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Contents:

  • An introduction to Python
    • Running Python
    • Scripting
      • argparse
    • 1: Basics
    • Jupyter
      • Basic types and operations
      • strong typing
      • Container types
      • Mutability
      • dynamic typing
    • Assignement and variables
      • Python variable assignement
      • Sugar: comprehensions
      • Sugar: using Markdown
    • Objects and operators
      • Objects
    • Numbers
    • Strings
      • Formatting
    • Lists and looping
      • Looping
      • List comprehension
      • Tuples
    • Dictionaries
      • Dictionary keys
    • Conditions
      • Truthiness
      • Conditions in loops
    • Functions
      • Inline methods
    • Scripting
      • argparse
    • Modules
      • Using modules into your code: import
      • The standard library
      • Modules from PyPi
      • Modules inside a virtual environment
      • Write your first Python module
      • Write a structured module
      • Run a module
    • Classes
      • Welcome to classes
      • Inheritance: a glance
        • How to fix
    • Learning more
      • Exploring Python
      • Conventional coding
    • Making your first histogram
      • Pandas
      • Plotting histograms
      • Applying cuts
    • More advanced topics in Python
      • Nice standard libraries
      • Nice libraries for data analysis
      • Python and ROOT
  • Advanced Python Tutorial
    • 1: Basics
      • Basics
      • Markdown
      • Jupyter
      • Importing modules
    • Advanced Python Concepts
      • Packing and unpacking of values
      • Context manager
        • Using yield
      • Using a class
      • Decorators and factories
        • Decorator
      • Exceptions
        • Custom Exception
      • Catching exceptions
        • pitfall “guaranteed execution”
      • Exceptions as control-flow
    • Advanced Classes
      • Dunder
      • len
      • str
      • Callable
      • Indexing (iterating)
      • self
    • Danger zone
    • 2: First look at data
      • Two plotting libraries?
      • Recap: Importing modules
        • 5. The toy dataset
      • Loading data
        • 6. Plotting a simple histogram
      • Adding variables
        • Using rectangular cuts
      • Comparing distributions
    • 3: Multivariate Analysis
      • Using a classifier
        • TODO Add a diagram of a decision tree for the above plot
    • 4: Extension on Classification
      • Alternative implimentations
      • Feature engineering
      • \(k\)-folding
      • Turn this into a scipt using argparse
    • 5: Boosting to Uniformity
      • Loading data
        • Distributions in the Dalitz features for signal and background
        • Preparation of train/test datasets
        • Setting up classifiers, training
        • Let’s look at the results of training
        • ROC curves after training
    • Model tuning setup
    • Cross-validation
    • \(k\)-folding & early stopping
    • Hyperameter optimisation
    • 6: Histograms
      • Axes
        • Regular
        • Variable
      • Axis Name
        • Compatibility with mplhep
        • Plotting with hist
      • Multiple dimensions
      • Access Bins
      • Getting Density
      • Projecting axes
      • Accessing everything relevant
        • Multi dimensional
      • Arithmetics
      • Weights
    • 7: Demonstration of distribution reweighting
      • Downloading data
      • prepare train and test samples
        • Original distributions
      • train part of original distribution
      • test part for target distribution
      • Bins-based reweighting in n dimensions
      • Gradient Boosted Reweighter
        • Comparing some simple expressions:
      • GB-discrimination
        • Great!
        • What did just happen?
      • How to tune
      • Folding reweighter
      • GB discrimination for reweighting rule
    • 8: Likelihood inference
      • Scope of this tutorial
      • Getting started
      • Difference of the two spaces
      • Plotting
      • Loss
        • Fixing parameters
    • 9: sPlot
      • Simple sPlot example
      • Observed distributions
      • Applying sWeights
        • Compare
      • More complex case
      • Splot
      • Alternative: Known probabilities
        • Building sPlot over mass
        • Of course we don’t have labels which events are signal and which are background beforehand
        • We have no information about real labels
      • Fitting doesn’t give us information about real labels
      • Appying sPlot
      • Using sWeights to reconstruct initial distribution
      • An important requirement of sPlot
      • Derivation of sWeights (optional)
        • Under assumption of linearity:
        • Minimization of variation
        • Uncorrelatedness
      • Conclusion
    • 10: Scikit-HEP
      • formulate - converting expressions
      • Particle
      • hepunits
      • Vector
        • Vector properties
  • Introducing the Shell
    • Background
    • The Command-Line Interface
    • The Shell
    • Why bother?
    • Nelle’s Pipeline: Starting Point
      • Navigating Files and Directories
        • Nelle’s Pipeline: Organizing Files
      • Working With Files and Directories
      • Pipes and Filters
        • Nelle’s Pipeline: Checking Files
      • Loops
      • Shell Scripts
      • Finding Things
  • UNIX shell
    • Using screen to keep things running
    • Advanced screen topics
      • Finding lost screens
      • Using tabs in screen
    • Persistent screen or tmux session on lxplus
      • lxplus7
        • Setting up password-less kerberos token on lxplus7
        • Making use of the keytab on lxplus7
        • Using k5reauth to automatically refresh your kerberos token on lxplus7
      • lxplus8
      • lxplus9
    • More about the UNIX shell
      • Types of shell
      • Manual pages
      • Environment variables
      • Variables
      • Differences among files
      • Looping over files
      • Conditionals
      • Linking commands
      • Pipes and redirection
      • Bash security
      • Complexity
      • Text viewers
      • Text editors
      • Disk space
      • Over the Wire and Bandit wargame
  • Analysis automation with Snakemake
    • Documentation and environments
    • Workflow preservation
    • Basic Tutorial
      • What is a workflow?
      • Why use a workflow management system?
      • Introducing Snakemake
      • Re-running rules
      • Chaining rules
      • The limits of wildcards
    • Advanced Tutorial
      • Running scripts
      • Log files
      • Config files
      • Includes
  • Git
    • Automated Version Control
    • Setting Up Git
    • Creating a Repository
    • Tracking Changes
    • Exploring History
    • Ignoring Things
    • Remotes in CERN GitLab
    • Sharing a repository with others
    • Collaborating with Pull Requests
      • What is a Pull (or Merge) Request
      • Fork the original project repository
      • Clone a remote project and its fork
      • Sync your local repository with remote changes
      • Implement your new feature
      • Push changes
      • Create a Pull (or Merge) Request
      • Discussing, amending, retiring a Merge Request
      • Accepting a Pull Request
      • The social side of coding
      • Automatic testing
    • Conflicts
    • GitLab CI
    • Open Science
    • Licensing
    • Citation
  • Contributing
    • Contributor Agreement
    • How to Contribute
    • What to Contribute
    • Using GitHub
    • Other Resources
  • Contributor Code of Conduct
  • Instructional Material
  • Software
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© Copyright HSF CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode) - Originally based on Software Carpentry (https://github.com/swcarpentry/) © 2016–2017 Software Carpentry Foundation.

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