Lots of changes to preface.
Too big a check in; got away from me. I rewrote a lot of the preface. Added instructions for cloud.sagemath install. Removed import of set_figsize and figsize in first cell because the first cell gets deleted for the pdf.
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],
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"source": [
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"#format the book\n",
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"source": [
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"## Kalman and Bayesian Filters\n",
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"\n",
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"Sensors are noisy. The world is full of data and events that we want to measure and track, but we cannot rely on our sensors to give us perfect information. I have a digital scale at home; if I weigh the same object multiple times I often get slightly different readings. \n",
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"Sensors are noisy. The world is full of data and events that we want to measure and track, but we cannot rely on sensors to give us perfect information. I have a mechanical scale at home; if I weigh the same object multiple times I often get slightly different readings. \n",
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"\n",
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"In simple cases the solution is obvious. If my scale gives slightly different readings I can just take a few readings and average them. But what do we do when the sensor is very noisy, or the information we are trying is very important? We may be trying to track the movement of an airplane. We may want to create an autopilot for a drone, track the milk production of cows, or ensure that our farm tractor seeded the entire field. I do computer vision, and I need to track moving objects in images. \n",
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"In simple cases the solution is obvious. If my scale gives slightly different readings I can just take a few readings and average them. Or I can replace it with a more accurate scale. But what do we do when the sensor is very noisy, or the environment makes data collection difficult? We may be trying to track the movement of a low flying aircraft. We may want to create an autopilot for a drone, track the milk production of cows, or ensure that our farm tractor seeded the entire field. I do computer vision, and I need to track moving objects in images, and the computer vision algorithms create very noisy and unreliable results. \n",
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"\n",
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"This book teaches you how to solve these sorts of filtering problems. We use several different algorithms, but they are all based on Bayesian probability. Later in the book Bayesian probability will be introduced with mathematical rigor, but in simple terms it is merely determining what is likely to be true based on past information. For example, suppose you drive home late one night and you see somebody with a mask on trying to open the window of your neighbors house. Absent any other information you might conclude that a break in is in progress. But you know what your neighbor looks like, and despite the mask this person looks like your neighbor. You are not sure, but your certainty that this is a crime lessens. Now suppose that you remember that this is October 31 - Halloween in the US, when we dress up in costumes. This provides a reason for the mask, and you form the hypothesis that your neighbor is locked outside of his house. Finally, suppose your wife pipes up with \"didn't they sell that house last week? Why is he trying to get inside\". This new piece of information seems to favor the crime hypothesis. \n",
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"This book teaches you how to solve these sorts of filtering problems. We use several different algorithms, but they are all based on Bayesian probability. Later in the book Bayesian probability will be introduced with mathematical rigor, but in simple terms it is merely determining what is likely to be true based on past information. For example, suppose you drive home late and see a masked person breaking the window of your neighbors house. You'd conclude that they are a burglar. But you know what your neighbor looks like, and despite the mask this person looks like him. You connot be sure, but your certainty that this is a crime lessens. Now suppose that you remember that this is October 31 - Halloween in the US, when we dress up in costumes. This provides a reason for the mask, and you form the hypothesis that your neighbor is locked outside of his house. Finally, suppose your wife pipes up with \"didn't they sell that house last week? Why is he trying to get inside\". This new piece of information seems to favor the crime hypothesis, and you call the police.\n",
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"\n",
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"There is more to Bayesian probability than that, but you have the main idea. Knowledge is uncertain, and we alter our beliefs based on *prior* evidence. How does this apply to filtering? Say we are trying to track an aircraft that up to now has been flying straight and level. Its on board navigation reports that it suddenly changed direction. Did it really do so, or is the sensor reporting bad data? It depends. If this is a jet fighter we may be very inclined to believe the report of a sudden maneuver. If it is a large, cumbersome passenger plane we might be very skeptical. Now say that in the next second the sensor reports a new position that is in line with the previous straight and level path. This leads us to make us think that the previous measurement was an error, though of course it could be true that it is *this* measurement that is wrong, not the last one.\n",
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"\n",
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"I've been using phrases like \"more likely.\" Bayesian filters use mathematics to compute these probabilities exactly. Kalman filters are a *type* of Bayesian filter. In other words, all Kalman filters are Bayesian filters, but not all Bayesian filters are Kalman filters. We will learn much more about how these two are related; for now it is enough to say that the Kalman filter can guarantee that its estimate is optimal under certain conditions, whereas other types of Bayesian filters are not necessarily optimal. "
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"There is more to Bayesian probability, but you have the main idea. Knowledge is uncertain, and we alter our beliefs based on the strength of the evidence. How does this apply to filtering? Say we are trying to track an aircraft that up to now has been flying straight and level. A sensor reports that it suddenly changed direction. Did it really do so, or is the sensor reporting bad data? It depends. If this is a jet fighter we may be very inclined to believe the report of a sudden maneuver. If it is a large, cumbersome passenger jet we might be very skeptical. If the sensor is extremely accurate then we would tend to believe it."
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]
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"collapsed": true
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"source": [
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"## Motivation\n",
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"## Motivation for this Book\n",
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"\n",
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"The Kalman filter was invented by Rudolf Emil Kálmán to solve this sort of problem in a mathematically optimal way. The theory and applications are beautiful, but quite difficult to learn if you are not already well trained in topics such as signal processing, control theory, probability and statistics, and guidance and control theory. Its first use was on the Apollo missions to the moon, and since then it has been used in an enormous variety of domains. There are Kalman filters in aircraft, on submarines, on cruise missiles. Wall street uses them to track the market. They are used in robots, in IoT (Internet of Things) sensors, and in laboratory instruments. Chemical plants use them to control and monitor reactions. They are used in medical equipment to do things like medical imaging or to remove noise from cardiac signals. If it involves a sensor, a Kalman filter or a close relative to the Kalman filter is usually involved.\n",
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"\n",
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"I'm a software engineer that spent almost two decades in aerospace, and so I have always been 'bumping elbows' with the Kalman filter, but never implemented one. They always had a fearsome reputation for difficulty, and I did not have the requisite education. Everyone I met that did implement them had multiple graduate courses on the topic and extensive industrial experience with them. As I moved into solving tracking problems with computer vision the need to implement them myself became urgent. There are classic textbooks in the field, such as Grewal and Andrew's excellent *Kalman Filtering*. But sitting down and trying to read many of these books is a dismal and trying experience if you do not have the background. Typically the first few chapters fly through several years of undergraduate math, blithely referring you to textbooks on, for example, Itō calculus, and presenting an entire semester's worth of statistics in a few brief paragraphs. These books are good textbooks for an upper undergraduate course, and an invaluable reference to researchers and professionals, but the going is truly difficult for the more casual reader. Symbology is introduced without explanation, different texts use different words and variables names for the same concept, and the books are almost devoid of examples or worked problems. I often found myself able to parse the words and comprehend the mathematics of a definition, but had no idea as to what real world phenomena these words and math were attempting to describe. \"But what does that *mean?*\" was my repeated thought.\n",
|
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"I'm a software engineer that spent almost two decades in aerospace, and so I have always been 'bumping elbows' with the Kalman filter, but never implemented one. They always had a fearsome reputation for difficulty, and I did not have the requisite education. Everyone I met that did implement them had multiple graduate courses on the topic and extensive industrial experience with them. As I moved into solving tracking problems with computer vision the need to implement them myself became urgent. There are classic textbooks in the field, such as Grewal and Andrew's *Kalman Filtering*. But sitting down and trying to read many of these books is a dismal and trying experience if you do not have the background. Typically the first few chapters fly through several years of undergraduate math, blithely referring you to textbooks on, for example, Itō calculus, and presenting an entire semester's worth of statistics in a few brief paragraphs. These books are good textbooks for an upper undergraduate course, and an invaluable reference to researchers and professionals, but the going is truly difficult for the more casual reader. Notation is introduced without explanation, different texts use different words and variables names for the same concept, and the books are almost devoid of examples or worked problems. I often found myself able to parse the words and comprehend the mathematics of a definition, but had no idea as to what real world phenomena these words and math were attempting to describe. \"But what does that *mean?*\" was my repeated thought. Here are typical examples which once puzzled me:\n",
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"\n",
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"$$\\hat{x}_{k} = \\Phi_{k}\\hat{x}_{k-1} + G_ku_{k-1} + K_k[z_k - H\\Phi_{k}\\hat{x}_{k-1} - HG_ku_{k-1} ] \\\\\n",
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"\\mathbf{P}_{k\\mid k} = (I - \\mathbf{K}_k \\mathbf{H}_{k})\\textrm{cov}(\\mathbf{x}_k - \\hat{\\mathbf{x}}_{k\\mid k-1})(I - \\mathbf{K}_k \\mathbf{H}_{k})^{\\text{T}} + \\mathbf{K}_k\\textrm{cov}(\\mathbf{v}_k )\\mathbf{K}_k^{\\text{T}}$$\n",
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"\n",
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"However, as I began to finally understand the Kalman filter I realized the underlying concepts are quite straightforward. A few simple probability rules, some intuition about how we integrate disparate knowledge to explain events in our everyday life and the core concepts of the Kalman filter are accessible. Kalman filters have a reputation for difficulty, but shorn of much of the formal terminology the beauty of the subject and of their math became clear to me, and I fell in love with the topic. \n",
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"\n",
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||||
"As I began to understand the math and theory more difficulties appeared. A book or paper will make some statement of fact and presents a graph as proof. Unfortunately, why the statement is true is not clear to me, nor is the method by which you might make that plot obvious. Or maybe I wonder \"is this true if R=0?\" Or the author provides pseudocode - at such a high level that the implementation is not obvious. Some books offer Matlab code, but I do not have a license to that expensive package. Finally, many books end each chapter with many useful exercises. Exercises which you need to understand if you want to implement Kalman filters for yourself, but exercises with no answers. If you are using the book in a classroom, perhaps this is okay, but it is terrible for the independent reader. I loathe that an author withholds information from me, presumably to avoid 'cheating' by the student in the classroom.\n",
|
||||
"As I began to understand the math and theory more difficulties appeared. A book or paper will make some statement of fact and presents a graph as proof. Unfortunately, why the statement is true is not clear to me, and I didn't understand how to create the plot. Or maybe I wonder \"is this true if R=0?\" Or the author provides pseudocode at such a high level that the implementation is not obvious. Some books offer Matlab code, but I do not have a license to that expensive package. Finally, many books end each chapter with many useful exercises. Exercises which you need to understand if you want to implement Kalman filters for yourself, but exercises with no answers. If you are using the book in a classroom, perhaps this is okay, but it is terrible for the independent reader. I loathe that an author withholds information from me, presumably to avoid 'cheating' by the student in the classroom.\n",
|
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"\n",
|
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"None of this necessary, from my point of view. Certainly if you are designing a Kalman filter for a aircraft or missile you must thoroughly master of all of the mathematics and topics in a typical Kalman filter textbook. I just want to track an image on a screen, or write some code for my Arduino project. I want to know how the plots in the book are made, and chose different parameters than the author chose. I want to run simulations. I want to inject more noise in the signal and see how a filter performs. There are thousands of opportunities for using Kalman filters in everyday code, and yet this fairly straightforward topic is the provenance of rocket scientists, medical engineers and academics.\n",
|
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"All of this impedes learning, from my point of view. Certainly if you are designing a Kalman filter for a aircraft or missile you must thoroughly master of all of the mathematics and topics in a typical Kalman filter textbook. I just want to track an image on a screen, or write some code for my Arduino project. I want to know how the plots in the book are made, and chose different parameters than the author chose. I want to run simulations. I want to inject more noise in the signal and see how a filter performs. There are thousands of opportunities for using Kalman filters in everyday code, and yet this fairly straightforward topic is the provenance of rocket scientists and academics.\n",
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"\n",
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"I wrote this book to address all of those needs. This is not the book for you if you program avionics for Boeing or design radars for Raytheon. Go get a degree at Georgia Tech, UW, or the like, because you'll need it. This book is for the hobbyist, the curious, and the working engineer that needs to filter or smooth data. \n",
