Python tutorial @ FHI-PC-SESD

Course materials for the internal python tutorial at Fritz Haber Institute's Physical Chemistry department
Lecturers: Patrick Xian, Faruk Krecinic
Duration: 4 lectures, each 1.5-2 hrs

Rosetta stone for Matlab users picking up Python

0. System configuration:

1. python installation (installed Anaconda distribution before class)
2. package installation
3. git

1. Python basics (incl. the standard library) ▶️

1. python data types and operations
2. sys/os/glob/glob2
3. time py2 | py3
4. itertools py2 | py3
5. functional vs. object-oriented programming in python
6. file i/o in scipy, pandas, h5py

2. Development environment and platforms ▶️

1. Jupyter
2. Spyder
3. PyCharm
4. Atom

3. Python numerics stack ▶️

1. numpy (matrix calculation)
2. scipy (numerical methods, signal processing)
3. pandas (time series, panel data)
4. sympy (symbolic calculation)
5. mpmath (arbitrary-precision calculation)

4. Python visualization & interactivity ▶️

1. matplotlib (2D)
2. seaborn (2D stats)
3. mayavi (3D)
4. ipywidgets/bokeh/plotly (interactivity)

5. Advanced python ▶️

1. PyQt (GUI incl. DaX interface)
2. code profiling in Jupyter and using line_profiler
3. multiprocessing py2 | py3 (parallel computation)
4. cython (compiled python)
5. python 2.7 vs python 3.5 (short summary)
6. interfacing w/ other languages (Matlab/Julia/C(++)/Fortran)

6. Custom python packages for experimental analysis ▶️

1. xarray (nD array)
2. lmfit/mystic/cvxpy (nonlinear regression, convex optimization)
3. deap (evolutionary algorithm)
4. scikit-image (image analysis)
5. scikit-learn (machine learning)
6. pyFAI (fast radial integrator)
7. larch (XAFS)
8. diffpy (diffraction)
9. mpes