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Added link to table of contents to all notebooks.

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this is so the user of nbviewer can navigate the book more easily.
This commit is contained in:
Roger Labbe 2014-09-01 20:53:44 -07:00
parent 07803040b0
commit 3f50d42d88
17 changed files with 137 additions and 34 deletions
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9
Appendix_A_Installation/Appendix_Installation.ipynb
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9
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Chapter06_Multivariate_Kalman_Filter/Multivariate_Kalman_Filters.ipynb
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9
Chapter07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb
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7
Introduction.ipynb
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Preface.ipynb
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"\n",
"You may access this book via nbviewer at any by using this address:\n",
"\n",
"http://nbviewer.ipython.org/github/rlabbe/Kalman-Filters-and-Random-Signals-in-Python/blob/master/Introduction.ipynb\n",
"http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/Preface.ipynb\n",
"\n",
"\n",
"If you prefer a PDF, it is available [here](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/Kalman_and_Bayesian_Filters_in_Python.pdf). I have to generate this PDF manually from the IPython Notebook, and I do not do that for every check in. During the book's development this PDF will often be somewhat out of date. I recommend the nbviewer version above if you can be online while reading.\n",

10
merge_book.py
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def remove_links(nb):
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return
def merge_notebooks(filenames):
merged = None
@ -20,6 +29,7 @@ def merge_notebooks(filenames):
with io.open(fname, 'r', encoding='utf-8') as f:
nb = current.read(f, u'json')
remove_formatting(nb)
remove_links(nb)
if merged is None:
merged = nb
else:

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toc.ipynb → table_of_contents.ipynb
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@ -17,11 +17,6 @@
" <p>\n",
"Table of Contents\n",
"-----\n",
"[**Preface**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Preface.ipynb)\n",
"\n",
"Motivation for the book. Where to download, how to use.\n",
"\n",
"** add something about teaching methodology. exploration vs regurgitation.\n",
"\n",
"[**Introduction**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Introduction.ipynb)\n",
" \n",
@ -29,58 +24,58 @@
"Yes, it is more or less the preface restated. will edit and delete one or the other.\n",
"\n",
"\n",
"[**Chapter 1: The g-h Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/g-h_filter.ipynb)\n",
"[**Chapter 1: The g-h Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter01_gh_filter/g-h_filter.ipynb)\n",
"\n",
"Intuitive introduction to the g-h filter, which is a family of filters that includes the Kalman filter. Not filler - once you understand this chapter you will understand the concepts behind the Kalman filter. \n",
"\n",
"\n",
"[**Chapter 2: The Discrete Bayes Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/discrete_bayes.ipynb)\n",
"[**Chapter 2: The Discrete Bayes Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter02_Discrete_Bayes/discrete_bayes.ipynb)\n",
"\n",
"Introduces the Discrete Bayes Filter. From this you will learn the probabilistic reasoning that underpins the Kalman filter in an easy to digest form.\n",
"\n",
"\n",
"[**Chapter 3: Least Squares Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Least_Squares_Filters.ipynb)\n",
"[**Chapter 3: Least Squares Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter03_Least_Squares/Least_Squares_Filters.ipynb)\n",
"\n",
"Introduces the least squares filter in batch and recursive forms.\n",
"\n",
"\n",
"[**Chapter 4: Gaussian Probabilities**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Gaussians.ipynb)\n",
"[**Chapter 4: Gaussian Probabilities**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter04_Gaussians/Gaussians.ipynb)\n",
"\n",
"Introduces using Gaussians to represent beliefs. Gaussians allow us to implement the algorithms used in the Discrete Bayes Filter to work in continuous domains.\n",
"\n",
"\n",
"[**Chapter 5: One Dimensional Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Kalman_Filters.ipynb)\n",
"[**Chapter 5: One Dimensional Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter05_Kalman_Filters/Kalman_Filters.ipynb)\n",
"\n",
"Implements a Kalman filter by modifying the Discrete Bayesian Filter to use Gaussians. This is a full featured Kalman filter, albeit only useful for 1D problems. \n",
"\n",
"\n",
"[**Chapter 6: Multivariate Kalman Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Multivariate_Kalman_Filters.ipynb)\n",
"[**Chapter 6: Multivariate Kalman Filter**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter06_Multivariate_Kalman_Filter/Multivariate_Kalman_Filters.ipynb)\n",
"\n",
"We extend the Kalman filter developed in the previous chapter to the full, generalized filter. \n",
"\n",
"\n",
"[**Chapter 7: Kalman Filter Math**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Kalman_Filter_Math.ipynb)\n",
"[**Chapter 7: Kalman Filter Math**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter07_Kalman_Filter_Math/Kalman_Filter_Math.ipynb)\n",
"\n",
"We gotten about as far as we can without forming a strong mathematical foundation. This chapter is optional, especially the first time, but if you intend to write robust, numerically stable filters, or to read the literature, you will need to know this.\n",
"\n",
"\n",
"[**Chapter 8: Designing Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Designing_Kalman_Filters.ipynb)\n",
"[**Chapter 8: Designing Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb)\n",
"\n",
"Building on material in Chapter 6, walks you through the design of several Kalman filters. Discusses, but does not solve issues like numerical stability.\n",
"\n",
"\n",
"[**Chapter 9: Extended Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Extended_Kalman_Filters.ipynb)\n",
"[**Chapter 9: Extended Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter09_Extended_Kalman_Filters/Extended_Kalman_Filters.ipynb)\n",
"\n",
"Kalman filter as covered only work for linear problems. Extended Kalman filters (EKF) are the most common approach to linearizing non-linear problems.\n",
"\n",
"\n",
"[**Chapter 10: Unscented Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Unscented_Kalman_Filter.ipynb)\n",
"[**Chapter 10: Unscented Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb)\n",
"\n",
"\n",
"Unscented Kalman filters (UKF) are a recent development in Kalman filter theory. They allow you to filter nonlinear problems without requiring a closed form solution like the Extended Kalman filter requires.\n",
"\n",
"\n",
"[**Chapter 11: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Designing_Nonlinear_Kalman_Filters.ipynb)\n",
"[**Chapter 11: Designing Nonlinear Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Chapter11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb)\n",
"\n",
"\n",
"[**Chapter 12: H-Infinity Filters**](not implemented)\n",
@ -109,13 +104,13 @@
"description\n",
"\n",
"\n",
"[**Appendix: Installation, Python, NumPy, and filterpy**](not implemented)\n",
"[**Appendix: Installation, Python, NumPy, and filterpy**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix_A_Installation/Appendix_Installation.ipynb)\n",
"\n",
"Brief introduction of Python and how it is used in this book. Description of the companion\n",
"library filterpy. \n",
" \n",
"\n",
"[**Appendix: Symbols and Notations**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix_Symbols_and_Notations.ipynb)\n",
"[**Appendix: Symbols and Notations**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/Appendix_B_Symbols_and_Notations/Appendix_Symbols_and_Notations.ipynb)\n",
"\n",
"Symbols and notations used in this book. Comparison with notations used in the literature.\n",
"\n",