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"I wrote this book to address all of those needs. This is not the book for you if you design radars for Raytheon. Go get a Masters or PhD at Georgia Tech, UW, or the like, because you'll need it. This book is for the hobbyist, the curious, and the working engineer that needs to filter or smooth data. \n",
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"\n",
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"This book is interactive. While you can read it online as static content, I urge you to use it as intended. It is written using IPython Notebook, which allows me to combine text, python, and python output in one place. Every plot, every piece of data in this book is generated from Python that is available to you right inside the notebook. Want to double the value of a parameter? Click on the Python cell, change the parameter's value, and click 'Run'. A new plot or printed output will appear in the book. \n",
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"This book is interactive. While you can read it online as static content, I urge you to use it as intended. It is written using Jupyter Notebook (formally known as IPython Notebook). This allows me to combine text, Python, and Python output in one place. Every plot, every piece of data in this book is generated from Python that is available to you inside the notebook. Want to double the value of a parameter? Click on the Python cell, change the parameter's value, and click 'Run'. A new plot or printed output will appear in the book. \n",
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"\n",
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"This book has exercises, but it also has the answers. I trust you. If you just need an answer, go ahead and read the answer. If you want to internalize this knowledge, try to implement the exercise before you read the answer. \n",
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"\n",
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"This book has supporting libraries for computing statistics, plotting various things related to filters, and for the various filters that we cover. This does require a strong caveat; most of the code is written for didactic purposes. It is rare that I chose the most efficient solution (which often obscures the intent of the code), and in the first parts of the book I did not concern myself with numerical stability. This is important to understand - Kalman filters in aircraft are carefully designed and implemented to be numerically stable; the naive implementation is not stable in many cases. If you are serious about Kalman filters this book will not be the last book you need. My intention is to introduce you to the concepts and mathematics, and to get you to the point where the textbooks are approachable.\n",
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"\n",
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"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 they 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/) [1]. 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. "
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"Finally, this book is free. I've spent several thousand dollars on Kalman filtering books. I cannot believe they 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/) [1]. It's time to repay that. So, the book is free, it is hosted on free servers at GitHub, and it uses only free and open software such as IPython and MathJax. "
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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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"You may access this book via nbviewer at any time by using this address:\n",
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"[*Read Online Now*](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)\n",
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"The book is hosted on GitHub, and you can read any chapter by clicking on its name. GitHub statically renders Jupyter Notebooks. You will not be able to run or alter the code, but you can read all of the content.\n",
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"\n",
|
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"The GitHub pages for this project are at\n",
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"\n",
|
||||
" https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python \n",
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"\n",
|
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"The nbviewer website will render any Notebook in a static format. I find it does a slightly better job than the GitHub renderer, but it is slighty harder to use. It accesses GitHub directly; whatever I have checked into GitHub will be rendered by nbviewer.\n",
|
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"\n",
|
||||
"You may access this book via nbviewer here:\n",
|
||||
"\n",
|
||||
"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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"## Host the Book Online for Free\n",
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"\n",
|
||||
"Maybe you don't want to, or can't install IPython and associated libraries on your device, or maybe you move from device to device a lot and don't want to distribute the work you are doing in the book in multiple places. Maybe you have high school students with Chromebooks, and you want them to be able to run the code, and write new code for exercises you devise. Wouldn't it be nice if the book was in the cloud?\n",
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"\n",
|
||||
"The site https://cloud.sagemath.com will host as many Jupyter Notebooks as you want. There are limits on memory and CPU, but what they offer is more than sufficient for this book. All that is required is creating a free account. There's even support for teachers - including student accounts and grading capabilities. \n",
|
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"\n",
|
||||
"### Installation Instructions\n",
|
||||
"\n",
|
||||
"1. Go to the site and make an account. You can create one manually, or sign in with Google, Facebook, Github, or Twitter.\n",
|
||||
"\n",
|
||||
"2. Once you are signed in it will take you to the Projects page. Click the button labelled \"Create New Project..\". Give your project any title you want, such as \"Kalman Filter Book\" and press the \"Create Project\" button.\n",
|
||||
"\n",
|
||||
"3. That project should be listed as your only project on the Projects page. Click on it to open it.\n",
|
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"\n",
|
||||
"4. You should be on a page with text stating \"Create or upload files or directories...\". Don't do that! We are going to pull the data directly from GitHub, which is much easier. Click the \"New...\" dropdown and select \"terminal\".\n",
|
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"\n",
|
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"5. This opens a linux terminal inside your browser. Every project you make creates a linux container that runs in the cloud. You can put any file here that you want, up to the disk quota. We will use `wget` to download a bash script which will install the book and its dependencies. type this at the terminal prompt (copy and paste is easier):\n",
|
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"\n",
|
||||
" wget https://raw.githubusercontent.com/rlabbe/kalman-book-sagemath-install/master/install.sh\n",
|
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" bash install sh\n",
|
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"\n",
|
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"You should see messages flash by as it downloads the book and FilterPy.\n",
|
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"\n",
|
||||
"The book is now installed.\n",
|
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"\n",
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"\n",
|
||||
"http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb\n",
|
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"### Reading/Using Instructions\n",
|
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"\n",
|
||||
"1. At the top left of the session should be a \"Files\" button. Click it if is not already highlighted. You should see a folder named `Kalman-and-Bayesian-Filters-in-Python`. Click it to go into that directory.\n",
|
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"\n",
|
||||
"2. All of the notebooks in the book are listed, but in order of time, not name. At the upper right is an down arrow button with 1..9 beside it. This will resort by name. Click it.\n",
|
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"\n",
|
||||
" \n",
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||||
"Now all of the Notebooks are listed in order. To run a notebook, just click on it's name. You'll be in a fully functional Notebook, hosted on the sagemath servers. You can run code, play with the animations, change code, heck, change the text. Do whatever you want. It's yours, free. Make a change and push it back to my repo. \n",
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"\n",
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"\n",
|
||||
"The quickest way to get starting with reading the book is to read it online using the link 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. \n",
|
||||
"### Updating the Book if I Made a Change.\n",
|
||||
"\n",
|
||||
"The preface available from the link above has all the information in this README and more, so feel free to follow the link now.\n"
|
||||
"1. Open a terminal as explained above. You can open the terminal you made during installation. They stay around until you delete them, even if you log out.\n",
|
||||
"\n",
|
||||
"2. from the home directory, enter the command\n",
|
||||
"\n",
|
||||
" git pull\n",
|
||||
" \n",
|
||||
"You may not enjoy life if you made a lot of changes to the book as git will try to merge both of our changes together. If you are good at `git` you know what to do. You can blow away all of your changes with the following commands (instead of running `git pull`)\n",
|
||||
"\n",
|
||||
" git fetch --all\n",
|
||||
" git reset --hard origin/master\n",
|
||||
"\n",
|
||||
"If you don't care about saving the changes you made you may just opt to delete the directory and reload it. You'd do that with\n",
|
||||
"\n",
|
||||
" rm -rf Kalman-and-Bayesian-Filters-in-Python\n",
|
||||
" git clone --depth=1 https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python.git\n",
|
||||
" cd Kalman-and-Bayesian-Filters-in-Python/\n",
|
||||
" git clone --depth=1 https://github.com/rlabbe/filterpy.git\n",
|
||||
"\n",
|
||||
"I'm not going to teach you git, so I'll stop here.\n",
|
||||
"\n",
|
||||
"I grant you this is not exactly painless, but this does allow you full interactivity, in the cloud, for free, with a free linux session!\n",
|
||||
"\n",
|
||||
"If you do go this route, spend some time in the FAQ. You can share your Notebooks with others (\"hey, look at this Julia version I did!\") and a lot more that I haven't discussed. Jupyter Notebooks is just one of several features they offer."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -352,9 +432,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"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.\n",
|
||||
"\n",
|
||||
"[*PDF Version of the book*](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/Kalman_and_Bayesian_Filters_in_Python.pdf)\n",
|
||||
"I periodically generate a PDF of the book from the Notebooks. I usually update after any substantive change, but it may lag the GitHub repo a small amount. 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. If you are offline, and either cannot or don't want to install IPython this is an excellent choice.\n",
|
||||
"\n",
|
||||
"https://drive.google.com/file/d/0By_SW19c1BfhSVFzNHc0SjduNzg/view?usp=sharing"
|
||||
]
|
||||
@@ -370,9 +448,9 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"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!\n",
|
||||
"This book is 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 inside your browser. 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 vital. You do not have to wonder \"what happens if\". Try it and see!\n",
|
||||
"\n",
|
||||
"The github pages for this project are at\n",
|
||||
"The GitHub pages for this project are at\n",
|
||||
"\n",
|
||||
" https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python \n",
|
||||
"\n",
|
||||
@@ -384,7 +462,7 @@
|
||||
"\n",
|
||||
" ipython notebook\n",
|
||||
"\n",
|
||||
"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.\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."
|
||||
]
|
||||
@@ -400,59 +478,44 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"** author's note**. *The book is still being written, and so I am not focusing on issues like supporting multiple versions of Python. I am staying more or less on the bleeding edge of Python 3 for the time being. If you follow my suggestion of installing Anaconda all of the versioning problems will be taken care of for you, and you will not alter or affect any existing installation of Python on your machine. I am aware that telling somebody to install a specific packaging system is not a long term solution, but I can either focus on endless regression testing for every minor code change, or work on delivering the book, and then doing one sweep through it to maximize compatibility. I opt for the latter. In the meantime I welcome bug reports if the book does not work on your platform.*\n",
|
||||
"Installation of the required software is described in detail in the Installation appendix, which you can read online here:\n",
|
||||
"\n",
|
||||
"If you want to run the notebook on your computer, which is what I recommend, then you will have to have IPython 2.4 or later installed. IPython is an interactive architecture that provides IPython Notebook, the tool used to write this book. Note that the IPython version has nothing to do with the Python version. IPython 2.4 can run Python 3.4, IPython 3.0 can run Python 2.7, and so on. \n",
|
||||
"http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/Appendix_A_Installation.ipynb\n",
|
||||
"\n",
|
||||
"I do not cover how to install IPython 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.\n",
|
||||
"You will need IPython 3.0 or greater, Python 2.7+ or Python 3.4+, NumPy, SciPy, SymPy, and Matplotlib. You will also need my open source library FilterPy.\n",
|
||||
"\n",
|
||||
"The IPython Notebook format was changed as of IPython 3.0. If you are running 2.4 you will still be able to open and run the notebooks, but they will be down converted for you. If you make changes DO NOT push 2.4 version notebooks to me! I strongly recommend updating to 3.0 as soon as possible, as this format change will just become more frustrating with time.\n",
|
||||
"The easiest way to get all of those except for FilterPy is by installing a free Scientific Python distribution, such as the Anaconda distribution https://store.continuum.io/cshop/anaconda/ (the link says \"shop\", but it is free). I am not naming them out of favoritism, I am merely documenting my environment. Should you have trouble running any of the code, perhaps knowing this will help you.\n",
|
||||
"\n",
|
||||
"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.\n",
|
||||
"\n",
|
||||
"You will need a recent version of NumPy, SciPy, SymPy, and Matplotlib installed. I don't really know what the minimal version might be. I have NumPy 1.71, SciPy 0.13.0, and Matplotlib 1.4.0 installed on my machines.\n",
|
||||
"\n",
|
||||
"Personally, I use the Anaconda Python distribution in all of my work, [available here](https://store.continuum.io/cshop/anaconda/) [3]. I am not selecting them out of favoritism, I am merely documenting my environment. Should you have trouble running any of the code, perhaps knowing this will help you.\n",
|
||||
"\n",
|
||||
"Finally, you will need to install FilterPy, described in the next section.\n",
|
||||
"\n",
|
||||
"Installation of all of these packages is described in the Installation appendix, which you can read online [here](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/Appendix_A_Installation.ipynb).\n",
|
||||
"\n",
|
||||
"http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/Appendix_A_Installation.ipynb"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##Provided Libraries"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"I am writing an open source Bayesian filtering Python library called **FilterPy**. It is available on github at (https://github.com/rlabbe/filterpy). To ensure that you have the latest 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.\n",
|
||||
"\n",
|
||||
