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I do not feel it is necessary to do every single one, as there is a fair amount of repetition of similar exercises - it's all about maximizing the learning-to-effort ratio! If you find mistakes or have suggestions for improvement, please contact me at weiyun(dot)lu(at)gmail(dot)com. Chapter 1 - Computing with formulas Chapter 2 - Loops and lists Chapter 3 - Functions and branching Chapter 4 - User input and error handling Chapter 5 - Array computing and curve plotting Chapter 6 - Dictionaries and strings Chapter 7 - Introduction to classes Chapter 8 - Random numbers and simple games Chapter 9 - Object-oriented programming Reload to refresh your session. Reload to refresh your session. Substantial changes were introduced in the fourth edition, and theSymbolic computation with the aid of SymPy is used to a larger extentThe concept of closures isWe also discuss the difference between. Python 2 and 3 and demonstrate how to use the future module to writeThe most substantial new material in the fifth edition appears towardThe numbering of sections and in particular exercises differs fromTo copy the whole repository to your computer, runIf you find any typo or error, pleaseOur primary recommendation is to install Anaconda - it suffices for the book. However, if you intend to do more advancedUbuntu machine for all that work.The package SciTools is freqently referred to and used in the book. You may want to install SciTools directly from its Git version controlSciTools is hosted at GitHub Make sure you have the Git version control systemGet the SciTools source code and install it byThe minimumTo copy the whole repository to your computer, runIf you find any typo or error, pleaseTo copy the whole repository to your computer, runSciTools is hosted at Googlecode. Make sure you have the Mercurial ( hg ) version control systemGet the SciTools source code and install it byThe minimum. The 13-digit and 10-digit formats both work. Please try again.Please try again.Please try again.
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We have chosen to use the Python programming language because it combines remarkable power with very clean, simple, and compact syntax. Python is easy to learn and very well suited for an introduction to computer programming. Python is also quite similar to Matlab and a good language for doing mathematical computing. The examples in this book integrate programming with appli- tions to mathematics, physics, biology, and ?nance. The reader is - pected to have knowledge of basic one-variable calculus as taught in mathematics-intensive programs in high schools. It is certainly an - vantage to take a university calculus course in parallel, preferably c- taining both classical and numerical aspects of calculus. Although not strictly required, a background in high school physics makes many of the examples more meaningful. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. Register a free business account Langtangen has published over 100 scientific publications and written several books, including papers and a book on Python's potential for scientific computing. He has also developed open source and commercial software systems for computational sciences. Upper-division undergraduates through professionals; general readers.” (F. H. Wild III, Choice, Vol. 47 (8), April, 2010) “It is an authoritative and almost monumental work that covers most aspects of the Python language and its numerical modules. It will also impart a deep knowledge of python, one of today’s most useful languages. I have learned a great deal from this book and recommend it highly.” (George Hacken, ACM Computing Reviews, September, 2010) Full content visible, double tap to read brief content. Videos Help others learn more about this product by uploading a video. Upload video To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average.
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Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzes reviews to verify trustworthiness. Please try again later. A. P. Chamberlain 5.0 out of 5 stars The book's great for that. As a side benefit I realized as soon as I opened the book that I also now had a clearer tutorial for Python than any other I've seen. Like many of the other posters here, I originally tried to learn Python from looking at bits and pieces of code online and at various reference manuals, but didn't get very far. Nor have I found any of the other introductory books, even O'Reilly's canonical Learning Python, to be much help. This book filled in what I was missing. Much as with Perl, Python is a deceptively simple language, and many people are productive in it just by tweaking code they have found on the Web a bit and deploying it; but beyond this level is an extraordinarily powerful tool with a number of unique features that can only be appreciated and put to good use with a bit of hand-holding and careful walkthroughs of well-crafted code. You could almost see this book as providing that kind of clear, comprehensive understanding of the language, with numerical programming as simply a vehicle by which to accomplish that.The main application areas covered are calculating the value of functions, both built in and user supplied, plotting data, finding the roots of equations, difference equations, numerical differentiation, numerical integration and the solution to differential equations by numerical methods. Two main highlights of the book are the thorough explanations the author provides on how to use most of the features of Python and the copious number of examples with answers. Other features are an example on how to extract data from a Web Page and scitools. Scitools provides a Matlab type of interface to gnuplot. About the only thing missing is a summary on how to install Numpy, Scipy, Scitools, gunplot, and gnuplot.
