daviddoji/Kalman-and-Bayesian-Filters-in-Python
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Removed links to PDF.

Browse Source 
I removed the PDF from the repository because git does poorly with
binary objects.
This commit is contained in:
Roger Labbe 2015-05-19 20:12:32 -07:00
parent 8a9ecc026e
commit 26fe2968e4
1 changed files with 17 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

40
README.md
View File

@ -5,33 +5,20 @@ Introductory textbook for Kalman and Bayesian filters. All code is written in Py
Reading Online
-----
Github is now able to render the notebooks directly. The quickest way to view a notebook is to just click on them above. Chapters are named 01_XXX, 02_XXX, and so on. Just go in order to read the book, or pick whatever chapter you want.
Github is now able to render the notebooks directly. The quickest way to view a notebook is to just click on them above. Chapters names are prefaced with numbers to indicate their order 01_gh_filter.ipynb, and so on. J
Alternatively You may access this book via nbviewer at any time by using this address. This seems to render the books more quickly than Github.
Alternatively You may access this book via nbviewer at any time by using this address.
[*Read Online Now*](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)
[Online PDF Version](http://rlabbe.github.io/Kalman-and-Bayesian-Filters-in-Python/Kalman_and_Bayesian_Filters_in_Python.pdf)
The quickest way to get starting with reading the book is to read it online using Github or one of the links above. The book is written as a collection of IPython Notebooks, an interactive, browser based system that allows you to combine text, Python, and math into your browser. The website http://nbviewer.org provides an IPython Notebook server that renders notebooks stored at github (or elsewhere). The rendering is done in real time when you load the book. If you read my book today, and then I make a change tomorrow, when you go back tomorrow you will see that change. Perhaps more importantly, the book uses animations to demonstrate how the algorithms perform over time. The PDF version of the book, discussed in the next paragraph, cannot show the animations.
The book is written as a collection of IPython Notebooks, an interactive, browser based system that allows you to combine text, Python, and math into your browser. The website http://nbviewer.org provides an IPython Notebook server that renders notebooks stored at github (or elsewhere). The rendering is done in real time when you load the book. If you read my book today, and then I make a change tomorrow, when you go back tomorrow you will see that change. Perhaps more importantly, the book uses animations to demonstrate how the algorithms perform over time.
The preface available from the link above has all the information in this README and more, so feel free to follow the link now.
I periodically generate a PDF of the book from the Notebooks. I do not do this for every check in, so the PDF will usually lag the content in github and on nbviewer.org. However, I do generate it whenever I make a substantial change. Of course, you will not be able to run and modify the code in the notebooks, nor will you be able to see the animations.
[*PDF Version of the book*](http://rlabbe.github.io/Kalman-and-Bayesian-Filters-in-Python/Kalman_and_Bayesian_Filters_in_Python.pdf)
Companion Software
-----
All of the filters used in this book as well as others not in this book are implemented in my Python library FilterPy, available [here](https://github.com/rlabbe/filterpy). You do not need to download or install this to read the book, but you will likely want to use this library to write your own filters. It includes Kalman filters, Fading Memory filters, H infinity filters, Extended and Unscented filters, least square filters, and many more. It also includes helper routines that simplify the designing the matrices used by some of the filters, and other code such as Kalman based smoothers.
Downloading the book
-----
**Breaking change: I have upgraded to IPython 3.0. This release alters the notebook format (.ipynb) files. If you are running an older version you will likely get the unhelpful error message "Bad request" when you try to open the notebook. Note that this is the version number for _IPython_, which provides the IPython Notebook software, and not the Python version. I.e. you can run these notebooks with Python 2.7, so long as you have IPython 3.0 installed. IPython 3.0 was released on Febuary 27, 2015, so if your install is later than that you will have to update IPython. IPython 2.4 can read the files, but not write them. I apologize if you are using an earlier version, but this is an unavoidable change and I'd rather change now instead of later. This will not affect you if you are reading online, only if you are running the notebooks on your local computer. Please note that this has nothing to do with Python 3 - you can run Python 2.7 in IPython 3.**
However, this book is intended to be interactive and I recommend using it in that form. If you install IPython on your computer and then clone this book you will be able to run all of the code in the book yourself. You can perform experiments, see how filters react to different data, see how different filters react to the same data, and so on. I find this sort of immediate feedback both vital and invigorating. You do not have to wonder "what happens if". Try it and see!