"I have also made the project available on PyPi, the Python Package Index. I will be honest, I am not updating this as fast as I am changing the code in the library. That will change as the library and this book mature. To install from PyPi, at the command line issue the command\n",
|
||||
"Once you have Python installed install FilterPy by typing\n",
|
||||
"\n",
|
||||
" pip install filterpy\n",
|
||||
"\n",
|
||||
"If you do not have pip, you may follow the instructions here:\n",
|
||||
"at the command line."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##My Libraries and Modules"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"I am writing an open source Bayesian filtering Python library called FilterPy. Installation instructions are given above.\n",
|
||||
"\n",
|
||||
"https://pip.pypa.io/en/latest/installing.html.\n",
|
||||
"FilterPy is hosted GitHub at (https://github.com/rlabbe/filterpy) but the `pip` installed version should serve your needs.\n",
|
||||
"\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",
|
||||
"\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",
|
||||
"\n",
|
||||
"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.\n",
|
||||
"\n",
|
||||
"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.\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 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",
|
||||
"\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",
|
||||
"\n",
|
||||
"The directory **styles** 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_Introduction.ipynb) [4]. I have also been very influenced by Professor Lorena Barba's fantastic work, [available here](https://github.com/barbagroup/CFDPython) [5]. I owe all of my look and feel to the work of these projects. "
|
||||
"The directory **/code** contains a css file containing the style guide for the book. The default look and feel of Jupyter Notebook is rather plain. 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_Introduction.ipynb) [4]. I have also been very influenced by Professor Lorena Barba's fantastic work, [available here](https://github.com/barbagroup/CFDPython) [5]. I owe all of my look and feel to the work of these projects. "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -466,40 +529,45 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"I am first and foremost a programmer. Most Kalman filtering and other engineering texts are written by mathematicians or engineers. As a result, the software is usually not production quality. I will take Paul Zarchan's book *Fundamentals of Kalman Filtering* as an example. This is a fantastic book and it belongs in your library. But the code is Fortran listing without any subroutines beyond calls to functions like `MATMUL`. This means that Kalman filters are re-implemented over and over again throughout the book. The same listing mixes simulation code with filtering code, so until you become aware of the author's naming style it can be difficult to ascertain what code is the filter and what code is the simulation. Some chapters implement the same filter in subtly different ways, and uses bold text to highlight the few lines that changed. If he needs to use Runge Kutta, that is just embedded in the code, without comments. \n",
|
||||
"I am first and foremost a programmer. Most Kalman filtering and other engineering texts are written by mathematicians or engineers. As a result, the software is usually not production quality. I will take Paul Zarchan's book *Fundamentals of Kalman Filtering* as an example. This is a fantastic book and it belongs in your library. But the code is Fortran without any subroutines beyond calls to functions like `MATMUL`. This means that Kalman filters are re-implemented over and over again throughout the book. The same listing mixes simulation code with filtering code, so until you become aware of the author's naming style it can be difficult to ascertain what code is the filter and what code is the simulation. Some chapters implement the same filter in subtly different ways, and uses bold text to highlight the few lines that changed. If he needs to use Runge Kutta, that is just embedded in the code, without comments. \n",
|
||||
"\n",
|
||||
"There's a better way. If I want to perform an SVD I call `svd()`, I do not embed an SVD implementation in my code. This buys me several things. First, I don't have to re-implement SVD multiple times. I don't have to debug SVD several times, and if I do find a bug, I can fix it once and be assured that it now works across all my different projects. Finally, it is readable. It is rare that I care about the implementation of SVD in my projects.\n",
|
||||
"There's a better way. If I want to perform an SVD I call `svd()`, I do not embed an SVD implementation in my code. I don't want to implement SVD multiple times and debug it several times. if I do find a bug, I can fix it once and be assured that it now works across all my different projects. And, it is readable. It is rare that I care about the implementation of SVD.\n",
|
||||
"\n",
|
||||
"Now, this is a textbook on Kalman filtering, and you could reasonably point out that we *do* care about the implementation of Kalman filters. To an extent that is true, but as you will find out the code that performs the filtering amounts to 7 or so lines of code. The code to implement the math is fairly trivial. Most of the work that Kalman filters requires is the design of the matrices that get fed into the math engine. So that is how I have structured the code.\n",
|
||||
"This is a textbook on Kalman filtering, and you can argue that we *do* care about the implementation of Kalman filters. That is true, but you will find out the code that performs the filtering amounts to 10 or so lines of code. The code to implement the math is fairly trivial. Most of the work that Kalman filters requires is the design of the matrices that get fed into the math engine.\n",
|
||||
"\n",
|
||||
"For example, there is a class named `KalmanFilter` which implements the linear algebra for performing Kalman filtering. To use it you will construct an object of that class, initialize various parameters, then enter a while loop where you call `KalmanFilter.predict()` and `KalmanFilter.update()` to incorporate your measurements. Thus most of your programs will be only 20-50 lines, most of that boilerplate - setting up the matrices, and then plotting and/or using the results. The possible downside of that is that the equations that perform the filtering are hidden behind functions, which we could argue is a loss in a pedagogical text. I argue the converse. I want you to learn how to use Kalman filters in the real world, for real projects, and you shouldn't be cutting and pasting established algorithms all over the place. If you want to use ode45 (Runga Kutta) you call that function, you don't re-implement it from scratch. We will do the same here.\n",
|
||||
"A possible downside is that the equations that perform the filtering are hidden behind functions, which we could argue is a loss in a pedagogical text. I argue the converse. I want you to learn how to use Kalman filters in the real world, for real projects, and you shouldn't be cutting and pasting established algorithms all over the place. If you want to use Runge Kutta you call `ode45`, you don't re-implement it from scratch. We will do the same here.\n",
|
||||
"\n",
|
||||
"However, it is undeniable that you will be a bit less versed with the equations of the Kalman filter as a result. I strongly recommend looking at the source for FilterPy, and maybe even implementing your own filter from scratch to be sure you understand the concepts.\n",
|
||||
"I use Python classes. I do not use inheritance or virtual functions or any of that sort of object oriented design. I use classes as a way to organize the data that the filters require. For example, the `KalmanFilter` class stores matrices called `x`, `P`, `R`, `Q`. I've seen procedural libraries for Kalman filters, and they require the programmer to maintain all of those matrices. This perhaps isn't so bad for a toy program, but program a bank of Kalman filters and you will not enjoy having to manage all of those matrices and other associated data.\n",
|
||||
"\n",
|
||||
"I use a fair number of classes in FilterPy. I do not use inheritance or virtual functions or any of that sort of object oriented design. I use classes as a way to organize the data that the filters require. For example, the `KalmanFilter` class mentioned above stores matrices called `x`, `P`, `R`, `Q`, and more. I've seen procedural libraries for Kalman filters, and they require the programmer to maintain all of those matrices. This perhaps isn't so bad for a toy program, but start programming, say, a bank of Kalman filters and you will not enjoy having to manage all of those matrices and other associated data.\n",
|
||||
"\n",
|
||||
"A word on variable names. I am an advocate for descriptive variable names. In the Kalman filter literature the measurement noise covariance matrix is called `R`. `R` is not, normally, descriptive. I could reasonably call it `measurement_noise_covariance`, and I've seen libraries do that. I've chosen not to. Why? In the end, Kalman filtering is math. To write a Kalman filter you are going to have to start by sitting down with a piece of paper and doing some math. You will be writing normal algebraic equations. Also, every Kalman filter text and source on the web uses the same linear algebra equations. You cannot read about the Kalman filter without seeing\n",
|
||||
"## FilterPy's Naming Conventions\n",
|
||||
"\n",
|
||||
"$$\\dot{\\mathbf{x}} = \\mathbf{Fx} + \\mathbf{Gu}$$\n",
|
||||
"A word on variable names. I am an advocate for descriptive variable names. In the Kalman filter literature the measurement noise covariance matrix is called `R`. The name `R` is not descriptive. I could reasonably call it `measurement_noise_covariance`, and I've seen libraries do that. I've chosen not to.\n",
|
||||
"\n",
|
||||
"in every source. One of my goals in this book is to bring you to the point where you can read the original literature on Kalman filtering. I take an optimistic tone in this book - that Kalman filtering is easy to learn - and in many ways it is. However, for nontrivial problems the difficulty is not the implementation of the equations, but learning how to set up the equations so they solve your problem. In other words, every Kalman filter will implement $\\dot{\\mathbf{x}} = \\mathbf{Fx} + \\mathbf{Gu}$; the difficult part is figuring out what to put in the matrices $\\mathbf{F}$ and $\\mathbf{G}$ to make your filter work for your problem. Vast amounts of work have been done to figure out how to apply Kalman filters in various domains, and it would be tragic to not be able to read the literature and avail yourself of this research. \n",
|
||||
"In the end, Kalman filtering is math. To write a Kalman filter you are going to start by sitting down with a piece of paper and doing math. You will be writing and solving normal algebraic equations. Every Kalman filter text and source on the web uses the same equations. You cannot read about the Kalman filter without seeing this equation\n",
|
||||
"\n",
|
||||
"So, like it or not you will need to learn that $\\mathbf{F}$ is the *state transition matrix* and that $\\mathbf{R}$ is the *measurement noise covariance*. Once you know that the code will become readable, and until you know that all publications and web articles on Kalman filters will be inaccessible to you. \n",
|
||||
"$$\\dot{\\mathbf{x}} = \\mathbf{Fx} + \\mathbf{Gu} + w$$\n",
|
||||
"\n",
|
||||
"Finally I feel that mathematical programming is somewhat different than regular programming; what is readable in one domain is not readable in another. `q = x + m` is opaque in a normal context. On the other hand, `x = .5*a*t**2 + v_0 * t + x_0` is to me the most readable way to write the Newtonian distance equation:\n",
|
||||
"One of my goals in this book is to bring you to the point where you can read the original literature on Kalman filtering. I take an optimistic tone in this book - that Kalman filtering is easy to learn - and in many ways it is. However, for nontrivial problems the difficulty is not the implementation of the equations, but learning how to set up the equations so they solve your problem. In other words, every Kalman filter implements $\\dot{\\mathbf{x}} = \\mathbf{Fx} + \\mathbf{Gu} + w$; the difficult part is figuring out what to put in the matrices $\\mathbf{F}$ and $\\mathbf{G}$ to make your filter work for your problem. Vast amounts of work have been done to apply Kalman filters in various domains, and it would be tragic to be unable to avail yourself of this research. \n",
|
||||
"\n",
|
||||
"I use the same equations in my book. You will need to know the variable names to understand them.\n",
|
||||
"\n",
|
||||
"So, like it or not you will need to learn that $\\mathbf{F}$ is the *state transition matrix* and that $\\mathbf{R}$ is the *measurement noise covariance*. Once you know that the code will become readable, and until then this book's math, and all publications and web articles on Kalman filters will be inaccessible to you. \n",
|
||||
"\n",
|
||||
"Finally, I think that mathematical programming is somewhat different than regular programming; what is readable in one domain is not readable in another. `q = x + m` is opaque in a normal context. On the other hand, `x = (.5*a)*t**2 + v_0*t + x_0` is to me the most readable way to program the Newtonian distance equation:\n",
|
||||
"\n",
|
||||
"$$ x = \\frac{1}{2}at^2 + v_0 t + x_0$$\n",
|
||||
"\n",
|
||||
"We could write it as \n",
|
||||
"\n",
|
||||
" distance = .5 * constant_acceleration * time_delta**2 + \n",
|
||||
" distance = (.5 * constant_acceleration) * time_delta**2 + \n",
|
||||
" initial_velocity * time_delta + initial_distance\n",
|
||||
" \n",
|
||||
"but I feel that obscures readability. This is surely debatable for this one equation; but most mathematical programs, and certainly Kalman filters, use systems of equations. I can most easily follow the code, and ensure that it does not have bugs, when it reads as close to the math as possible. Consider this equation taken from the Kalman filter:\n",
|
||||
"but I feel that obscures readability. This is debatable for this one equation; but most mathematical programs, and certainly Kalman filters, use systems of equations. I can most easily follow the code, and ensure that it does not have bugs, when it reads as close to the math as possible. Consider this equation from the Kalman filter:\n",
|
||||
"\n",
|
||||
"$$\\mathbf{K} = \\mathbf{PH}^\\mathsf{T}[\\mathbf{HPH}^\\mathsf{T} + \\mathbf{R}]^{-1}$$\n",
|
||||
"\n",
|
||||
"My Python code for this would be\n",
|
||||
"Python code for this would be\n",
|
||||
"\n",
|
||||
" K = dot(P, H.T).dot(inv(dot(H, P).dot(H.T) + R))\n",
|
||||
" \n",
|
||||
@@ -507,12 +575,16 @@
|
||||
"\n",
|
||||
" kalman_gain = (\n",
|
||||
" dot(apriori_state_covariance, measurement_function_transpose).dot(\n",
|
||||
" inv (dot(measurement_function, apriori_state_covariance).dot(\n",
|
||||
" inv(dot(measurement_function, apriori_state_covariance).dot(\n",
|
||||
" measurement_function_transpose) + measurement_noise_covariance)))\n",
|
||||
"\n",
|
||||
"I grant you this version has more context, but I cannot reasonable glance at this and see what math it is implementing. In particular, the linear algebra $\\mathbf{HPH}^\\mathsf{T}$ is doing something very specific - multiplying $\\mathbf{P}$ by $\\mathbf{H}$ and its transpose is changing $\\mathbf{P}$ from world space to measurement space (we will learn what that means - it is important!) It is nearly impossible to see that the Kalman gain is just a ratio of one number divided by a second number which has been converted to a different basis. If you are not solid in linear algebra perhaps that statement does not convey a lot of information to you yet, but I assure you that $\\mathbf{K} = \\mathbf{PH}^\\mathsf{T}[\\mathbf{HPH}^\\mathsf{T} + \\mathbf{R}]^{-1}$ is saying something very succinctly. There are two key pieces of information here - we are taking a ratio, and we are converting the *basis* of a matrix. I can see that in my first Python line, I cannot see that in the second line. \n",
|
||||
"which I adapted from a popular library. I grant you this version has more context, but I cannot glance at this and see what math it is implementing. In particular, the linear algebra $\\mathbf{HPH}^\\mathsf{T}$ is doing something very specific - multiplying $\\mathbf{P}$ by $\\mathbf{H}$ in a way that converts $\\mathbf{P}$ from *world space* to *measurement space* (we'll learn what that means). It is nearly impossible to see that the Kalman gain (`K`) is just a ratio of one number divided by a second number which has been converted to a different basis. This statement may not convey a lot of information to you before reading the book, but I assure you that $\\mathbf{K} = \\mathbf{PH}^\\mathsf{T}[\\mathbf{HPH}^\\mathsf{T} + \\mathbf{R}]^{-1}$ is saying something very succinctly. There are two key pieces of information here - we are finding a ratio, and we are doing it in measurement space. I can see that in my first Python line, I cannot see that in the second line. If you want a counter-argument, my version obscures the information that $\\mathbf{P}$ is in this context is a *prior* (we will learn what a *prior* is in the book). \n",
