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