Training materials for Next Collaboration in IFIC Valencia
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bcolz
best_practices
data
hdf5
iterators_contexts_decorators
matplotlib
numpy-scipy
optimization
pandas
testing_code
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README.rst
requisites.txt

README.rst

Training on Advanced Python for Scientists

When:17-19 October 2016
Where:Institut de Física Corpuscular (Centre mixt Universitat de València / CSIC)
Who:Next experiment

Day 1

  • Introduction to the course

    Estimated time: 1 hour

    • Distribution of materials and description of the software environment.
    • Description of the scope and schedule.
    • Jupyter: merging code and data for reproducibility.
  • NumPy: the basic building block of every scientific application

    Estimated time: 1 hour

Break: 30 min
  • Advanced NumPy

    Estimated time: 1 hour

Lunch: 1 hour
  • SciPy: advanced toolset on top of NumPy

    Estimated time: 1h

Break: 30 min
  • Matplotlib: visualizing your data (with exercises)

    Estimated time: 1h45min

    • Matplotlib
  • General questions on what was learned during the day

    Estimated time: 15 min

Day 2

  • Intermediate Python

    Estimated time: 1 hour

    • Iterators, generators, contexts, decorators
    • Packaging for distributing
  • Best practices in coding

    Estimated time: 30 min

    • PEP 8
    • PyFlakes
    • PyLint
Break: 30 min
  • Unit testing

    Estimated time: 1h30m

Lunch: 1 hour
  • Optimizing Python code and linking with C/C++ (lecture)

    Estimated time: 1h15m

    • Numba
    • Cython
    • pybind11
Break: 30 min
  • Optimizing Python code and linking with C/C++ (exercises)

    Estimated time: 1:30 hour

  • General questions on what was learned during the day

    Estimated time: 15 min

Day 3

  • On-disk Data Management (lecture and hands on)

    Estimated time: 3 hours (including 30 min break)

    • HDF5/PyTables
    • Applied exercises based on real-life datasets
Lunch: 1 hour
  • In-memory Data Management (lecture and hands on)

    Estimated time: 2 hours

    • pandas (tabular datasets, import from CSV, text, Excel, HDF5)
    • bcolz (compressed tabular datasets)
Break: 30 min
  • Closing

    Estimated time: 30 min

    • Overview
    • General questions on what was learned during the training