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py.When I got this book, I was told by my professor to learn the basics, but this book isn't meant for that. This book is built towards making your own projects and programs through Python, so in my experience, it was a little lacking, but if you already know a bit of Python, though, this book is a great companion.If you work with number crunching aspect of python, I'd recommend buying this book.I do a lot of scientific programming and was looking to extend myself into Python. The material presented in this book is too basic. I did not find it very helpful for most of my Python code.Is more designed for some type of class, for clueless science types(i.e. computational science people would just glance through this book).Until now, I haven't been able to find a handy desk reference. This is the book I've been seeking for years. Unlike most of the other Python books (usually with animals on the cover) written for computer science folk, this one is written for people like me trying to do science. The simple examples for basic science tasks are perfect. I certainly will be recommending this to other new faculty learning Python.On the one hand, this makes the author to explain things absolutely obvious, clearly oriented to students in the first years of their technical degree. On the other hand, some of these explanations become handy if you have to teach this material or even, sometimes, to learn the origins of something that you have accepted as obvious without knowing exactly why is so. This is particularly relevant in those parts dealing with mathematics (many in the book). I clearly recommend this book for such target users. The book is also excellently well written, with a clear and concise style. Errors seem to be absent from the text and exercises are very well targeted to the area of scientific computation.To be honest, the book could have been a bit more concise. But better too much than too less.
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It has to be mentioned that this book is not a introduction to Python. Even though the main concepts get a short review I would recommend it only to those you are familiar to the concepts of this programming language. If thats the case I can only recommend buying this book. And by the way: It really proofs much easier to do all the programming and computong on Linux since it is somewhat cumbersome to install all the required components on Windows, besides some features wont work on Windows! Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-point precision and algorithm stability, and extensive online resources support further study. This textbook represents a targeted package for students requiring a solid foundation in Python programming. A broad introduction to Python programming in the sciencesGeneral scientific programming Appendix A. Solutions Appendix B. Differences between Python versions 2 and 3 Appendix C. SciPy's odeint ordinary differential equation solver Glossary Index.
Look Inside Copyright Information Page (42 KB) Front Matter (122 KB) Table of Contents (49 KB) Marketing Excerpt (98 KB) Index (85 KB) Access to locked resources is granted exclusively by Cambridge University Press to lecturers whose faculty status has been verified. To gain access to locked resources, lecturers shouldOther lecturers may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.Lecturers are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.He has over 25 years' experience of programming in the physical sciences and has been programming in Python for 15 years. His research uses Python to produce, analyze, process, curate and visualize large data sets in the area of spectroscopy and plasma physics and material science. Create an account now. If you are having problems accessing these resources please emailYour eBook purchase and download will be. We first Page 516 and 517: 9.5 Summary 485 Reading from the Co Page 518 and 519: 9.5 Summary 487 The next example re Page 520 and 521: 9.6 Exercises 489 the subclass.Thank you, for helping us keep this platform clean. The editors will have a look at it as soon as possible. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Solutions Manual For Python Programming. To get started finding Solutions Manual For Python Programming, you are right to find our website which has a comprehensive collection of manuals listed. Our library is the biggest of these that have literally hundreds of thousands of different products represented. I get my most wanted eBook Many thanks If there is a survey it only takes 5 minutes, try any survey which works for you.