|
||||
"\n",
|
||||
"I will not *win* this argument, and some people will not agree with my naming choices. I will finish by stating, very truthfully, that I made two mistakes the first time I typed that second version and it took me awhile to find it. In any case, I aim for using the mathematical symbol names whenever possible, coupled with readable class and function names. So, it is `KalmanFilter.P`, not `KF.P` and not `KalmanFilter.apriori_state_covariance`. "
|
||||
"These comments apply to library code. The book's examples uses names like `sensor_noise`, or `gps_sensor_noise`, not `R`. Math code should read like math, and interface or glue code should read like normal code. Context is important.\n",
|
||||
"\n",
|
||||
"I will not *win* this argument, and some people will not agree with my naming choices. I will finish by stating, very truthfully, that I made two mistakes the first time I typed the second version and it took me awhile to find it. In any case, I aim for using the mathematical symbol names whenever possible, coupled with readable class and function names. So, it is `KalmanFilter.P`, not `KF.P` and not `KalmanFilter.apriori_state_covariance`. \n",
|
||||
"\n",
|
||||
"Let's learn some math."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
+46
-45
@@ -267,8 +267,8 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"import book_format\n",
|
||||
"book_format.load_style()"
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -329,7 +329,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x4d7a198>"
|
||||
"<matplotlib.figure.Figure at 0x4ca8c50>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -337,6 +337,7 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import book_format\n",
|
||||
"import book_plots\n",
|
||||
"with book_format.figsize(y=1.5):\n",
|
||||
" book_plots.plot_errorbars([(160, 8, 'A'), (170, 8, 'B')], xlims=(145, 185))"
|
||||
@@ -364,7 +365,7 @@
|
||||
"data": {
|
||||
"image/png": 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||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x6ca35c0>"
|
||||
"<matplotlib.figure.Figure at 0x4ca8d68>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -396,7 +397,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x73def98>"
|
||||
"<matplotlib.figure.Figure at 0x738fd68>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -432,7 +433,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Average of measurements is 165.0121\n"
|
||||
"Average of measurements is 165.0058\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -463,7 +464,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Average of measurements is 165.0270\n"
|
||||
"Average of measurements is 164.9245\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -496,7 +497,7 @@
|
||||
"data": {
|
||||
"image/png": 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||||
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||||
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|
||||
"<matplotlib.figure.Figure at 0x4ca8b00>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x73390f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -563,7 +564,7 @@
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7590400>"
|
||||
"<matplotlib.figure.Figure at 0x7533b70>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -592,9 +593,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA48AAAEtCAYAAACoIlXDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7623470>"
|
||||
"<matplotlib.figure.Figure at 0x75b1400>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -624,9 +625,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x751b5f8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -661,7 +662,7 @@
|
||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x6c6d128>"
|
||||
"<matplotlib.figure.Figure at 0x73b17b8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -690,7 +691,7 @@
|
||||
"data": {
|
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|
||||
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||||
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|
||||
"<matplotlib.figure.Figure at 0x7426be0>"
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||||
]
|
||||
},
|
||||
"metadata": {},
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||||
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"data": {
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||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x74d70b8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -788,9 +789,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x78da080>"
|
||||
"<matplotlib.figure.Figure at 0x782afd0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -865,9 +866,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7914160>"
|
||||
"<matplotlib.figure.Figure at 0x7858080>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -907,9 +908,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x757c3c8>"
|
||||
"<matplotlib.figure.Figure at 0x7426f28>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1010,7 +1011,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x766e390>"
|
||||
"<matplotlib.figure.Figure at 0x7393fd0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1417,7 +1418,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x73dea58>"
|
||||
"<matplotlib.figure.Figure at 0x737a048>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1508,9 +1509,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x74b6710>"
|
||||
"<matplotlib.figure.Figure at 0x73ea780>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1568,9 +1569,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7589e10>"
|
||||
"<matplotlib.figure.Figure at 0x754f9b0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1631,9 +1632,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x77b3828>"
|
||||
"<matplotlib.figure.Figure at 0x8a0f320>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1698,7 +1699,7 @@
|
||||
"data": {
|
||||
"image/png": 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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x75c6e10>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1776,7 +1777,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7837780>"
|
||||
"<matplotlib.figure.Figure at 0x8a93470>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1817,7 +1818,7 @@
|
||||
"data": {
|
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x8ac0710>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1862,7 +1863,7 @@
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"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x7870e10>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1909,7 +1910,7 @@
|
||||
"data": {
|
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x74b2668>"
|
||||
"<matplotlib.figure.Figure at 0x759e1d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1958,7 +1959,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x74069e8>"
|
||||
"<matplotlib.figure.Figure at 0x7426d68>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2004,7 +2005,7 @@
|
||||
"data": {
|
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||||
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||||
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|
||||
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|
||||
},
|
||||
"metadata": {},
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||||
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"data": {
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"text/plain": [
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|
||||
"<matplotlib.figure.Figure at 0x74fef98>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2100,7 +2101,7 @@
|
||||
"data": {
|
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x74758d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2151,7 +2152,7 @@
|
||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x7406c88>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2182,7 +2183,7 @@
|
||||
"data": {
|
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x8a4f438>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2215,7 +2216,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x77c1ba8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2265,7 +2266,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8a8dda0>"
|
||||
"<matplotlib.figure.Figure at 0x77ca7f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2296,7 +2297,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8adb828>"
|
||||
"<matplotlib.figure.Figure at 0x7587b00>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+23
-22
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -372,7 +372,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x744d828>"
|
||||
"<matplotlib.figure.Figure at 0x6e2b320>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -380,8 +380,9 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from book_format import figsize, set_figsize\n",
|
||||
"import book_plots as bp\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"belief = np.array([1./3, 1./3, 0, 0, 0, 0, 0, 0, 1./3, 0])\n",
|
||||
"set_figsize(y=2)\n",
|
||||
@@ -510,7 +511,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7504160>"
|
||||
"<matplotlib.figure.Figure at 0x6e73748>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -562,7 +563,7 @@
|
||||
"data": {
|
||||
"image/png": 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||||
"text/plain": [
|
||||
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||||
"<matplotlib.figure.Figure at 0x76476a0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -645,7 +646,7 @@
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||||
"data": {
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|
||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x75a6588>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -719,7 +720,7 @@
|
||||
"data": {
|
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -769,7 +770,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x76adda0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -803,7 +804,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
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||||
@@ -839,7 +840,7 @@
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"data": {
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||||
"text/plain": [
|
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|
||||
"<matplotlib.figure.Figure at 0x760bda0>"
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1082,7 +1083,7 @@
|
||||
"data": {
|
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||||
"text/plain": [
|
||||
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||||
"<matplotlib.figure.Figure at 0x77d4a58>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1121,7 +1122,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x7832a58>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1152,7 +1153,7 @@
|
||||
"data": {
|
||||
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||||
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|
||||
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|
||||
"<matplotlib.figure.Figure at 0x77cc470>"
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1183,7 +1184,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x74c3a90>"
|
||||
"<matplotlib.figure.Figure at 0x77f9d68>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1216,7 +1217,7 @@
|
||||
"data": {
|
||||
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||||
"text/plain": [
|
||||
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||||
"<matplotlib.figure.Figure at 0x787bda0>"
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1297,7 +1298,7 @@
|
||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x75c8f60>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1335,7 +1336,7 @@
|
||||
"data": {
|
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||||
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|
||||
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|
||||
"<matplotlib.figure.Figure at 0x76f1b70>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1366,7 +1367,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x77268d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1426,7 +1427,7 @@
|
||||
"data": {
|
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1616,7 +1617,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x7965c50>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1674,7 +1675,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fe3978>"
|
||||
"<matplotlib.figure.Figure at 0x7928668>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1708,7 +1709,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7ec07b8>"
|
||||
"<matplotlib.figure.Figure at 0x776a0b8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+21
-20
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -464,7 +464,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x4c58208>"
|
||||
"<matplotlib.figure.Figure at 0x58f8c50>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -472,8 +472,9 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from book_format import set_figsize, figsize\n",
|
||||
"from gaussian_internal import plot_height_std\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"with figsize(y=2):\n",
|
||||
" plot_height_std([1.8, 2.0, 1.7, 1.9, 1.6])"
|
||||
@@ -499,7 +500,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7b247f0>"
|
||||
"<matplotlib.figure.Figure at 0x88079e8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -686,7 +687,7 @@
|
||||
"data": {
|
||||
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|
||||
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||||
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||||
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|
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|
||||
"metadata": {},
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@@ -780,7 +781,7 @@
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"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x58f8c88>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -817,7 +818,7 @@
|
||||
"data": {
|
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|
||||
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|
||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -855,7 +856,7 @@
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"data": {
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|
||||
"<matplotlib.figure.Figure at 0x88b2c88>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -935,7 +936,7 @@
|
||||
"data": {
|
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||||
"text/plain": [
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||||
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||||
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||||
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||||
},
|
||||
"metadata": {},
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||||
@@ -1063,7 +1064,7 @@
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"data": {
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x88decf8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1110,7 +1111,7 @@
|
||||
"data": {
|
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x8a20908>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1155,7 +1156,7 @@
|
||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8dca9e8>"
|
||||
"<matplotlib.figure.Figure at 0x9aaf0b8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1296,9 +1297,9 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ -2.54712791 0.88513509 1.50475278 -1.61633568 10.94576518\n",
|
||||
" 5.14935637 -0.84490685 5.37194746 2.17711202 2.14500505\n",
|
||||
" 2.43140784 -2.30281256 3.40936751 0.18093421 1.60952931]\n"
|
||||
"[ 4.46094599 2.83048304 3.73452597 -0.1482742 5.86460023 3.02981698\n",
|
||||
" 6.13383308 -2.62593238 1.0436962 2.63004201 6.04516235 1.96553502\n",
|
||||
" 2.02526256 0.95307332 -1.27607559]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1401,7 +1402,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8e2ceb8>"
|
||||
"<matplotlib.figure.Figure at 0x9a4d550>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1456,9 +1457,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8e971d0>"
|
||||
"<matplotlib.figure.Figure at 0x9b774a8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1508,9 +1509,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAADaCAYAAAAMl0zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8e59c18>"
|
||||
"<matplotlib.figure.Figure at 0x9b12278>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -267,10 +267,19 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -441,7 +450,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x79416d8>"
