Tutorials marked with an asterisk are available to Caltech personnel only. This is because they contain unpublished information that the researchers who generously supplied the data would prefer be shared in their own publication. Apologies to non-Caltech people who may otherwise find these tutorials useful.
Similarly, many of the data sets are also only available to Caltech personnel. This is because many of them are unpublished and/or being used for ongoing research.
- Tutorial 0
- [Before first class] Setting up a Python environment for scientific computing
- Tutorial 1a
- [09/29] Introduction to Python
- Tutorial 1b
- [09/29] Loading and displaying data (data set)
- Tutorial 2a*
- [10/06] Managing data sets (data set)
- Tutorial 2b*
- [10/06] Defining parameters and estimating them
- Tutorial 3a
- [10/13] Model generation and checking (data set)
- Tutorial 3b
- [10/13] Linear regression
- Tutorial 4a
- [10/27] Nonlinear regression (data set 1, data set 2)
- Tutorial 4b
- [10/27] Model selection (data set)
- Tutorial 5a
- [11/03] Outlier detection (data set)
- Tutorial 5b
- [11/03] Data smoothing (data set)
- Tutorial 6a*
- [11/10] Prepping images (data sent via Dropbox)
- Tutorial 6b
- [11/10] A case study in FRAP (data sent via Dropbox)
- Tutorial 7a*
- [11/17] Basic filtering and thresholding (data sent via Dropbox)
- Tutorial 7b*
- [11/17] Segmentation (data sent via Dropbox)
- Tutorial 8a
- [11/24] Particle tracking (data set)
- Tutorial 8b
- [11/24] Cross correlation and PIV (data set same as Tutorial 8a)
- Tutorial 9*
- [12/01] Colocalization (data set)