usual plotting packages
+
+-   `UnicodePlots.jl` (useful in terminal, https://github.com/JuliaPlots/UnicodePlots.jl)
+-   `Plots.jl` (matplotlib workalike, works with Plotly)
+-   `GLMakie.jl` (interactive plots)
+-   `CairoMakie.jl` (PDF export of Makie plots)
+
+Native `ggplot` and `cowplot` ports are in development.
+
+Gallery available: https://makie.juliaplots.org

2022/2022-06-08_JuliaForNewcomers/slides/language.md

+
+<div class=leader>
+<i class="twa twa-blue-circle"></i>
+<i class="twa twa-red-circle"></i>
+<i class="twa twa-green-circle"></i>
+<i class="twa twa-purple-circle"></i><br>
+Julia language primer
+</div>
+
+
+
+# Expressions and types
+
+Expressions and types You can discover types of stuff using `typeof`.
+
+Common types:
+
+- `Bool`
+```julia
+false, true
+```
+- `Char`
+```julia
+'a', 'b', ...
+```
+- `String`
+```julia
+"some random text" 
+```
+- `Int`
+```julia
+1, 0, -1, ...
+```
+- `Float64`
+```julia
+ 1.1, 0, -1, ...
+```
+
+
+
+# Types may have parameters (usually "contained type")
+
+- `Vector{Int}`
+
+```julia
+[1, 2, 5, 10]
+```
+
+- `Matrix{Float64}`
+
+```julia
+[1.0 2.0; 2.0 1.0]
+```
+
+- `Tuple`
+
+```julia
+(1, 2.0, "SomeLabel") 
+```
+
+- `Set{Int}`
+- `Dict{Int,String}`
+
+(default parameter value is typically `Any`)
+
+
+
+# Basic functionality and expectable stuff
+
+- Math: `+`, `-`, `*`, `/`, `^`, ...
+
+- Logic: `==`, `!=`, `<`, `>`, `<=`, `>=`, `&&`, `||`, `!`, ...
+
+- Assignment: `=`, `+=`, `-=`, `*=`, ...
+
+- I/O: `open`, `println`, `read`, `readlines`, ...
+
+- Arrays: `array[1]`, `array[2:5]`, `array[begin+1:end-1]`, `size`, `length`, `cat`, `vcat`, `hcat`, ...
+
+Most functions are *overloaded* to *efficiently* work with multiple types of data.
+
+Functionality is easy to discover by just `Tab`bing the definitions, also `methods(...)` and `methodswith(...)`.
+
+
+
+# Control flow: Commands and code blocks
+
+Typically you write 1 command per 1 line.
+
+Commands can be separated by semicolons, and grouped using code blocks:
+
+```julia
+begin
+  a = 10
+  b = 20; b += 20
+  a + b                # implicit return!
+end
+```
+
+Many constructions (cycles, function definitions) start the block
+automatically, you only write `end`.
+
+
+
+# Control flow: Conditional execution
+
+-   Traditional `if`:
+
+```julia
+if condition
+  actions
+else        # optional
+  actions   # optional
+end
+```
+
+- Shorter inline condition:
+
+```julia
+myfunction( index<=10 ? array[index] : default_value )
+```
+
+- <i class="twa twa-light-bulb"></i> Useful shell-like shortcuts:
+
+```julia
+a <  0  &&  (a =  0)
+a > 10  &&  (a = 10)
+
+
+isfinite(a) ||  @error "a is infinite, program will crash!"
+```
+
+
+
+# Control flow: Doing stuff many times
+
+Iteration count-based loop:
+
+```julia
+for var = iterable   # , var2 = iterable2, ...
+  code(variable, variable2)
+  # ...
+end
+```
+
+Syntax with `in` instead of `=` is also supported.
+
+Examples:
+```julia
+for i = 1:10
+  @info "iterating!" i
+end
+
+for i = 1:10, j = 1:10
+  matrix[i,j] = i*j
+end
+```
+
+Utilities: `eachindex`, `enumerate`
+
+
+
+# Control flow: Doing stuff many times
+
+Condition satisfaction-based loop:
+
+```julia
+    while condition
+      do_something() # condition is true
+    end
+    # condition is false
+```
+
+Example:
+```julia
+    number = 123519
+    digit_sum = 0
+    while number > 0
+      digit_sum += number % 10
+      number ÷= 10 
+    end
+    @info "We've got results!" digit_sum
+```
+
+
+
+<div class=leader>
+<i class="twa twa-hot-beverage"></i><i class="twa twa-bubble-tea"></i><i class="twa twa-beverage-box"></i><br>
+Short break
+</div>
+
+Let's have *10 minutes* for a coffee or something.
+
+(Questions?)
+
+
+
+<div class=leader>
+<i class="twa twa-blue-circle"></i>
+<i class="twa twa-red-circle"></i>
+<i class="twa twa-green-circle"></i>
+<i class="twa twa-purple-circle"></i><br>
+Julia language primer<br>(part 2)
+</div>
+
+
+
+# <i class="twa twa-light-bulb"></i> Structured cycles!
+
+Using functional-style loops is *much less error-prone* to indexing
+errors.
+
+- Transform an array:
+
+```julia
+map(sqrt, [1,2,3,4,5])
+map((x,y) -> (x^2 - exp(y)), [1,2,3], [-1,0,1])
+```
+
+- Summarize an array:
+
+```julia
+reduce(+, [1,2,3,4,5])
+reduce((a,b) -> "$b $a", ["Use", "the Force", "Luke"])
+reduce(*, [1 2 3; 4 5 6], dims=1)
+```
+