|
||||
"<matplotlib.figure.Figure at 0x6b940b8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -481,9 +490,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7941748>"
|
||||
"<matplotlib.figure.Figure at 0x6c4a4a8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -533,9 +542,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7941438>"
|
||||
"<matplotlib.figure.Figure at 0x6b923c8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -556,9 +565,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8134da0>"
|
||||
"<matplotlib.figure.Figure at 0x73ace80>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -589,7 +598,7 @@
|
||||
"data": {
|
||||
"image/png": 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||||
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||||
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||||
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||||
@@ -621,7 +630,7 @@
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"data": {
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x764c390>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -657,7 +666,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x828ee10>"
|
||||
"<matplotlib.figure.Figure at 0x79a4208>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -665,6 +674,7 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from book_format import set_figsize, figsize\n",
|
||||
"import filterpy.stats as stats\n",
|
||||
"with figsize(y=3):\n",
|
||||
" stats.plot_gaussian(mean=23, variance=5)"
|
||||
@@ -694,7 +704,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x79416a0>"
|
||||
"<matplotlib.figure.Figure at 0x6b94080>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -784,7 +794,7 @@
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8349d68>"
|
||||
"<matplotlib.figure.Figure at 0x75b1ef0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -841,7 +851,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x75b3ef0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -894,7 +904,7 @@
|
||||
"data": {
|
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x84c92e8>"
|
||||
"<matplotlib.figure.Figure at 0x77472b0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -984,7 +994,7 @@
|
||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8685240>"
|
||||
"<matplotlib.figure.Figure at 0x78f7f28>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1236,7 +1246,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x83a0240>"
|
||||
"<matplotlib.figure.Figure at 0x78aab70>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1294,7 +1304,7 @@
|
||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x879d9e8>"
|
||||
"<matplotlib.figure.Figure at 0x78f7eb8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1428,7 +1438,7 @@
|
||||
"data": {
|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
},
|
||||
"metadata": {},
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||||
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||||
"data": {
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||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x7709ba8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1688,7 +1698,7 @@
|
||||
"data": {
|
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x7aba278>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1718,7 +1728,7 @@
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||||
"data": {
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|
||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x9312390>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1842,7 +1852,7 @@
|
||||
"data": {
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x834c550>"
|
||||
"<matplotlib.figure.Figure at 0x77891d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1852,7 +1862,7 @@
|
||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x846e940>"
|
||||
"<matplotlib.figure.Figure at 0x813be10>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2012,7 +2022,7 @@
|
||||
"data": {
|
||||
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x776ecf8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2076,7 +2086,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x760a9e8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2134,7 +2144,7 @@
|
||||
"data": {
|
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truncated
|
||||
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|
||||
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|
||||
"<matplotlib.figure.Figure at 0x9254278>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2197,7 +2207,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x812ff28>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2256,7 +2266,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9e980b8>"
|
||||
"<matplotlib.figure.Figure at 0x777ffd0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2302,7 +2312,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x83a4550>"
|
||||
"<matplotlib.figure.Figure at 0x7a8a278>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2398,9 +2408,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8325fd0>"
|
||||
"<matplotlib.figure.Figure at 0x76df6a0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2491,9 +2501,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9fab358>"
|
||||
"<matplotlib.figure.Figure at 0x77139b0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2580,9 +2590,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xa086828>"
|
||||
"<matplotlib.figure.Figure at 0x79d1d68>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -380,7 +380,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x572bfd0>"
|
||||
"<matplotlib.figure.Figure at 0x4cd89e8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -544,7 +544,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x572bf60>"
|
||||
"<matplotlib.figure.Figure at 0x74913c8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -581,9 +581,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x572b7b8>"
|
||||
"<matplotlib.figure.Figure at 0x4cd8748>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -620,7 +620,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9cd60f0>"
|
||||
"<matplotlib.figure.Figure at 0x9206be0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -628,6 +628,8 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from book_format import set_figsize, figsize\n",
|
||||
"\n",
|
||||
"with figsize(y=5):\n",
|
||||
" mkf_internal.plot_3_covariances()"
|
||||
]
|
||||
@@ -660,7 +662,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9c0be48>"
|
||||
"<matplotlib.figure.Figure at 0x7865e80>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -693,7 +695,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0xa2e29b0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -773,7 +775,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x75cd710>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -808,7 +810,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
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|
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},
|
||||
"metadata": {},
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||||
@@ -851,7 +853,7 @@
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"data": {
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|
||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x91e1ac8>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -905,7 +907,7 @@
|
||||
"data": {
|
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|
||||
"text/plain": [
|
||||
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|
||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -936,7 +938,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0xa4aa1d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -969,7 +971,7 @@
|
||||
"data": {
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9c2b828>"
|
||||
"<matplotlib.figure.Figure at 0x92323c8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1007,7 +1009,7 @@
|
||||
"data": {
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x95e7748>"
|
||||
"<matplotlib.figure.Figure at 0x9232400>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1038,7 +1040,7 @@
|
||||
"data": {
|
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0xa3a82b0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1078,7 +1080,7 @@
|
||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xadac3c8>"
|
||||
"<matplotlib.figure.Figure at 0x9232438>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1114,7 +1116,7 @@
|
||||
"data": {
|
||||
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||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xae1fba8>"
|
||||
"<matplotlib.figure.Figure at 0xa41a208>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1147,7 +1149,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xadbf128>"
|
||||
"<matplotlib.figure.Figure at 0xa341ba8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1196,7 +1198,7 @@
|
||||
"data": {
|
||||
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||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0xa3e7518>"
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1226,7 +1228,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x7731d68>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1261,7 +1263,7 @@
|
||||
"data": {
|
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||||
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|
||||
"<matplotlib.figure.Figure at 0x77b8748>"
|
||||
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|
||||
},
|
||||
"metadata": {},
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||||
@@ -1290,7 +1292,7 @@
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"data": {
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||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0xa48dbe0>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1319,7 +1321,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f510b8>"
|
||||
"<matplotlib.figure.Figure at 0x8b9d320>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1348,7 +1350,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xad4ad30>"
|
||||
"<matplotlib.figure.Figure at 0x7678978>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -425,7 +425,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x560beb8>"
|
||||
"<matplotlib.figure.Figure at 0x5878358>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -886,7 +886,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7eef6d8>"
|
||||
"<matplotlib.figure.Figure at 0x818c128>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1407,9 +1407,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fa6828>"
|
||||
"<matplotlib.figure.Figure at 0x81e3a90>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1419,7 +1419,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f355c0>"
|
||||
"<matplotlib.figure.Figure at 0x82dda58>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1466,9 +1466,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x80dabe0>"
|
||||
"<matplotlib.figure.Figure at 0x9aaa8d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1476,6 +1476,7 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from book_format import set_figsize, figsize\n",
|
||||
"from nonlinear_plots import plot_multiple_gaussians\n",
|
||||
"with figsize(y=5):\n",
|
||||
" plot_multiple_gaussians(Ms[::7], Ps[::7], (0,52), (-1, 2), 75)"
|
||||
@@ -1553,7 +1554,7 @@
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAvEAAAEoCAYAAADR4a6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlczPkfB/DXVFJ0OFJyRIQcuRUi9006LLIIuXOndSzC\nyrFa5V6FrKsWqXUmZ25yLgpdjk1CB901M5/fH7PNTzuT7ZiZ78zs+/l4eKT5fuf7fc9MTe/5fN+f\n94fHGGMghBBCCCGEqAwNrgMghBBCCCGElA0l8YQQQgghhKgYSuIJIYQQQghRMZTEE0IIIYQQomIo\niSeEEEIIIUTFUBJPCCGEEEKIiqEknhBCCCGEEBWjxdWJr169Ch8fHzx48ADv3r1DYGAgXF1di+2z\ncuVKBAQEID09HTY2Nti+fTtatGhRbJ/Pnz8rMmxCCCGEEEJkytDQsMz34WwkPjs7G61bt8bmzZuh\nq6sLHo9XbPuGDRuwadMmbNu2DVFRUTA2Nka/fv2QlZXFUcSEEEIIIYQoB54yrNiqr6+P7du3Y/z4\n8QAAxhjq1KmDOXPmYMmSJQCAvLw8GBsbw8fHB1OnThXfl0biCSGEEEKIKivPSDxn5TTfkpiYiJSU\nFPTv3198m46ODuzs7HDz5s1iSfzXyvMEEOVx7949AEDHjh05joRUFL2W6oNeS/VBr6X6oNdSPVR0\nIFopk/j3798DAExMTIrdbmxsjHfv3pV4v6IfaqLa6HVUH/Raqg96LdUHvZbqg15L1dakSZMK3V/l\nutP8s3aeEEIIIYSQ/xqlHImvXbs2ACAlJQX16tUT356SkiLeJg1dVlJtdHlQfdBrqT7otVQf9Fqq\nD3ot1UNFy2mUciTe3NwctWvXRkREhPi2vLw8XL9+HV27duUwMkIIIYQQQrjH2Uh8dnY2YmNjAQBC\noRCvX7/Go0ePULNmTdSvXx/z5s3D2rVrYWlpiSZNmmDNmjXQ19fHmDFjuAqZEEIIIYQQpcBZEh8V\nFYXevXsDENW5e3l5wcvLCxMmTMDevXvxww8/IDc3F+7u7khPT0fnzp0RERGBqlWrchUyIYQQQggh\nSoGzJL5nz54QCoXf3KcosSeEEEIIIYT8n1LWxBNCCCGEEEJKRkk8IYQQQgghKoaSeEIIIYQQQlQM\nJfGEEEIIIYSoGEriCSGEEEIIUTGUxBNCCCGEEKJiKIknhBBCCCFExVASTwghhBBCiIqhJJ4QQggh\nhBAVQ0k8IYQQQgghKoaSeEIIIYQQQlQMJfGEEEIIIYSoGEriCSGEEEIIUTGUxBNCCCGEEKJiKIkn\nhBBCCCFExVASTwghhBBCiIqhJJ4QQgghhBAVQ0k8IYQQQgghKoaSeEIIIYQQQlQMJfGEEEIIIYSo\nGEriCSGEEEIIUTGUxBNCCCGEEKJiKIknhBBCCCFExVASTwghhBBCiIqhJJ4QQgghhBAVQ0k8IYQQ\nQgghKoaSeEIIIYQQQlQMJfGEEEIIIYSoGEriCSGEEEIIUTGUxBNCCCGEEKJiKIknhBBCCCFExVAS\nTwghhBBCiIqhJJ4QQgghhBAVQ0k8IYQQQgghKoaSeEIIIYQQQlSM0ibxfD4fS5cuRaNGjaCrq4tG\njRph+fLlEAgEXIdGCCGEEEIIp7S4DqAka9euxa5du7B//35YWVnh8ePHmDBhAipXroxly5ZxHR4h\nhBBCCCGcUdokPioqCvb29hgyZAgAwMzMDEOHDsXdu3c5jowQQgghhBBuKW05zaBBg3Dp0iW8ePEC\nABAdHY3Lly9j8ODBHEdGCCGEEEIIt3iMMcZ1ECVZunQp1q9fDy0tLfD5fCxbtgyrV68uts/nz5/F\n/4+NjVV0iIQQQgghhJRZkyZNxP83NDQs8/2Vtpxmy5YtCAwMRHBwMFq2bImHDx9i7ty5aNiwISZN\nmsR1eIQQQgghhHBGaUfiTUxMsGzZMsyePVt8m7e3N/bt21dsxP3rkfjyfIohyuPevXsAgI4dO3Ic\nCakoei3VB72W6oNeS/VBr6V6qGgOq7Q18YwxaGgUD09DQwNK+pmDEEIIIYQQhVHachoHBwesX78e\n5ubmaNGiBR4+fAhfX1+4urpyHRohhBBCCCGcUtok3tfXFwYGBnB3d0dKSgpMTU0xdepUrFixguvQ\nCCGEEEII4ZTSJvFVq1aFj48PfHx8uA6FEEIIIYQQpaK0NfGEEEIIIYQQ6SiJJ4QQQgghRMVQEk8I\nIYQQQoiKoSSeEEIIIYQQFUNJPCGEEEIIISqGknhCCCGEEEJUDCXxhBBCCCGEqBhK4gkhhBBCCFEx\nlMQTQgghhBCiYiiJJ4QQQgghRMVQEk8IIYQQQoiKoSSeEEIIIYQQFUNJPCGEEEIIISqGknhCCCGE\nEEJUDCXxhBBCCCGEqBhK4gkhhBBCCFExlMQTQgghhBCiYiiJJ4QQQgghRMVQEk8IIYQQQoiKoSSe\nEEIIIYQQFUNJPCGEEEIIISqGknhCCCGEEEJUjBbXAZD/sMJCID4eiIsD4uNhGhMDnlAInD8P1K8P\nWFgATZsCNWpwHana2rZtG3Jzc6GpqYnGjRvDwsICzZo1g5YWvTXIw82bN3H16lUUFhbC1NQUdevW\nRefOnVG9enWuQ1MbjDHweDyJ2yMiIpCUlIT27dujRYsWqFSpEgfR/Te8fv0aISEhMDAwgIGBAXr0\n6AETExOuw1IbhYWFeP36NTQ0NNCxY0eJ7Xw+HzweD5qamhxERxSJ/lITxfr8GTh7FvjjD9HXz5/F\nm+qWdJ9OnQB7e2D4cKBVK0DKH2gi3ZcvX3D37l306dNHamKzcOFC5OfnF7vNyMgI9+/fh5mZmaLC\nVBu5ubm4e/cuqlWrhjZt2khsv3DhAry8vIrdpqWlhRUrVmD58uWKClNtZGZm4tatW7h27RquXbuG\nmJgYrF+/HhMnTpTYNzQ0FL/++isAoF69evjll1/w3XffSf29INIlJSXh5s2biI6ORnx8PBo2bIjV\nq1dL7Pfs2TN4eHiIv9fW1sbkyZOxbds2er7L4fHjxwgMDMTLly8RGxuLxMRECAQCDBkyBI6OjhL7\nnzt3DjNmzMDjx49pgEDNURJPFCM+HvjhB+DECYDP///tDRqIRtsbN8a7ggKAx0Od6tWB169FI/Qx\nMUBUlOjf8uVAo0air+PHAxpUDSZNVlYWDh8+jMDAQNy9exdCoRAJCQkwNzeX2HfOnDlgjCEvLw9x\ncXGIjo4Gj8dD/fr1OYhc9QgEAly9ehVnz57FtWvXcP/+fRQWFmLy5MkICAiQ2L9bt2744YcfoKGh\ngeTkZMTHx+PWrVuwsLDgIHrV5u3tDS8vLwgEgmK3v3r1Sur+/fr1Q1paGm7fvo03b95g1KhR4PP5\nGDNmjAKiVW1RUVEYMWIE3rx5U+z29u3bS03i69evj7lz5+LLly94+/YtLly4gB07dsDZ2Rm9e/dW\nVNgqgzGGV69eIS0tDR06dJDYnpSUhM2bN4u/5/F4MDU1hZ6entRjrVixAm/fvsW5c+cwevRoucZO\nuEVJPJGvrCxg3TrAxwcoKBAl3j16iEbW7e1FJTN/e3fvHgCgzteXB3NygAsXRCP3J08CCQnAxInA\nr78CW7eKRumJmJeXF3x9fZGZmQkA0NTUhI2NDdLS0qQm8T///HOx7xlj+PjxI42WlVJQUBDGjRsn\n/p7H46Ft27Zo3Lix1P179+4tkcSkpqZCV1dXrnGqqry8PHz69An16tWT2NawYUMAgLW1Nbp3747u\n3bujQ4cOMDU1lXosJycnODk5QSAQYOXKlVizZg18fX0pif9beno6nj9/ji5dukhsMzMzw5s3b2Bg\nYIAuXbqgXbt2sLCwgKWlpdRjWVlZwc/PT/x9UFAQ1q9fj/fv38stflWSnZ2NK1eu4O7du4iKisLd\nu3eRmpqKjh07IioqSmL/du3aYcOGDWjatCmaNGmCxo0b4+nTpxL75ebmwsPDAw8ePIC+vj7at2+v\niIdDOERJPJEPxoDgYMDTE0hKEt3m6gqsXQvUqVP641Sp8v+EXyAADh8GFi0C7twBrK2BSZNEx6R6\nSwCiWsjMzEx069YN06ZNg4ODg9TRmpLweDwYGxtL3SYUCqFBVz+KGTp0KCwtLWFvb49evXqhS5cu\nMDQ0LNMxatasKafoVNOnT59w+vRpnDhxAufOnUPPnj1x6tQpif0cHR0xfPjwMv18A6IPtgsXLkTD\nhg2Rl5cnq7BVTmZmJq5cuYKIiAhcunQJ0dHR0NHRwefPn6GtrV1sXxMTE0RHR6Np06blqrN2cXHB\n6NGjIRQKZRW+SktJScHQoUOL3VarVi3Ur19f6pwOU1NT/PDDD9885sOHD/H9998jJiYGWlpa+O23\n39C0aVOZx06UCyXxRPYyMoARI4CLF0Xfd+woGjXv3Llix9XUBMaNAxwcAG9vYNMmYO9e4Ngx4Lff\nRLf/x82aNQsuLi5o1aqVTI9769YtuLm54dixY2jRooVMj63M8vLyEBISgmPHjiE4OBiVK1cutr1a\ntWqIiYnhKDr18vbtW4wfPx5Xr14tluylp6dL/QBZpUqVcp/L0NAQbm5u5b6/qhMKhWjYsCHS0tLE\nt1WuXBkdO3bEhw8fpF75aN68eYXO+V+ZaJmamorr16/j2rVrePToESIiIiR+ds3NzTFkyBA0b94c\nnTp1grW1NRo0aFChK6DJycmIiYmBpaUlDh48KLUsh6gfSuKJbH34AAwYADx6BNSqBaxfD0yYINv6\ndX190XHd3IB584AzZ0QfGvbtA8aOld15lNTnz58RFBSEadOmSR2xKamcoCJ2796NmJgYjB49Gg8e\nPFD77jVpaWnYsWMHtmzZgo8fPwIATp48iREjRsj1vHw+H5qamv/JciYTExM8fPgQGLine truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9dd0c50>"
|
||||
"<matplotlib.figure.Figure at 0x80055c0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1602,7 +1603,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9d94c18>"
|
||||
"<matplotlib.figure.Figure at 0x9b15470>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1665,7 +1666,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7ffe278>"
|
||||
"<matplotlib.figure.Figure at 0x828a160>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1853,9 +1854,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9cf77b8>"
|
||||
