Fixed PDF output; blank Particle filter chapter.
The PDF was not setting the Preface as a unnumbered chapter. I think there is more work to get it's subsections unnumbered. Added empty Particl filter chapter. Had to renumber the chapters to make it fit in.
This commit is contained in:
Roger Labbe
parent
08123b0d15
commit
f4d218e1b4
305
13_Particle_Filters.ipynb
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305
13_Particle_Filters.ipynb
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@ -0,0 +1,305 @@
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"cell_type": "markdown",
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"source": [
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"[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Particle Filters"
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]
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},
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{
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" ol li{\n",
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" padding-left: 0.5em; \n",
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" }\n",
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" margin-bottom: 0;\n",
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" line-height: 125%;\n",
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" font-size: 100%;\n",
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" }\n",
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"</style>\n",
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"<script>\n",
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" MathJax.Hub.Config({\n",
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" TeX: {\n",
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" extensions: [\"AMSmath.js\"]\n",
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" },\n",
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" },\n",
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],
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"text/plain": [
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#format the book\n",
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"%matplotlib inline\n",
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"%load_ext autoreload\n",
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"%autoreload 2 \n",
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"from __future__ import division, print_function\n",
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"import matplotlib.pyplot as plt\n",
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"import book_format\n",
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"book_format.load_style()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.4.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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@ -1,13 +1,12 @@
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#! /bin/bash
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echo "merging book..."
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python merge_book.py > Kalman_and_Bayesian_Filters_in_Python.ipynb
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python merge_book.py
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echo "creating pdf..."
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ipython nbconvert --to latex --template book Kalman_and_Bayesian_Filters_in_Python.ipynb
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ipython nbconvert --to latex --template book book.ipynb
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ipython to_pdf.py
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mv Kalman_and_Bayesian_Filters_in_Python.pdf ..
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echo "done."
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@ -47,9 +47,10 @@ if __name__ == '__main__':
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'../10_Unscented_Kalman_Filter.ipynb',
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'../11_Extended_Kalman_Filters.ipynb',
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'../12_Designing_Nonlinear_Kalman_Filters.ipynb',
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'../13_Smoothing.ipynb',
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'../14_Adaptive_Filtering.ipynb',
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'../15_HInfinity_Filters.ipynb',
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'../16_Ensemble_Kalman_Filters.ipynb',
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'../13_Particle_Filters.ipynb',
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'../14_Smoothing.ipynb',
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'../15_Adaptive_Filtering.ipynb',
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'../16_HInfinity_Filters.ipynb',
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'../17_Ensemble_Kalman_Filters.ipynb',
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'../Appendix_A_Installation.ipynb',
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'../Appendix_B_Symbols_and_Notations.ipynb'])
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@ -4,10 +4,22 @@ import io
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import IPython.nbconvert.exporters.pdf as pdf
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import fileinput
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'''
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for line in fileinput.input('book.tex', openhook=fileinput.hook_encoded("iso-8859-1")):
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# print(line.replace('\chapter{Preface}', '\chapter*{Preface}'), end='')
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line.replace('\chapter{Preface}', '\chapter*{Preface}')
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#print(line.replace('\chapter{Preface}\label{preface}', '\chapter*{Preface}\label{preface}'), end='')
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# line.replace(' \chapter{Preface}\label{preface}', ' \chapter*{Preface}\label{preface}')
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line.replace('shit', 'poop')
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'''
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f = open('book.tex', 'r', encoding="iso-8859-1")
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filedata = f.read()
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f.close()
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newdata = filedata.replace('\chapter{Preface}', '\chapter*{Preface}')
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f = open('book.tex', 'w', encoding="iso-8859-1")
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f.write(newdata)
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f.close()
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p = pdf.PDFExporter()
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p.run_latex('book.tex')
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@ -77,24 +77,29 @@
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"Works through some examples of the design of Kalman filters for nonlinear problems. *This is still very much a work in progress.*\n",
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"\n",
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"\n",
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"[**Chapter 13: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13_Smoothing.ipynb)\n",
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"[**Chapter 13: Particle Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13_Particle_Filters.ipynb)\n",
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" \n",
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"Particle filters uses Monte Carlo techniques to filter data. They easily handle highly nonlinear and non-Gaussian systems, as well as multimodal distributions (tracking multiple objects simultaneously) at the cost of high computational requirements.\n",
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"\n",
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"\n",
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"[**Chapter 14: Smoothing**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Smoothing.ipynb)\n",
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"\n",
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"Kalman filters are recursive, and thus very suitable for real time filtering. However, they work extremely well for post-processing data. After all, Kalman filters are predictor-correctors, and it is easier to predict the past than the future! We discuss some common approaches.\n",
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"\n",
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"\n",
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"[**Chapter 14: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14_Adaptive_Filtering.ipynb)\n",
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"[**Chapter 15: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/15_Adaptive_Filtering.ipynb)\n",
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" \n",
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"Kalman filters assume a single process model, but manuevering targets typically need to be described by several different process models. Adaptive filtering uses several techniques to allow the Kalman filter to adapt to the changing behavior of the target.\n",
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"\n",
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"\n",
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"[**Chapter 15: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/15_HInfinity_Filters.ipynb)\n",
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"[**Chapter 16: H-Infinity Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/16_HInfinity_Filters.ipynb)\n",
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" \n",
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"Describes the $H_\\infty$ filter. \n",
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"\n",
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"*I have code that implements the filter, but no supporting text yet.*\n",
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"\n",
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"\n",
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"[**Chapter 16: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/16_Ensemble_Kalman_Filters.ipynb)\n",
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"[**Chapter 17: Ensemble Kalman Filters**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/17_Ensemble_Kalman_Filters.ipynb)\n",
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"\n",
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"Discusses the ensemble Kalman Filter, which uses a Monte Carlo approach to deal with very large Kalman filter states in nonlinear systems.\n",
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"\n",
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@ -105,12 +110,6 @@
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"\n",
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"*This chapter is not started. I'm likely to rearrange where this material goes - this is just a placeholder.*\n",
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"\n",
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"[**Chapter XX: Particle Filters**](not implemented)\n",
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" \n",
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"Particle filters uses a Monte Carlo technique to filter. \n",
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"\n",
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"*This is not implemented, and I have not decided if I want to make it part of this book or not.*\n",
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"\n",
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"\n",
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"[**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.ipynb)\n",
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"\n",
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