+**Tricky question (<i class="twa twa-light-bulb"></i><i class="twa twa-light-bulb"></i><i class="twa twa-light-bulb"></i>):** What is the overhead of the "nice" loops?
+
+
+
+# Making new arrays with loops
+
+```julia
+julia> [i*10 + j for i = 1:3, j = 1:5]
+3×5 Matrix{Int64}:
+11   12   13   14   15
+21   22   23   24   25
+31   32   33   34   35
+
+julia> join(sort([c for word in ["the result is 123", "what's happening?", "stuff"]
+                    for c in word
+                    if isletter(c)]))
+"aaeeeffghhhiilnnpprssssttttuuw"
+```
+
+
+
+# Control flow: subroutines (functions)
+
+- Multi-line function definition
+
+```julia
+function combine(a,b)
+  return a + b
+end
+```
+
+- "Mathematical" neater definition
+
+```julia
+combine(a,b) = a + b
+```
+
+- <i class="twa twa-light-bulb"></i> Definition with types specified (prevents errors, allows optimizations!)
+
+```julia
+function combine(a::Int, b::Int)::Int
+    return a + b
+end
+```
+
+
+
+# <i class="twa twa-light-bulb"></i><i class="twa twa-light-bulb"></i> Control flow: subroutine overloading (methods)
+
+- A method for combining integers
+
+```julia
+combine(a::Int, b::Int)::Int = a + b
+```
+
+- A method of the "same function" for combining strings
+
+```julia
+combine(a::String, b::String)::String = "$a and $b"
+```
+
+
+
+# <i class="twa twa-light-bulb"></i><i class="twa twa-light-bulb"></i><i class="twa twa-light-bulb"></i>  Supertype hierarchy
+
+Types possess a single supertype, which allows you to easily group
+multiple types under e.g. `Real`, `Function`, `Type`, `Any`, ...
+
+This creates *groups of types* that are useful for restricting your functions to work on the most reasonable subsets of inputs.
+
+<pre style="font-size: 80%; line-height:120%;"><code class="language-julia hljs">
+julia> Int
+Int64
+
+julia> Int.super
+Signed
+
+julia> Int.super.super
+Integer
+
+julia> Int.super.super.super
+Real
+
+julia> Int.super.super.super.super
+Number
+
+julia> Int.super.super.super.super.super
+Any
+
+</code></pre>
+
+(Upon calling the function, Julia picks the *most specific* available method.)
+
+
+
+# Function arguments
+
+-   Keyword arguments (can not be used for overloading)
+
+```julia
+function f(a, b=0; extra=0)
+  return a + b + extra
+end
+
+f(123, extra=321)
+```
+
+- <i class="twa twa-light-bulb"></i> Managing arguments en masse
+
+```julia
+euclidean(x; kwargs...) = sqrt.(sum(x.^2; kwargs...))
+
+max_squared(args...) = maximum(args .^ 2)
+```
+
+
+
+# Broadcasting over iterable things
+
+-   Broadcasting operators by prepending a dot
+
+```julia
+matrix[row, :] .+= vector1 .* vector2
+```
+
+-   Broadcasting a function
+
+```julia
+sqrt.(1:10)
+maximum.(eachcol(rand(100,100)))
+
+x = [1,2,3,4]
+x' .* x
+```
+
+<i class="twa twa-light-bulb"></i> The "magic dot" is a shortcut for calling `broadcast(...)`.
+
+
+
+# Advanced container types
+
+- Dictionaries (`Dict{KeyType, ValueType`) allow O(log n) indexing, great for
+  lookups or keyed data structures. Contents may be typed for increased
+  efficiency.
+
+```julia
+person = Dict("name" => "John", "surname" => "Foo", "age" => 30)
+person["age"]
+
+indexof(v::Vector) = Dict(v .=> eachindex(v))
+```
+
+- <i class="twa twa-light-bulb"></i> Sets are key-only containers (keys are _unique_)
+
+```julia
+julia> x=Set([1,2,3,2,1]);
+julia> println(x)
+Set([2, 3, 1])
+
+julia> push!(x,5);
+julia> push!(x,5);
+julia> println(x)
+Set([5, 2, 3, 1])
+```

2022/2022-06-08_JuliaForNewcomers/slides/list.json

+[
+    { "filename": "index.md" },
+    { "filename": "overview.md" },
+    { "filename": "intro.md" },
+    { "filename": "bootstrap.md" },
+    { "filename": "language.md" },
+    { "filename": "io.md" },
+    { "filename": "distributed.md" },
+    { "filename": "thanks.md" }
+]

2022/2022-06-08_JuliaForNewcomers/slides/overview.md

+# Overview
+
+1. Why would you learn another programming language again?
+2. Bootstrapping, working with packages, writing a script (30m)
+3. Language and syntax primer (35m) with a pause (10m) in the middle
+4. Getting the data in and out (15m)
+5. Running programs on ULHPC (15m)
+6. Questions, hands-on, time buffer (15m)

2022/2022-06-08_JuliaForNewcomers/slides/thanks.md

+
+# Thank you!
+
+<center><img src="slides/img/r3-training-logo.png" height="200px"></center>
+
+Contact us if you need help:
+
+<a href="mailto:lcsb-r3@uni.lu">lcsb-r3@uni.lu</a>
+