"<matplotlib.figure.Figure at 0x9b9a4e0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1964,7 +1965,7 @@
|
||||
"data": {
|
||||
"image/png": 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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1974,7 +1975,7 @@
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"data": {
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||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x9b76908>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2033,7 +2034,7 @@
|
||||
"data": {
|
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||||
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|
||||
},
|
||||
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||||
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"data": {
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x81f7d30>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2094,7 +2095,7 @@
|
||||
"data": {
|
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||||
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"data": {
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||||
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||||
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|
||||
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|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2199,7 +2200,7 @@
|
||||
"data": {
|
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||||
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||||
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|
||||
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||||
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"data": {
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||||
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||||
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|
||||
"<matplotlib.figure.Figure at 0x9ee8668>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2272,7 +2273,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
"<matplotlib.figure.Figure at 0x9d00d30>"
|
||||
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|
||||
},
|
||||
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||||
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"data": {
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||||
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|
||||
"<matplotlib.figure.Figure at 0x80a7588>"
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2326,7 +2327,7 @@
|
||||
"data": {
|
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||||
"text/plain": [
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||||
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||||
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||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2360,7 +2361,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x9b64208>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2394,7 +2395,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xabb5fd0>"
|
||||
"<matplotlib.figure.Figure at 0x9d04588>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2431,9 +2432,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9db0358>"
|
||||
"<matplotlib.figure.Figure at 0x9ee0128>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2441,9 +2442,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9827a90>"
|
||||
"<matplotlib.figure.Figure at 0x81adeb8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2553,7 +2554,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7ff40f0>"
|
||||
"<matplotlib.figure.Figure at 0x80d0208>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2683,9 +2684,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xab9c588>"
|
||||
"<matplotlib.figure.Figure at 0x81f74a8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2734,9 +2735,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fddc88>"
|
||||
"<matplotlib.figure.Figure at 0x8224400>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2775,9 +2776,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x80314e0>"
|
||||
"<matplotlib.figure.Figure at 0x9a702b0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2841,9 +2842,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9dae908>"
|
||||
"<matplotlib.figure.Figure at 0x808c6d8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+14
-12
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -584,7 +584,7 @@
|
||||
"data": {
|
||||
"image/png": 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||||
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|
||||
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|
||||
"<matplotlib.figure.Figure at 0x52d8c50>"
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||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -618,7 +618,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x51c91d0>"
|
||||
"<matplotlib.figure.Figure at 0x52d8b38>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -648,7 +648,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x80ac320>"
|
||||
"<matplotlib.figure.Figure at 0x81d1c50>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1018,9 +1018,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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||||
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||||
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|
||||
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||||
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|
||||
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||||
"data": {
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|
||||
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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2069,7 +2069,7 @@
|
||||
"data": {
|
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||||
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|
||||
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||||
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|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2102,7 +2102,7 @@
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"data": {
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|
||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0xa122780>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2183,7 +2183,7 @@
|
||||
"data": {
|
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0xa15dac8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2338,7 +2338,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0xaa14a90>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2401,7 +2401,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xa8ed2e8>"
|
||||
"<matplotlib.figure.Figure at 0xaa2fa20>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2409,6 +2409,8 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from book_format import set_figsize, figsize\n",
|
||||
"\n",
|
||||
"import ukf_internal\n",
|
||||
"with figsize(10, 6):\n",
|
||||
" ukf_internal.show_four_gps()"
|
||||
|
||||
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -350,9 +350,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x553be48>"
|
||||
"<matplotlib.figure.Figure at 0x730b668>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -687,9 +687,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fb16d8>"
|
||||
"<matplotlib.figure.Figure at 0x7b13f98>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -753,9 +753,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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||||
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"<matplotlib.figure.Figure at 0x7b73f28>"
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},
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@@ -856,7 +856,7 @@
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"data": {
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x7aeec88>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -966,7 +966,7 @@
|
||||
"data": {
|
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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"data": {
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|
||||
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|
||||
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|
||||
"<matplotlib.figure.Figure at 0x7a703c8>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1360,7 +1360,7 @@
|
||||
"data": {
|
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||||
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||||
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|
||||
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||||
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||||
},
|
||||
"metadata": {},
|
||||
@@ -1393,7 +1393,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x84a2ef0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1427,7 +1427,7 @@
|
||||
"data": {
|
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||||
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||||
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|
||||
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||||
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"data": {
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||||
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|
||||
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|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1503,7 +1503,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
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|
||||
},
|
||||
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||||
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"data": {
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||||
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|
||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1576,7 +1576,7 @@
|
||||
"data": {
|
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|
||||
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||||
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|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1618,7 +1618,7 @@
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"data": {
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x96dfcc0>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1651,7 +1651,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
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||||
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|
||||
"metadata": {},
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@@ -1661,7 +1661,7 @@
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"data": {
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||||
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|
||||
"<matplotlib.figure.Figure at 0x73a7710>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1703,7 +1703,7 @@
|
||||
"data": {
|
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x84fd898>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1713,7 +1713,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x84fd550>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1755,7 +1755,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
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||||
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|
||||
},
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||||
"metadata": {},
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||||
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"data": {
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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1852,7 +1852,7 @@
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||||
"data": {
|
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9aee2b0>"
|
||||
"<matplotlib.figure.Figure at 0x84fd908>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1862,7 +1862,7 @@
|
||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xa1c6860>"
|
||||
"<matplotlib.figure.Figure at 0x9d05390>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1902,7 +1902,7 @@
|
||||
"data": {
|
||||
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||||
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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
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||||
@@ -1912,7 +1912,7 @@
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||||
"data": {
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x9e1b518>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1947,7 +1947,7 @@
|
||||
"data": {
|
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||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x7b09710>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
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||||
@@ -1957,7 +1957,7 @@
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||||
"data": {
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||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x7b23518>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2041,7 +2041,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fe95c0>"
|
||||
"<matplotlib.figure.Figure at 0x95da668>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2122,7 +2122,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x78670f0>"
|
||||
"<matplotlib.figure.Figure at 0x7a705c0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2156,9 +2156,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x894bc50>"
|
||||
"<matplotlib.figure.Figure at 0x8427f28>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2208,9 +2208,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x89f62e8>"
|
||||
"<matplotlib.figure.Figure at 0x8484940>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2295,7 +2295,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xa318390>"
|
||||
"<matplotlib.figure.Figure at 0x9e1b630>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2303,9 +2303,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7890630>"
|
||||
"<matplotlib.figure.Figure at 0x7b096d8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2313,9 +2313,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9b17d30>"
|
||||
"<matplotlib.figure.Figure at 0x9e75be0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2362,7 +2362,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x9628710>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2370,9 +2370,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8974e48>"
|
||||
"<matplotlib.figure.Figure at 0x963dbe0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2380,9 +2380,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9a7cc88>"
|
||||
"<matplotlib.figure.Figure at 0x84a57f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2491,7 +2491,7 @@
|
||||
"data": {
|
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|
||||
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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
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||||
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"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x8412a58>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2808,7 +2808,7 @@
|
||||
"data": {
|
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|
||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x7b459b0>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3118,7 +3118,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x853ecc0>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3244,7 +3244,7 @@
|
||||
"data": {
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xa70c128>"
|
||||
"<matplotlib.figure.Figure at 0xa744320>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3392,7 +3392,7 @@
|
||||
"data": {
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xa7062e8>"
|
||||
"<matplotlib.figure.Figure at 0x853e9b0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3463,7 +3463,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
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||||
@@ -3473,7 +3473,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x963a048>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3538,7 +3538,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xa340358>"
|
||||
"<matplotlib.figure.Figure at 0x9d5c0f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3548,7 +3548,7 @@
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAEWCAYAAAAw37JZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xa3de198>"
|
||||
"<matplotlib.figure.Figure at 0x9f18ac8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -372,9 +372,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x6ec6668>"
|
||||
"<matplotlib.figure.Figure at 0x73d2240>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -429,9 +429,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x6eb1c18>"
|
||||
"<matplotlib.figure.Figure at 0x73bc1d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -463,9 +463,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7a93cf8>"
|
||||
