Reusing well-written, well-debugged, and well-tested code improves productivity, code quality, and software configurability and relieves pressure on software developers. When you organize your code into self-contained modular units, you can use them as building blocks for your future projects and share them with other programmers, if needed. Understand the benefits and downsides of seven code reuse models so you can confidently reuse code at any development stage. Create static and dynamic libraries in C and Python, two of the most popular modern programming languages. Adapt your code for the real world: deploy shared functions remotely and build software that accesses them using remote procedure calls. Avoid the drawbacks and harness the benefits associated with seven code reuse models. Create static and dynamic libraries in C and Python, deploy shared functions remotely, and build software that makes intelligent use of remote procedure calls. In no time at all, you'll develop the confidence to reuse code at any stage of real-world development. This one-stop solution covers the complete build cycle: editing, compiling, linking, and running a ready program. Apply Linux/macOS power software development tools, such as ld, ldd, ranlib, and nm, to construct and explore state-of-the-art function libraries in C that could be linked with application-specific code either permanently or for the duration of execution. Learn why Python has modules for reuse and how they differ from C object files and libraries. Understand the risks and other negative implications of sharing and reuse. As a bonus, distill the dependencies between your project's components and automate and optimize your build process with the "make" utility. Whether you are an amateur coder or an experienced developer, become a more productive and resourceful programmer by reusing previously written code. What You Need: To compile and run the C examples mentioned in the book, you need a decent C compiler (GCC is the best, but Intel and Microsoft would probably work, too) and a set of C development tools: maker (make), linker (ld), file, strip, ldd, and ranlib. Again, the GNU development toolset works marvels; other toolsets may or may not work. All examples in the book have been tested on a Linux computer but will most likely work on macOS. For the Python examples, a Python-3.x interpreter is all you want. No third-party modules are required.
Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.
Make your good Python code even better by following proven and effective pythonic programming tips. Avoid logical errors that usually go undetected by Python linters and code formatters, such as frequent data look-ups in long lists, improper use of local and global variables, and mishandled user input. Discover rare language features, like rational numbers, set comprehensions, counters, and pickling, that may boost your productivity. Discover how to apply general programming patterns, including caching, in your Python code. Become a better-than-average Python programmer, and develop self-documented, maintainable, easy-to-understand programs that are fast to run and hard to break. Python is one of the most popular and rapidly growing modern programming languages. With more than 200 standard libraries and even more third-party libraries, it reaches into the software development areas as diverse as artificial intelligence, bioinformatics, natural language processing, and computer vision. Find out how to improve your understanding of the spirit of the language by using one hundred pythonic tips to make your code safer, faster, and better documented. This programming style manual is a quick reference of helpful hints and a random source of inspiration. Choose the suitable data structures for searching and sorting jobs and become aware of how a wrong choice may cause your application to be completely ineffective. Understand global and local variables, class and instance attributes, and information-hiding techniques. Create functions with flexible interfaces. Manage intermediate computation results by caching them in files and memory to improve performance and reliability. Polish your documentation skills to make your code easy for other programmers to understand. As a bonus, discover Easter eggs cleverly planted in the standard library by its developers. Polish, secure, and speed-up your Python applications, and make them easier to maintain by following pythonic programming tips. What You Need: You will need a Python interpreter (ideally, version 3.4 or above) and the standard Python library that usually comes with the interpreter.
Departing from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels. Through integrated content, readers can explore fundamental concepts in data analysis while gaining hands-on experience with Python programming, ensuring a holistic understanding of theory and practical application in Python. Emphasizing the practical relevance of data analysis in today's world, the book equips readers with essential skills for success in the field. By advocating for the use of Python, an open-source and versatile programming language, we break down financial barriers and empower a diverse range of learners to access the tools they need to excel. Whether you're a novice seeking to grasp the foundational concepts of data analysis or a seasoned professional looking to enhance your programming skills, this book offers a comprehensive and accessible guide to mastering the art and science of data analysis in social science research. Key Features: Dual-track learning: Offers both Executive and Technical Tracks, catering to readers with varying levels of conceptual and technical proficiency in data analysis. Includes comprehensive quantitative methodologies for quantitative social science studies. Seamless integration: Interconnects key concepts between tracks, ensuring a smooth transition from theory to practical implementation for a comprehensive learning experience. Emphasis on Python: Focuses on Python programming language, leveraging its accessibility, versatility, and extensive online support to equip readers with valuable data analysis skills applicable across diverse domains.
