daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Updated to specify Jupyter, not ipython.

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Roger Labbe 2016-09-25 11:30:01 -07:00
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#[Kalman and Bayesian Filters in Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python)
Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written in IPython Notebook (now known as Jupyter) so that you can run and modify the code in the book in place, seeing the results inside the book. What better way to learn?
Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your browser. What better way to learn?
**"Kalman and Bayesian Filters in Python" looks amazing! ... your book is just what I needed** - Allen Downey, Professor and O'Reilly author of several math and programming textbooks, via twitter.
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Follow the instructions in **Installation and Software Requirements** below to install all the supporting sofware require. Then, navigate to the directory, and run IPython notebook with the command
ipython notebook
jupter notebook
This will open a browser window showing the contents of the base directory. The book is organized into chapters. To read Chapter 2, click on the link for chapter 2. This will cause the browser to open that subdirectory. In each subdirectory there will be one or more IPython Notebooks (all notebooks have a .ipynb file extension). The chapter contents are in the notebook with the same name as the chapter name. There are sometimes supporting notebooks for doing things like generating animations that are displayed in the chapter. These are not intended to be read by the end user, but of course if you are curious as to how an animation is made go ahead and take a look.
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FilterPy is hosted github at (https://github.com/rlabbe/filterpy). If you want the bleading edge release you will want to grab a copy from github, and follow your Python installation's instructions for adding it to the Python search path. However, it is quite possible that it will not be compatible with the notebooks in this project. I try to make sure the notebooks are in sync with the PyPi hosted version.
Code that is specific to the book is stored with the book in the subdirectory ./*code*. This code is in a state of flux; I do not wish to document it here yet. I do mention in the book when I use code from this directory, so it should not be a mystery.
In the *code* subdirectory there are Python files with a name like *xxx*_internal.py. I use these to store functions that are useful for a specific chapter. This allows me to hide away Python code that is not particularly interesting to read - I may be generating a plot or chart, and I want you to focus on the contents of the chart, not the mechanics of how I generate that chart with Python. If you are curious as to the mechanics of that, just go and browse the source.
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There is an undocumented directory called **experiments**. This is where I write and test code prior to putting it in the book. There is some interesting stuff in there, and feel free to look at it. As the book evolves I plan to create examples and projects, and a lot of this material will end up there. Small experiments will eventually just be deleted. If you are just interested in reading the book you can safely ignore this directory.
The directory **code** contains a css file containing the style guide for the book. The default look and feel of IPython Notebook is rather plain. Work is being done on this. I have followed the examples set by books such as [Probabilistic Programming and Bayesian Methods for Hackers](http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Chapter1.ipynb). I have also been very influenced by Professor Lorena Barba's fantastic work, [available here](https://github.com/barbagroup/CFDPython). I owe all of my look and feel to the work of these projects.
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