The github pages for this project are at https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python You can clone it to your hard drive with the command
@ -39,14 +26,25 @@ The github pages for this project are at https://github.com/rlabbe/Kalman-and-Ba
git clone https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python.git
This will create a directory named Kalman-and-Bayesian-Filters-in-Python. Navigate to the directory, and run IPython notebook with the command
This will create a directory named Kalman-and-Bayesian-Filters-in-Python.
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
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 browwer 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.
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.
This is admittedly a somewhat cumbersome interface to a book; I am following in the footsteps of several other projects that are somewhat repurposing IPython Notebook to generate entire books. I feel the slight annoyances have a huge payoff - instead of having to download a separate code base and run it in an IDE while you try to read a book, all of the code and text is in one place. If you want to alter the code, you may do so and immediately see the effects of your change. If you find a bug, you can make a fix, and push it back to my repository so that everyone in the world benefits. And, of course, you will never encounter a problem I face all the time with traditional books - the book and the code are out of sync with each other, and you are left scratching your head as to which source to trust.
**Breaking change: I have upgraded to IPython 3.0. This release alters the notebook format (.ipynb) files. If you are running an older version you will likely get the unhelpful error message "Bad request" when you try to open the notebook. Note that this is the version number for _IPython_, which provides the IPython Notebook software, and not the Python version. I.e. you can run these notebooks with Python 2.7, so long as you have IPython 3.0 installed. IPython 3.0 was released on Febuary 27, 2015, so if your install is later than that you will have to update IPython. IPython 2.4 can read the files, but not write them. I apologize if you are using an earlier version, but this is an unavoidable change and I'd rather change now instead of later. This will not affect you if you are reading online, only if you are running the notebooks on your local computer. Please note that this has nothing to do with Python 3 - you can run Python 2.7 in IPython 3.**
Companion Software
-----
All of the filters used in this book as well as others not in this book are implemented in my Python library FilterPy, available [here](https://github.com/rlabbe/filterpy). You do not need to download or install this to read the book, but you will likely want to use this library to write your own filters. It includes Kalman filters, Fading Memory filters, H infinity filters, Extended and Unscented filters, least square filters, and many more. It also includes helper routines that simplify the designing the matrices used by some of the filters, and other code such as Kalman based smoothers.
In Development
--------------
@ -74,10 +72,6 @@ This book has supporting libraries for computing statistics, plotting various th
Finally, this book is free. The cost for the books required to learn Kalman filtering is somewhat prohibitive even for a Silicon Valley engineer like myself; I cannot believe the are within the reach of someone in a depressed economy, or a financially struggling student. I have gained so much from free software like Python, and free books like those from Allen B. Downey [here](http://www.greenteapress.com/). It's time to repay that. So, the book is free, it is hosted on free servers, and it uses only free and open software such as IPython and mathjax to create the book.
Installation and Software Requirements
-----
@ -85,7 +79,7 @@ Installation and Software Requirements
If you want to run the notebook on your computer, which is what I recommend, then you will have to have IPython installed. I do not cover how to do that in this book; requirements change based on what other Python installations you may have, whether you use a third party package like Anaconda Python, what operating system you are using, and so on.
To use all features you will have to have IPython 2.0 installed, which is released and stable as of April 2014. Most of the book does not require that recent of a version, but I do make use of the interactive plotting widgets introduced in this release. A few cells will not run if you have an older version installed. This is merely a minor annoyance.
To use all features you will have to have IPython 3.0 or later installed, which is released and stable as of April 2014. Most of the book does not require that recent of a version, but I do make use of the interactive plotting widgets introduced in this release. A few cells will not run if you have an older version installed. This is merely a minor annoyance.
You will need Python 2.7 or later installed. Almost all of my work is done in Python 3.4, but I periodically test on 2.7. I do not promise any specific check in will work in 2.7 however. I do use Python's "from \_\_future\_\_ import ..." statement to help with compatibility. For example, all prints need to use parenthesis. If you try to add, say, "print 3.14" into the book your script will fail; you must write "print (3.4)" as in Python 3.X.