The purpose of this book is to bring together brief descriptions of more than four hundred unique transport systems. The book contains information on narrow-gauge railways and trams, as well as ropeways, overhead railways, and monorails, and several park railways located on the territory of modern Ukraine and Moldova. Also included are military narrow-gauge railways in Romania, built by the Russian army during the First World War. Due to the fact that material for this book was collected using the "crowdsourcing" method through the now-defunct "Younger Brother" website, the book in no way pretends to be complete, accurate, relevant, and scientific. Some data could be outdated, some may be inaccurate. Nevertheless, since this is the first experience of publishing a manuscript of this kind, I consider all the above disadvantages acceptable - provided that the book is not used by the readers to make vital decisions.
Departing from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels. Through integrated content, readers can explore fundamental concepts in data analysis while gaining hands-on experience with Python programming, ensuring a holistic understanding of theory and practical application in Python. Emphasizing the practical relevance of data analysis in today's world, the book equips readers with essential skills for success in the field. By advocating for the use of Python, an open-source and versatile programming language, we break down financial barriers and empower a diverse range of learners to access the tools they need to excel. Whether you're a novice seeking to grasp the foundational concepts of data analysis or a seasoned professional looking to enhance your programming skills, this book offers a comprehensive and accessible guide to mastering the art and science of data analysis in social science research. Key Features: Dual-track learning: Offers both Executive and Technical Tracks, catering to readers with varying levels of conceptual and technical proficiency in data analysis. Includes comprehensive quantitative methodologies for quantitative social science studies. Seamless integration: Interconnects key concepts between tracks, ensuring a smooth transition from theory to practical implementation for a comprehensive learning experience. Emphasis on Python: Focuses on Python programming language, leveraging its accessibility, versatility, and extensive online support to equip readers with valuable data analysis skills applicable across diverse domains.
Make your good Python code even better by following proven and effective pythonic programming tips. Avoid logical errors that usually go undetected by Python linters and code formatters, such as frequent data look-ups in long lists, improper use of local and global variables, and mishandled user input. Discover rare language features, like rational numbers, set comprehensions, counters, and pickling, that may boost your productivity. Discover how to apply general programming patterns, including caching, in your Python code. Become a better-than-average Python programmer, and develop self-documented, maintainable, easy-to-understand programs that are fast to run and hard to break. Python is one of the most popular and rapidly growing modern programming languages. With more than 200 standard libraries and even more third-party libraries, it reaches into the software development areas as diverse as artificial intelligence, bioinformatics, natural language processing, and computer vision. Find out how to improve your understanding of the spirit of the language by using one hundred pythonic tips to make your code safer, faster, and better documented. This programming style manual is a quick reference of helpful hints and a random source of inspiration. Choose the suitable data structures for searching and sorting jobs and become aware of how a wrong choice may cause your application to be completely ineffective. Understand global and local variables, class and instance attributes, and information-hiding techniques. Create functions with flexible interfaces. Manage intermediate computation results by caching them in files and memory to improve performance and reliability. Polish your documentation skills to make your code easy for other programmers to understand. As a bonus, discover Easter eggs cleverly planted in the standard library by its developers. Polish, secure, and speed-up your Python applications, and make them easier to maintain by following pythonic programming tips. What You Need: You will need a Python interpreter (ideally, version 3.4 or above) and the standard Python library that usually comes with the interpreter.
Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.
Reusing well-written, well-debugged, and well-tested code improves productivity, code quality, and software configurability and relieves pressure on software developers. When you organize your code into self-contained modular units, you can use them as building blocks for your future projects and share them with other programmers, if needed. Understand the benefits and downsides of seven code reuse models so you can confidently reuse code at any development stage. Create static and dynamic libraries in C and Python, two of the most popular modern programming languages. Adapt your code for the real world: deploy shared functions remotely and build software that accesses them using remote procedure calls. Avoid the drawbacks and harness the benefits associated with seven code reuse models. Create static and dynamic libraries in C and Python, deploy shared functions remotely, and build software that makes intelligent use of remote procedure calls. In no time at all, you'll develop the confidence to reuse code at any stage of real-world development. This one-stop solution covers the complete build cycle: editing, compiling, linking, and running a ready program. Apply Linux/macOS power software development tools, such as ld, ldd, ranlib, and nm, to construct and explore state-of-the-art function libraries in C that could be linked with application-specific code either permanently or for the duration of execution. Learn why Python has modules for reuse and how they differ from C object files and libraries. Understand the risks and other negative implications of sharing and reuse. As a bonus, distill the dependencies between your project's components and automate and optimize your build process with the "make" utility. Whether you are an amateur coder or an experienced developer, become a more productive and resourceful programmer by reusing previously written code. What You Need: To compile and run the C examples mentioned in the book, you need a decent C compiler (GCC is the best, but Intel and Microsoft would probably work, too) and a set of C development tools: maker (make), linker (ld), file, strip, ldd, and ranlib. Again, the GNU development toolset works marvels; other toolsets may or may not work. All examples in the book have been tested on a Linux computer but will most likely work on macOS. For the Python examples, a Python-3.x interpreter is all you want. No third-party modules are required.
Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.
The book is devoted to the theory of algebraic geometric codes, a subject formed on the border of several domains of mathematics. On one side there are such classical areas as algebraic geometry and number theory; on the other, information transmission theory, combinatorics, finite geometries, dense packings, etc. The authors give a unique perspective on the subject. Whereas most books on coding theory build up coding theory from within, starting from elementary concepts and almost always finishing without reaching a certain depth, this book constantly looks for interpretations that connect coding theory to algebraic geometry and number theory. There are no prerequisites other than a standard algebra graduate course. The first two chapters of the book can serve as an introduction to coding theory and algebraic geometry respectively. Special attention is given to the geometry of curves over finite fields in the third chapter. Finally, in the last chapter the authors explain relations between all of these: the theory of algebraic geometric codes.
Reexamining Economic and Political Reforms in Russia, 1985–2000: Generations, Ideas, and Changes analyzes the impact of generational changes and ideational changes on major political and economic reforms conducted in Russia during the late twentieth century. This book examines how the policy agenda was shaped by the ideas of the generations’ representatives for the “sixtiers” and “seventiers.” Representatives of the generation of “sixtiers” conducted reforms from 1985 to 1991 and invested major efforts in political liberalization but did not pay enough attention to economic reforms. On the other hand, the reformers from the generation of “seventiers,” who were in charge of policy making from 1991 to 1998, were genuinely oriented toward market building but rather insensitive to the democratization of the political regime. This book explores how these differences in ideational agendas produced inconsistent and controversial outcomes from both stages of reforms.
Employ the latest satellite positioning tech with this extensive guide GPS Satellite Surveying is the classic text on the subject, providing the most comprehensive coverage of global navigation satellite systems applications for surveying. Fully updated and expanded to reflect the field's latest developments, this new edition contains new information on GNSS antennas, Precise Point Positioning, Real-time Relative Positioning, Lattice Reduction, and much more. New contributors offer additional insight that greatly expands the book's reach, providing readers with complete, in-depth coverage of geodetic surveying using satellite technologies. The newest, most cutting-edge tools, technologies, and applications are explored in-depth to help readers stay up to date on best practices and preferred methods, giving them the understanding they need to consistently produce more reliable measurement. Global navigation satellite systems have an array of uses in military, civilian, and commercial applications. In surveying, GNSS receivers are used to position survey markers, buildings, and road construction as accurately as possible with less room for human error. GPS Satellite Surveying provides complete guidance toward the practical aspects of the field, helping readers to: Get up to speed on the latest GPS/GNSS developments Understand how satellite technology is applied to surveying Examine in-depth information on adjustments and geodesy Learn the fundamentals of positioning, lattice adjustment, antennas, and more The surveying field has seen quite an evolution of technology in the decade since the last edition's publication. This new edition covers it all, bringing the reader deep inside the latest tools and techniques being used on the job. Surveyors, engineers, geologists, and anyone looking to employ satellite positioning will find GPS Satellite Surveying to be of significant assistance.
This book examines the interplay between key rulers and intellectuals in creating and sustaining popular discourses that often help keep rulers in power. By focusing in particular on the relationship between Putin and Dugin during the early Putin regime, the author zooms in on the questionable honesty in Putin's interest in Dugin's philosophy, and the instrumentality of that philosophy for strategic regime building. Arguing that ideology is largely supported by political philosophies that gain popular traction, the book questions the extent to which rulers are likely to stay faithful to their stated ideologies. Providing on-the-ground insight into Putin's rule, this book appeals to researchers and policymakers studying Post-Soviet Politics.
Pulling together information previously scattered throughout numerous research articles into one detailed resource, this book connects the fundamentals of structure formation during solidification with the practically observed structure and defect patterns in billets and ingots. The author examines the formation of a structure, properties, and defects in the as-cast material in tight correlation to the physical phenomena involved in the solidification and the process parameters. Compiling recent results and data, the book discusses the fundamentals of solidification together with metallurgical and technological aspects of DC casting. It gives new insight and perspective into DC casting research.
Drawing on recent experimental results, this volume defines the kinematic parameters of braiding pultrusion of rod-like products based on polymer composites, and describes the primary dependencies for computing the structural and geometric parameters of such materials. It reports probable time and temperature regimes during molding, estimates their
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