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And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Learning Scientific Programming With Python. To get started finding Learning Scientific Programming With Python, you are right to find our website which has a comprehensive collection of manuals listed. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Solutions Manual Numerical Analysis Timothy Sauer Pdf Download. To get started finding Solutions Manual Numerical Analysis Timothy Sauer Pdf Download, you are right to find our website which has a comprehensive collection of manuals listed. We have chosen to use the Python programming language because it combines remarkable expressive power with very clean, simple, and compact syntax. Python is also quite similar to MATLAB and a good language for doing mathematical computing. The examples in this book integrate programming with applications to mathematics, physics, biology, and finance. The reader is expected to have knowledge of basic one-variable calculus as taught in mathematicsintensive programs in high schools. It is certainly an advantage to take a university calculus course in parallel, preferably containing both classical and numerical aspects of calculus. Although not strictly required, a background in high school physics makes many of the examples more meaningful. Many introductory programming books are quite compact and focus on listing functionality of a programming language. However, learning to program is learning how to think as a programmer. This book has its main focus on the thinking process, or equivalently: programming as a problem solving technique. That is why most of the pages are devoted to case studies in programming, where we define a problem and explain how to create the corresponding program. New constructions and programming styles (what we could call theory) is also usually introduced via examples.
Particular attention is paid to verification of programs and to finding errors. These topics are very demanding for mathematical software, because the unavoidable numerical approximation errors are possibly mixed with programming mistakes. v Remember, nobody can learn programming by just reading - one has to solve a large amount of exercises hands on. The book is therefore full of exercises of various types: modifications of existing examples, completely new problems, or debugging of given programs. To work with this book, I recommend using Python version 2.7. For Chapters 5-9 and Appendices A-E you need the NumPy and Matplotlib packages, preferably also the IPython and SciTools packages, and for Appendix G Cython is required. Other packages used occasionally in the text are nose and sympy. Section H.1 has more information on how you can get access to Python and the mentioned packages. However, there is still a problem that much useful mathematical software in Python has not yet been ported to Python 3. Therefore, scientific computing with Python still goes mostly with version 2. A widely used strategy for software developers who want to write Python code that works with both versions, is to develop for version 2.7, which is very close to what is found version 3.4, and then use the translation tool 2to3 to automatically translate from Python 2 to Python 3. When using v2.7, you should employ the newest syntax and modules that make the differences between Python 2 and 3 very small. This strategy is adopted in the present book. Chapter 2 presents programming with while and for loops as well as lists, including nested lists. The next chapter deals with two other fundamental concepts in programming: functions and if-else tests. Successful further reading of the book demands that Chapters 1-3 are digested. Many of the examples in the first five chapters are strongly related.
Typically, formulas from the first chapter are used to produce tables of numbers in the second chapter. Then the formulas are encapsulated in functions in the third chapter. In the next chapter, the input to the functions are fetched from the command line, or from a question-answer dialog with the user, and validity checks of the input are added. The formulas are then shown as graphs in Chapter 5. After having studied Chapters 1-5, the reader should have enough knowledge of programming to solve mathematical problems by what many refer to as MATLAB-style programming. Chapter 6 explains how to work dictionaries and strings, especially for interpreting text data in files and storing the extracted information in flexible data structures. Class programming, including user-defined types for mathematical computations (with overloaded operators), is introduced in Chapter 7. Chapter 8 deals with random numbers and statistical computing with applications to games and random walks. Object-oriented programming, in the meaning of class hierarchies and inheritance, is the subject of Chapter 9. The key examples here deal with building toolkits for numerical differentiation and integration as well as graphics. Appendix A introduces mathematical modeling, using sequences and difference equations. Only programming concepts from Chapters 1-5 are used in this appendix, the aim being to consolidate basic programming knowledge and apply it to mathematical problems. Some important mathematical topics are introduced via difference equations in a simple way: Newton s method, Taylor series, inverse functions, and dynamical systems. Appendix B deals with functions on a mesh, numerical differentiation, and numerical integration. A simple introduction to ordinary differential equations and their numerical treatment is provided in Appendix C. Appendix D shows how a complete project in physics can be solved by mathematical modeling, numerical methods, and programming elements from Chapters 1-5.
