Fitting models with scipy.optimize

Fitting models and testing the match of the models to the measured data is a fundamental activity in many fields of science. In this lesson, we will learn how to use non-linear optimization routines in scipy.optimize, to fit models to data.

Prerequisites

Learners need to know how to use Python and Numpy, to the level taught in the Software Carpentry novice lesson.

Getting ready

  1. Learners need to have the scipy library installed on their computers. If you have used your computer in a Software Carpentry workshop, it is likely that you already have it installed. You can test that by running the following in an IPython session:
import scipy

If this raises an error, you might need to run the following in the unix shell:

$ pip install scipy

Explanation: pip is a python ‘package manager’, which will go grab the necessary software for this lesson and install it on your machine. Another option is to use the conda package manager in the unix shell as follows:

$ conda install scipy
  1. You need to download some files to follow this lesson:

  • Make a new folder in your Desktop called scipy-optimize.

  • Download scipy-optimize-data.zip and move the file to this folder.

  • If it’s not unzipped yet, double-click on it to unzip it. You should end up with a new folder called scipy-optimize-data.

  • You can access this folder from the Unix shell with:

$ cd && cd Desktop/scipy-optimize/scipy-optimize-data

Topics

  1. Introduction: models and why we like them
  2. Linear models
  3. Nonlinear models and optimization
  4. Model evaluation with cross-validation
  5. Summary

Other Resources

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Instruction of this lesson was recorded on February 19th, 2016, as part of the Data Intensive Biology training program run by C. Titus Brown. You can view the video on youtube.

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