"<matplotlib.figure.Figure at 0x5508c50>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -473,6 +473,7 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from book_format import set_figsize, figsize\n",
|
||||
"from nonlinear_plots import plot_nonlinear_func\n",
|
||||
"\n",
|
||||
"def g1(x):\n",
|
||||
@@ -504,9 +505,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xa1a6f60>"
|
||||
"<matplotlib.figure.Figure at 0x7b0aef0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -546,9 +547,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xa2acbe0>"
|
||||
"<matplotlib.figure.Figure at 0x5508a20>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -580,8 +581,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"input mean, variance: 0.0031, 0.9991\n",
|
||||
"output mean, variance: -0.1293, 2.4017\n"
|
||||
"input mean, variance: 0.0008, 1.0026\n",
|
||||
"output mean, variance: -0.1255, 2.4077\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -608,9 +609,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7aa5da0>"
|
||||
"<matplotlib.figure.Figure at 0xa889898>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -620,7 +621,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"output mean, variance: -0.0047, 2.2480\n"
|
||||
"output mean, variance: -0.0011, 2.2558\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -652,8 +653,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"linear output mean, variance: -0.2715, 7475.2350\n",
|
||||
"nonlinear output mean, variance: -9.6817, 30478.6547\n"
|
||||
"linear output mean, variance: -0.0657, 7500.9568\n",
|
||||
"nonlinear output mean, variance: -9.2950, 30612.5646\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -688,9 +689,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7bd07f0>"
|
||||
"<matplotlib.figure.Figure at 0x8112b00>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -739,7 +740,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
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|
||||
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|
||||
},
|
||||
"metadata": {},
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@@ -769,7 +770,7 @@
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"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x83b91d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -798,7 +799,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x823cdd8>"
|
||||
"<matplotlib.figure.Figure at 0x7b0ea58>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -827,7 +828,7 @@
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAEWCAYAAACt0rvRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7615080>"
|
||||
"<matplotlib.figure.Figure at 0x873dd68>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -260,7 +260,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -287,9 +287,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x6c01c50>"
|
||||
"<matplotlib.figure.Figure at 0x6e7c7f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -297,10 +297,11 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from numpy.random import normal\n",
|
||||
"import numpy as np\n",
|
||||
"from book_format import set_figsize, figsize\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from nonlinear_plots import plot_nonlinear_func\n",
|
||||
"from numpy.random import normal\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"gaussian=(0., 1.)\n",
|
||||
"data = normal(loc=gaussian[0], scale=gaussian[1], size=500000)\n",
|
||||
@@ -349,7 +350,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x4d3bc88>"
|
||||
"<matplotlib.figure.Figure at 0x4fc88d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -395,14 +396,14 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Difference in mean x=-0.086, y=42.313\n"
|
||||
"Difference in mean x=-0.010, y=42.676\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8367c50>"
|
||||
"<matplotlib.figure.Figure at 0x8649c18>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -457,7 +458,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x84b8160>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -495,7 +496,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
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|
||||
},
|
||||
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||||
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"data": {
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|
||||
"<matplotlib.figure.Figure at 0x8429860>"
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -581,7 +582,7 @@
|
||||
"data": {
|
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x82594a8>"
|
||||
"<matplotlib.figure.Figure at 0x85fdef0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -694,7 +695,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x816c7b8>"
|
||||
"<matplotlib.figure.Figure at 0xa06de10>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -734,14 +735,14 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Difference in mean x=0.357, y=0.308\n"
|
||||
"Difference in mean x=-0.013, y=-0.691\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9e6db00>"
|
||||
"<matplotlib.figure.Figure at 0xa1845c0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -960,7 +961,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1079,7 +1080,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x857cb38>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1156,7 +1157,7 @@
|
||||
"data": {
|
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truncated
|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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"data": {
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||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0xa184518>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1344,7 +1345,7 @@
|
||||
"data": {
|
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||||
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||||
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|
||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
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"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0xa0bb908>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1438,7 +1439,7 @@
|
||||
"data": {
|
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x81deac8>"
|
||||
"<matplotlib.figure.Figure at 0xa22d438>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1511,7 +1512,7 @@
|
||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9e6c710>"
|
||||
"<matplotlib.figure.Figure at 0x78499e8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1603,7 +1604,7 @@
|
||||
"data": {
|
||||
"image/png": 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||||
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||||
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||||
"<matplotlib.figure.Figure at 0x84d1eb8>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1663,7 +1664,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0xa297080>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1766,7 +1767,7 @@
|
||||
"data": {
|
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|
||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x8432978>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1818,7 +1819,7 @@
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"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x848a048>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1920,7 +1921,7 @@
|
||||
"data": {
|
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truncated
|
||||
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||||
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|
||||
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||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2032,7 +2033,7 @@
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"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x853c3c8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2073,7 +2074,7 @@
|
||||
"data": {
|
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||||
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||||
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|
||||
"<matplotlib.figure.Figure at 0x7839320>"
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||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2147,7 +2148,7 @@
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"data": {
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x84f6be0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2266,7 +2267,7 @@
|
||||
"data": {
|
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||||
"text/plain": [
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||||
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||||
"<matplotlib.figure.Figure at 0xa0f1e80>"
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2326,7 +2327,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0xa24b748>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2379,7 +2380,7 @@
|
||||
"data": {
|
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||||
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"data": {
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||||
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||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2818,7 +2819,7 @@
|
||||
"data": {
|
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0xa1c0978>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2828,7 +2829,7 @@
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"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x853c400>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2838,7 +2839,7 @@
|
||||
"data": {
|
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0xa0d4c18>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2908,7 +2909,7 @@
|
||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9d71c50>"
|
||||
"<matplotlib.figure.Figure at 0xa1bae80>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2963,7 +2964,7 @@
|
||||
"data": {
|
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9df9710>"
|
||||
"<matplotlib.figure.Figure at 0xa250ef0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2973,7 +2974,7 @@
|
||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8258828>"
|
||||
"<matplotlib.figure.Figure at 0xa031828>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -2983,7 +2984,7 @@
|
||||
"data": {
|
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||||
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|
||||
"<matplotlib.figure.Figure at 0xa039828>"
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||||
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||||
},
|
||||
"metadata": {},
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"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0xa257780>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3640,7 +3641,7 @@
|
||||
"data": {
|
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||||
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||||
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|
||||
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|
||||
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"data": {
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||||
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|
||||
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|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -3764,7 +3765,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9d6fbe0>"
|
||||
"<matplotlib.figure.Figure at 0x863f7f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -318,7 +318,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x6e4fe10>"
|
||||
"<matplotlib.figure.Figure at 0x78bd8d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -366,7 +366,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x6ea9a58>"
|
||||
"<matplotlib.figure.Figure at 0x794d2e8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -440,7 +440,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x4c1bc50>"
|
||||
"<matplotlib.figure.Figure at 0x59d8908>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -776,9 +776,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7744208>"
|
||||
"<matplotlib.figure.Figure at 0x81addd8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -786,9 +786,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x77e75f8>"
|
||||
"<matplotlib.figure.Figure at 0x8245240>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -796,9 +796,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7833438>"
|
||||
"<matplotlib.figure.Figure at 0x82930f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1018,7 +1018,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x774d7f0>"
|
||||
"<matplotlib.figure.Figure at 0x81aec18>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1611,9 +1611,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7833860>"
|
||||
"<matplotlib.figure.Figure at 0x8289710>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1623,7 +1623,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 0.02369026 0.04261685 0.00220302]\n"
|
||||
"[ 0.02411999 0.04207742 0.00218955]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1645,9 +1645,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7833908>"
|
||||
"<matplotlib.figure.Figure at 0x8289320>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1657,7 +1657,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 0.0196579 0.02107363 0.00153995]\n"
|
||||
"[ 0.02018057 0.02053449 0.00154182]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1682,30 +1682,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7833e10>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 0.02119884 0.04570641 0.00022678]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ekf = run_localization(landmarks[0:2], sigma_vel=1.e-10, sigma_steer=1.e-10,\n",
|
||||
" sigma_range=1.4, sigma_bearing=.05)\n",
|
||||
@@ -1721,30 +1703,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x78335f8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 0.29553148 0.83615222 0.00372413]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ekf = run_localization(landmarks[0:1], sigma_vel=1.e-10, sigma_steer=1.e-10,\n",
|
||||
" sigma_range=1.4, sigma_bearing=.05)\n",
|
||||
@@ -1760,30 +1724,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9abd828>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 0.00868882 0.00877678 0.00077243]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"landmarks = array([[5, 10], [10, 5], [15, 15], [20, 5], [15, 10], \n",
|
||||
" [10,14], [23, 14], [25, 25], [10, 20]])\n",
|
||||
@@ -1823,30 +1769,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9a9d828>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"actual mean=1.30, std=1.13\n",
|
||||
"EKF mean=1.00, std=0.95\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import nonlinear_plots\n",
|
||||
"nonlinear_plots.plot_ekf_vs_mc()"
|
||||
@@ -1863,30 +1790,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9e6d4e0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"actual mean=1.30, std=1.12\n",
|
||||
"UKF mean=1.30, std=1.08\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"nonlinear_plots.plot_ukf_vs_mc(alpha=0.001, beta=3., kappa=1.)"
|
||||
]
|
||||
|
||||
+21
-21
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -322,9 +322,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x4f7bc50>"
|
||||
"<matplotlib.figure.Figure at 0x58b8860>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -398,7 +398,7 @@
|
||||
"data": {
|
||||
"image/png": 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||||
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||||
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||||
]
|
||||
},
|
||||
"metadata": {},
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"data": {
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"text/plain": [
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|
||||
"<matplotlib.figure.Figure at 0x9771f28>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -526,7 +526,7 @@
|
||||
"data": {
|
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x961d7f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -560,7 +560,7 @@
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"data": {
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|
||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x97c0710>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -792,7 +792,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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"data": {
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -948,7 +948,7 @@
|
||||
"data": {
|
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||||
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||||
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||||
"<matplotlib.figure.Figure at 0x964d320>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
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@@ -999,7 +999,7 @@
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"data": {
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0xa99af98>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1038,7 +1038,7 @@