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This project is a good example on problem solving in computational science, where it is necessary to integrate physics, mathematics, numerics, and computer science. How to create software for solving ordinary differential equations, using both function-based and object-oriented programming, is the subject of Appendix E. The material in this appendix brings together many parts of the book in the context of physical applications. Appendix F is devoted to the art of debugging, and in fact problem solving in general. Speeding up numerical computations in Python by Finally, Appendix H deals with various more advanced technical topics. Most of the examples and exercises in this book are quite short. Changes from the third to the fourth edition. A large number of the exercises have been reformulated and reorganized. Typically, longer exercises are divided into subpoints a), b), c), etc., various type of help is factored out in separate paragraphs marked with Hint, and information that puts the exercise into a broader context is placed at the end under the heading Remarks. Quite some related exercises have been merged. Another major change is the enforced focus on testing and verification. Already as soon as functions are introduced in Chapter 3, we start verifying the implementations through test functions written according to the conventions in the nose testing framework. This is continued throughout the book and especially incorporated in the reformulated exercises. Testing is challenging in programs containing unknown approximation errors, so strategies for finding appropriate test problems have also become an integral part of the fourth edition. Many chapters now refer to the Online Python Tutor for visualizing the program flow. This is a splendid tool for learning what happens with the variables and execution of statements in small programs. The sympy package for symbolic computing is a powerful tool in scientific programming and introduced already in Chapter 1.
The sections in Chapter 4 have been reorganized, and the basic information on file reading and writing was moved from Chapter 6 to Chapter 4. The fourth edition clearly features three distinct parts: basic programming concepts in Chapters 1-5, more advanced programming concepts in Chapters 6-9, and programming for solving science problems in Appendix A-E. Sections 4.9 and have been rewritten to emphasize the importance of test functions. The information on how to make animations and Section has been completely rewritten to better reflect how to work with data associated with dates. Appendix E has been reworked so that function-based programming and object-oriented programming appear in separate sections. This allows reading the appendix and solving ODEs without knowledge of classes and inheritance. Much of the text in Appendix E has been rewritten and extended, the exercises are substantially revised, and several new exercises have been added. Section H.1 is new and describes the various options for getting access to Python and its packages for scientific computations. This topic includes, e.g., installing software on personal laptops and writing notebooks in cloud services. In addition to the mentioned changes, a large number of smaller updates, improved explanations, and correction of typos have been incorporated in the new edition. I am very thankful to all the readers, instructors, and students who have sent s with corrections or suggestions for improvements. The perhaps biggest change for me was to move the whole manuscript from L A TEX to DocOnce 1. This move enables a much more flexible composition of topics for various purposes, and support for output in different formats such as L A TEX, HTML, Sphinx, Markdown, IPython notebooks, and MediaWiki. The chapters have been made more independent by repeating key knowledge, which opens up for meaningful reading of only parts of the book, even the most advanced parts. Acknowledgments.
This book was born out of stimulating discussions with my close colleague Aslak Tveito, and he started writing what is now Appendix B and C. The whole book project and the associated university course were critically dependent on Aslak s enthusiastic role back in The continuous support from Aslak regarding my book projects is much appreciated and contributes greatly to my strong motivation. Another key contributor in the early days was Ilmar Wilbers. He made extensive efforts with assisting the book project and establishing the university course INF1100. I feel that without Ilmar and his solutions to numerous technical problems the first edition of the book would never have been completed. Johannes H. Ring also deserves special acknowledgment for the development of the Easyviz graphics tool and for his careful maintenance and support of software associated with the book over the years. Professor Loyce Adams studied the entire book, solved all the exercises, found numerous errors, and suggested many improvements. Her contributions are so much appreciated.I am so thankful for all his efforts and for his enthusiasm during the preparations of the fourth edition. Special thanks go to Geir Kjetil Sandve for being the primary author of the computational bioinformatics examples in Sections 3.3, 6.5, 8.3.4, and 9.5, with contributions from Sveinung Gundersen, Ksenia Khelik, Halfdan Rydbeck, and Kai Trengereid. Several people have contributed with suggestions for improvements of the text, the exercises, and the associated software. Hakon Adler is greatly acknowledged for his careful reading of early various versions of the manuscript. I also appreciate the cover image made by my good friend Jan Olav Langseth. This book and the associated course are parts of a comprehensive and successful reform at the University of Oslo, called Computing in Science Education.
