daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Changed IPython Notebook to Jupyter Notebook.

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Roger Labbe 2015-11-24 08:37:51 -08:00
parent 6c1de8ce61
commit 87032818fd
6 changed files with 905 additions and 957 deletions
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20
00-Preface.ipynb
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@ -35,6 +35,7 @@
"@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
"@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
"@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
"@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
"\n",
" div.cell{\n",
" width: 900px;\n",
@ -125,9 +126,10 @@
" white-space: nowrap;\n",
" }\n",
" div.text_cell_render{\n",
" font-family: 'Arimo',verdana,arial,sans-serif;\n",
" line-height: 125%;\n",
" font-size: 120%;\n",
" font-family: 'Fira sans', verdana,arial,sans-serif;\n",
" line-height: 150%;\n",
" font-size: 110%;\n",
" font-weight: 400;\n",
" text-align:justify;\n",
" text-justify:inter-word;\n",
" }\n",
@ -240,7 +242,7 @@
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {\n",
" extensions: [\"AMSmath.js\"]\n",
" extensions: [\"AMSmath.js\", \"autobold.js\"]\n",
" },\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
@ -278,7 +280,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Introductory textbook for Kalman filters and Bayesian filters. All code is written in Python, and the book itself is written in IPython Notebook 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?\n",
"Introductory textbook for Kalman filters and Bayesian filters. The book is written using Jupyter Notebook so that read the book in your browser and also run and modify the code, seeing the results inside the book. What better way to learn?\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/animations/05_dog_track.gif\">\n"
]
@ -434,13 +436,13 @@
"\n",
"`git clone --depth=1 https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python.git`\n",
" \n",
"This will create a directory named `Kalman-and-Bayesian-Filters-in-Python`. The `depth` parameter just gets you the latest version. Unless you need to see my entire commit history this is a lot faster and saves space. Navigate to the directory, and run IPython notebook with the command\n",
"This will create a directory named `Kalman-and-Bayesian-Filters-in-Python`. The `depth` parameter just gets you the latest version. Unless you need to see my entire commit history this is a lot faster and saves space. Navigate to the directory, and run Jupyter Notebook with the command\n",
"\n",
" ipython notebook\n",
"\n",
"This will open a browser window showing the contents of the base directory. The book is organized into chapters. Each chapter is named *xx*-*name*.ipynb, where *xx* is the chapter number. .ipynb is the Notebook file extension.\n",
"\n",
"This is admittedly a somewhat cumbersome interface to a book; I am following in the footsteps of several other projects that are somewhat re-purposing 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."
"This is admittedly a somewhat cumbersome interface to a book; I am following in the footsteps of several other projects that are somewhat re-purposing Jupyter 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."
]
},
{
@ -486,7 +488,7 @@
"\n",
"Code that is specific to the book is stored with the book in the subdirectory **/code**. It contains code for formatting the book. It also contains python files with names like *xxx*_internal.py. I use these to store functions that are useful for a specific chapter. This allows me to hide 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.\n",
"\n",
"Some chapters introduce functions that are useful for the rest of the book. Those functions are initially defined within the Notebook itself, but the code is also stored in a Python file in **/code** that is imported if needed in later chapters. I do document when I do this where the function is first defined, but this is still a work in progress. I try to avoid this because then I always face the issue of code in the directory becoming out of sync with the code in the book. However, IPython Notebook does not give us a way to refer to code cells in other notebooks, so this is the only mechanism I know of to share functionality across notebooks.\n",
"Some chapters introduce functions that are useful for the rest of the book. Those functions are initially defined within the Notebook itself, but the code is also stored in a Python file in **/code** that is imported if needed in later chapters. I do document when I do this where the function is first defined, but this is still a work in progress. I try to avoid this because then I always face the issue of code in the directory becoming out of sync with the code in the book. However, Jupyter Notebook does not give us a way to refer to code cells in other notebooks, so this is the only mechanism I know of to share functionality across notebooks.\n",
"\n",
"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. \n",
"\n",
@ -583,7 +585,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
"version": "3.5.0"
}
},
"nbformat": 4,

162
01-g-h-filter.ipynb
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6
02-Discrete-Bayes.ipynb
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@ -832,7 +832,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"And, if you are viewing this on the web or in IPython Notebook, here is an animation of that output.\n",
"And, if you are viewing this on the web or in Jupyter Notebook, here is an animation of that output.\n",
"<img src=\"animations/02_no_info.gif\">"
]
},
@ -1211,7 +1211,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, for those viewing this in a Notebook or on the web, here is an animation of that algorithm.\n",
"Finally, for those viewing this in a Jupyter Notebook or on the web, here is an animation of that algorithm.\n",
"<img src=\"animations/02_simulate.gif\">"
]
},
@ -1827,7 +1827,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
"version": "3.5.0"
}
},
"nbformat": 4,

2
04-One-Dimensional-Kalman-Filters.ipynb
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@ -1326,7 +1326,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"This chart is very similar to one we already saw in the *g-h Filter* chapter. At each predict step we take our existing estimate and update it based on our process model. We then make a measurement. We know neither the measurement or prediction is perfect, so we choose a value in between the two. This is scaled so that the new estimate is closer to the one we trust more. That scaling depends on the variance of the measurement vs the variance of the process model. We then step time forward one step, and what was the new estimate is considered to be the prior estimate, and the process repeats. "
"This chart is very similar to one we already saw in the *g-h Filter* chapter. At each predict step we take our existing estimate and update it based on our process model. We then make a measurement. The difference between the measurement and the prediction is called the *residual*. We know neither the measurement or prediction is perfect, so we choose a value in between the two. This is scaled so that the new estimate is closer to the one we trust more. That scaling depends on the variance of the measurement vs the variance of the process model. We then step time forward one step, and what was the new estimate is considered to be the prior estimate, and the process repeats. "
]
},
{

2
05-Multivariate-Gaussians.ipynb
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@ -683,7 +683,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"For those of you viewing this online or in IPython Notebook on your computer, here is an animation.\n",
"For those of you viewing this online or in Juptyer Notebook on your computer, here is an animation.\n",
"<img src='animations/multivariate_ellipse.gif'>\n",
"\n",
"(source: http://git.io/vqxLS)"

1670
Appendix-A-Installation.ipynb
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