|
||||
"data": {
|
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"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xa54b438>"
|
||||
"<matplotlib.figure.Figure at 0xb289cc0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1162,7 +1162,7 @@
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAABACAYAAAC+2bgWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAABfVJREFUeJzt3F9Ik3scx/Hvo8fZxp52LqKtNkiJIugisFUo9GcE3gTR\nRWAQgUlIEUVBROFNrUA4UDeBwUmwBf2x7CYwKC8EjbrryfxTkF3khSh0cQYFrdi+5+KgNPW4tueR\n/ar3C0T27Pd7/PgT/PAbv81SVRUAAAxSUe4AAADMRTkBAIxDOQEAjEM5AQCM84fbG6TTaS9yAAB+\nMaFQqOS57JwAAMahnAAAxnH9st73/jxb+hZuqWirVe4IPyXrbzPf/qbC3xMwWfqvfzy5DzsnAIBx\nKCcAgHEoJwCAcSgnAIBxKCcAgHEoJwCAcSgnAIBxKCcAgHEoJwCAcSgnAIBxKCcAgHEoJwCAcSgn\nAIBxKCcAgHEoJwCAcSgnAIBxKCcAgHEoJwCAcSgnAIBxKCcAgHEoJwCAcSgnAIBxKCcAgHEoJwCA\ncSgnAIBxKCcAgHEoJwCAcSgnAIBxKCcAgHEsVVU3N0in015lAQD8QkKhUMlz2TkBAIxDOQEAjOP6\nZT0AALzGzgkAYBzKCQBgHMoJAGCcHyqnjo4Oqa2tFb/fL/F4XJ49e7bo+OHhYdm5c6cEAgGJxWJy\n6dIlT8K6zZbJZKS5uVk2bdokPp9PEonEkuUyXTHrNjY2JolEQiKRiPj9flm7dq20tbXJt2/fyp7t\ne+/evRPbtsW27SXJBfzuBgYGZO/evRKLxaSiokJSqVTBOSX3gRZw7949raqq0s7OTn379q2eOHFC\ng8GgTkxMLDg+nU5rOBzWpqYmHR0d1Z6eHrVtW69cuVLoRxWt2GyfP3/Wo0eP6o0bN3Tfvn2aSCQ8\nz/QzKHbdxsfHNZVK6evXr3ViYkIfPXqk4XBYz5w5U/ZsMzKZjNbV1emePXvUtm3PcwFQffz4sba1\ntWlPT48GAgFNpVKLjnfTBwXLaevWrdra2pp3bd26dXr+/PkFx3d0dGgoFNIvX77MXrt8+bJGo9GC\nYYpVbLbvHT9+XHft2uV5pp+Bm3Wbcfr0aa2vr/c6WsnZTp06pS0tLXrz5k0NBoOe5wKQLxgMFiwn\nN32w6Mt6X79+lZcvX0pjY2Pe9cbGRnn+/PmCc168eCHbt2+X6urqvPGTk5Py4cOHH9vO/YBSssGb\ndRsfH5cnT57Mu0e5svX29kpvb69cu3ZNlHdGAMZw0weLltPHjx8lm81KOBzOu75y5UqZmppacM7U\n1NS88TOP/29OKUrJBnfr1tDQIH6/X9avXy/btm2TCxculD3b5OSktLa2yu3btyUQCHiaB4A7bvrA\n89N6lmV5fUsY4v79++I4jty5c0f6+vrk7Nmz5Y4khw4dkmPHjsmWLVvKHQXAHG764I/FnlyxYoVU\nVlbK9PR03vXp6WlZtWrVgnMikci8RpyZH4lESg7qRTa4W7dYLCYiIhs2bJBsNistLS3S3t4ulZWV\nZcvW398vAwMDcvHiRRERUVXJ5XJSVVUl169flyNHjniSDUDx3PTBojsnn88nmzdvlqdPn+Zd7+vr\nk4aGhgXn1NfXy+DgoGQymbzx0WhU1qxZs2iYYpSSDd6tWzablVwuJ7lcrqzZRkZGZGhoaPYrmUyK\n3++XoaEh2b9/v2fZABTPVR8UOjHR3d2tPp9POzs7dWxsTE+ePKm2bc8e7T137pzu3r17dnw6ndZI\nJKIHDhzQkZERffjwoS5fvlyvXr1a8HRGsYrNpqo6OjqqjuNoU1OTxuNxffXqlTqO43k2kxW7brdu\n3dIHDx7omzdv9P3799rd3a3RaFQPHjxY9mxzdXV1cVoPWCKfPn1Sx3HUcRwNBAKaTCbVcZwl6YOC\n5aT633HAmpoara6u1ng8roODg7PPNTc3a21tbd744eFh3bFjhy5btkxXr16tyWTyh37xUhSbraam\nRi3LUsuytKKiYvb776aYdbt7967W1dWpbdsaDAZ148aN2t7ennc8tFzZ5urq6uJ9TsAS6e/vn/f/\n07IsPXz4sKp62wd8KjkAwDh8th4AwDiUEwDAOJQTAMA4lBMAwDiUEwDAOJQTAMA4lBMAwDiUEwDA\nOP8CSdsd9j7kQ7EAAAAASUVORK5CYII=\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xaa62588>"
|
||||
"<matplotlib.figure.Figure at 0xb260c88>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1203,7 +1203,7 @@
|
||||
"data": {
|
||||
"image/png": 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|
||||
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||||
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|
||||
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||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1276,7 +1276,7 @@
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||||
"data": {
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truncated
|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x96c19e8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1319,7 +1319,7 @@
|
||||
"data": {
|
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||||
"text/plain": [
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||||
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|
||||
"<matplotlib.figure.Figure at 0x8f8e400>"
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||||
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|
||||
},
|
||||
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||||
@@ -1378,7 +1378,7 @@
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||||
"data": {
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|
||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x902b2e8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1439,7 +1439,7 @@
|
||||
"data": {
|
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||||
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||||
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|
||||
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||||
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|
||||
},
|
||||
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|
||||
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||||
"data": {
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||||
"text/plain": [
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x97ed908>"
|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1459,7 +1459,7 @@
|
||||
"data": {
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8a4ff98>"
|
||||
"<matplotlib.figure.Figure at 0x96fa8d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1469,7 +1469,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8950208>"
|
||||
"<matplotlib.figure.Figure at 0xa9d5b00>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+16
-14
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -296,7 +296,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x54fbf98>"
|
||||
"<matplotlib.figure.Figure at 0x56e8a90>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -340,7 +340,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x73a2b70>"
|
||||
"<matplotlib.figure.Figure at 0x75be438>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -372,7 +372,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7b3dfd0>"
|
||||
"<matplotlib.figure.Figure at 0x7d69b38>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -512,9 +512,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7bd5b70>"
|
||||
"<matplotlib.figure.Figure at 0x7e05668>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -585,9 +585,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x73ae748>"
|
||||
"<matplotlib.figure.Figure at 0x7de8630>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -614,9 +614,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x73a7748>"
|
||||
"<matplotlib.figure.Figure at 0x7f92240>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -653,9 +653,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7b32f98>"
|
||||
"<matplotlib.figure.Figure at 0x7da1208>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -695,7 +695,7 @@
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAADaCAYAAACYceUaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noQ0AAAmENgAAJBDaAACQ4H9iXqVlk6CqDgAAAABJRU5ErkJggg==\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7d60c88>"
|
||||
"<matplotlib.figure.Figure at 0x7f88be0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -703,6 +703,8 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from book_format import figsize\n",
|
||||
"\n",
|
||||
"from smoothing_internal import *\n",
|
||||
"with figsize(y=3.):\n",
|
||||
" show_fixed_lag_numberline()"
|
||||
@@ -742,7 +744,7 @@
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEWCAYAAAAq41LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7afe6a0>"
|
||||
"<matplotlib.figure.Figure at 0x7d69ba8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+48
-48
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -409,7 +409,7 @@
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAE6CAYAAABAsXcUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x6af55c0>"
|
||||
"<matplotlib.figure.Figure at 0x734df98>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -469,9 +469,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x6af90f0>"
|
||||
"<matplotlib.figure.Figure at 0x7338ac8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -559,7 +559,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.legend.Legend at 0x73b5a20>"
|
||||
"<matplotlib.legend.Legend at 0x7c02400>"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
@@ -568,9 +568,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7285d30>"
|
||||
"<matplotlib.figure.Figure at 0x734d908>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -613,9 +613,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x72d3898>"
|
||||
"<matplotlib.figure.Figure at 0x7c1ec50>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -655,9 +655,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAE6CAYAAAB0yLYIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7289a20>"
|
||||
"<matplotlib.figure.Figure at 0x7acd588>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -694,9 +694,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x747f1d0>"
|
||||
"<matplotlib.figure.Figure at 0x7d21470>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -759,9 +759,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x73f6748>"
|
||||
"<matplotlib.figure.Figure at 0x7c9ac88>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -819,7 +819,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x747f390>"
|
||||
"<matplotlib.figure.Figure at 0x7cea048>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -847,9 +847,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7e10080>"
|
||||
"<matplotlib.figure.Figure at 0x851cd68>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -955,9 +955,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7decc88>"
|
||||
"<matplotlib.figure.Figure at 0x867c278>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1006,9 +1006,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7ee10f0>"
|
||||
"<matplotlib.figure.Figure at 0x87408d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1091,9 +1091,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7dc6780>"
|
||||
"<matplotlib.figure.Figure at 0x7b3cac8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1191,9 +1191,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7dc7f60>"
|
||||
"<matplotlib.figure.Figure at 0x882fc88>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1220,9 +1220,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fa5208>"
|
||||
"<matplotlib.figure.Figure at 0x7adab70>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1249,9 +1249,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x90f2e48>"
|
||||
"<matplotlib.figure.Figure at 0x883ff28>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1259,9 +1259,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x90afbe0>"
|
||||
"<matplotlib.figure.Figure at 0x87e3d30>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1291,9 +1291,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7375f98>"
|
||||
"<matplotlib.figure.Figure at 0x7d0f080>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1301,9 +1301,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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|
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x90af5c0>"
|
||||
"<matplotlib.figure.Figure at 0x7d0fd68>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1311,9 +1311,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7289eb8>"
|
||||
"<matplotlib.figure.Figure at 0x87c1cf8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1321,9 +1321,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7cf6390>"
|
||||
"<matplotlib.figure.Figure at 0x86a2080>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1331,9 +1331,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7e836a0>"
|
||||
"<matplotlib.figure.Figure at 0x9ac0710>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1404,9 +1404,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9069470>"
|
||||
"<matplotlib.figure.Figure at 0x7b235c0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1566,9 +1566,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7de1940>"
|
||||
"<matplotlib.figure.Figure at 0x8693518>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1597,9 +1597,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAE6CAYAAAB0yLYIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x920b9e8>"
|
||||
"<matplotlib.figure.Figure at 0x84e3278>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -296,7 +296,7 @@
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAEjCAYAAABzSrSOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7424358>"
|
||||
"<matplotlib.figure.Figure at 0x6daae80>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -267,7 +267,7 @@
|
||||
"from __future__ import division, print_function\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'./code')\n",
|
||||
"from book_format import load_style, set_figsize, figsize\n",
|
||||
"from book_format import load_style\n",
|
||||
"load_style()"
|
||||
]
|
||||
},
|
||||
@@ -298,9 +298,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7d2e2b0>"
|
||||
"<matplotlib.figure.Figure at 0x764b828>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -570,9 +570,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAt4AAAEPCAYAAAB1HsNIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x89174e0>"
|
||||
"<matplotlib.figure.Figure at 0x8aa04e0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
+63
-1
@@ -55,7 +55,8 @@
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
"collapsed": false,
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -116,6 +117,67 @@
|
||||
" F11=widgets.FloatSliderWidget(value=1, min=0., max=2.),\n",
|
||||
" covar=widgets.FloatSliderWidget(value=0, min=0, max=1.));"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f660f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"from IPython.html.widgets import interact, interactive, fixed\n",
|
||||
"import IPython.html.widgets as widgets\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"import numpy.random as random\n",
|
||||
"\n",
|
||||
"def gen_data(x0, dx, count, noise_factor):\n",
|
||||
" return [x0 + dx*i + random.randn()*noise_factor for i in range (count)]\n",
|
||||
"\n",
|
||||
"def g_h_filter(data, x0, dx, g, h, dt=1., pred=None): \n",
|
||||
" x = x0\n",
|
||||
" results = []\n",
|
||||
" for z in data:\n",
|
||||
" #prediction step\n",
|
||||
" x_est = x + (dx*dt)\n",
|
||||
" dx = dx \n",
|
||||
" if pred is not None:\n",
|
||||
" pred.append(x_est)\n",
|
||||
" \n",
|
||||
" # update step\n",
|
||||
" residual = z - x_est\n",
|
||||
" dx = dx + h * (residual) / dt\n",
|
||||
" x = x_est + g * residual \n",
|
||||
" results.append(x) \n",
|
||||
" return np.array(results)\n",
|
||||
"\n",
|
||||
"zs = gen_data(x0=5, dx=5, count=100, noise_factor=50)\n",
|
||||
"\n",
|
||||
"def interactive_gh(x, dx, g, h):\n",
|
||||
" data = g_h_filter(data=zs, x0=x, dx=dx, dt=1.,g=g, h=h)\n",
|
||||
" plt.plot(zs, color='r')\n",
|
||||
" plt.plot(data, color='k')\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
"interact (interactive_gh, \n",
|
||||
" x=widgets.FloatSliderWidget(value=0., min=-50, max=50.), \n",
|
||||
" dx=widgets.FloatSliderWidget(value=5., min=-50., max=50.), \n",
|
||||
" g=widgets.FloatSliderWidget(value=0.1, min=0.01, max=2, step=.02), \n",
|
||||
" h=widgets.FloatSliderWidget(value=0.02, min=0.0, max=0.5, step=0.01));"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
@@ -11,7 +11,13 @@ Github is able to render the notebooks directly. The quickest way to view a note
|
||||
|
||||
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. You may use [*this nbviewer link*](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb) to access my book via nbviewer. If you read my book today, and then I make a change tomorrow, when you go back tomorrow you will see that change.
|
||||
|
||||
If you have comments, you can write an issue so that everyone can read it along with my response. Please don't view it as a way to report bugs only. Alternatively I've created a gitter room for more informal discussion. [](https://gitter.im/rlabbe/Kalman-and-Bayesian-Filters-in-Python?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
|
||||
The Preface contains instructions on how to host the book online, for free, on a cloud server (cloud.sagemath.com) that allows you to run and alter the code insde the Notebooks. It takes 5 minutes to set up.
|
||||
|
||||
|
||||
Issues or Questsions
|
||||
------
|
||||
|
||||
If you have comments, you can write an issue at GitHub so that everyone can read it along with my response. Please don't view it as a way to report bugs only. Alternatively I've created a gitter room for more informal discussion. [](https://gitter.im/rlabbe/Kalman-and-Bayesian-Filters-in-Python?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
|
||||
|
||||
|
||||
The preface available from the link above has all the information in this README and more, so feel free to follow the link now.
|
||||
@@ -56,7 +62,8 @@ All of the filters used in this book as well as others not in this book are impl
|
||||
|
||||
In Development
|
||||
--------------
|
||||
This book is still very much in development. The earlier chapters have settled down a lot, but I am still adding examples to the UKF chapter, and the EKF chapter is largely unwritten. Early chapters are now pretty solid up to, say, the Kalman Filter Math chapter.
|
||||
This book is still very much in development.
|
||||
|
||||
|
||||
Motivation
|
||||
-----
|
||||
|
||||
Reference in new issue
Block a user