The goal of the reform is to integrate computer programming and simulation in all bachelor courses in natural science where mathematical models are used. The present book lays the foundation for the modern computerized problem solving technique to be applied in later courses. The excellent assistance from the Springer system, in particular Martin Peters, Thanh-Ha Le Thi, Ruth Allewelt, Peggy Glauch-Ruge, Nadja Kroke, Thomas Schmidt, Patrick Waltemate, Donatas Akmanavicius, and Yvonne Schlatter, is highly appreciated, and ensured a smooth and rapid production of all editions of this book. Oslo, March 2014 Hans Petter Langtangen Beyond mathematical functions Multiple return values Computing sums Functions with no return values Keyword arguments Doc strings Functions as arguments to functions The main program Lambda functions Branching If-else blocks Inline if tests Mixing loops, branching, and functions in bioinformatics examples Counting letters in DNA strings Efficiency assessment Verifying the implementations Summary Chapter topics Example: Numerical integration Exercises User input and error handling Asking questions and reading answers Reading keyboard input Reading from the command line Providing input on the command line A variable number of command-line arguments More on command-line arguments Turning user text into live objects The magic eval function The magic exec function Turning string expressions into functions Option-value pairs on the command line Basic usage of the argparse module Mathematical expressions as values Reading data from file Reading a file line by line Alternative ways of reading a file Reading a mixture of text and numbers Writing data to file Example: Writing a table to file Standard input and output as file objects What is a file, really? You will learn how to write and run a Python program, how to work with variables, how to compute with mathematical functions such as e x and sin x, and how to use Python for interactive calculations.
We assume that you are somewhat familiar with computers so that you know what files and folders are, how you move between folders, how you change file and folder names, and how you write text and save it in a file. Another frequent word for folder is directory. I strongly recommend you to visit this page, download and pack out the files. The examples are organized in a folder tree with src as root. Each subfolder corresponds to a particular chapter. For example, the subfolder formulas contains the program examples associated with this first chapter. The relevant subfolder name is listed at the beginning of every chapter. The folder structure with example programs can also be directly accessed in a GitHub repository 1 on the web. You can click on the formulas folder to see all the examples from the present chapter. Clicking on a filename shows a nicely typeset version of the file. The file can be downloaded by first clicking Raw to get the plain text version of the file, and then right-clicking in the web page and choosing Save As 32 2 1 Computing with formulas 1.1 The first programming encounter: a formula The first formula we shall consider concerns the vertical motion of a ball thrown up in the air. Most computer languages look somewhat similar to English, but they are very much simpler. The number of words and associated instructions is very limited, so to perform a complicated operation we must combine a large number of different types of instructions. The program text, containing the sequence of instructions, is stored in one or more files. The computer can only do exactly what the program tells the computer to do. Another perception of the word program is a file that can be run ( double-clicked ) to perform a task. Sometimes this is a file with textual instructions (which is the case with Python), and sometimes this file is a translation of all the program text to a more efficient and computerfriendly language that is quite difficult to read for a human.
All the programs in this chapter consist of short text stored in a single file. Other programs that you have used frequently, for instance Firefox or Internet Explorer for reading web pages, consist of program text distributed over a large number of files, written by a large number of people over many years. One single file contains the machine-efficient translation of the whole program, and this is normally the file that you double-click on when starting the program. In general, the word program means either this single file or the collection of files with textual instructions. Programming is obviously about writing programs, but this process is more than writing the correct instructions in a file. First, we must understand how a problem can be solved by giving a sequence of instructions to the computer. This is one of the most difficult things with programming. Second, we must express this sequence of instructions correctly in a computer language and store the corresponding text in a file (the program). This is normally the easiest part. Third, we must find out how to check the validity of the results. Usually, the results are not as expected, and we need to a fourth phase where we systematically track down the errors and correct them. Mastering these four steps requires a lot of training, which means making a large number of programs (exercises in this book, for instance!) and getting the programs to work Tools for writing programs There are three alternative types of tools for writing Python programs: a plain text editor an integrated development environment (IDE) with a text editor an IPython notebook What you choose depends on how you access Python. Section H.1 contains information on the various possibilities to install Python on your own 34 4 1 Computing with formulas computer, access a pre-installed Python environment on a computer system at an institution, or access Python in cloud services through your web browser.