Software architecture with Python

Today, more than ever, building software is hard. Not only we have to chase ever-changing technological trends, but we also have to grasp business domains that we are building the software for. The latter is often overseen, and it explains why so many projects are doomed to fail. After all, how can you build a solution if you don't understand the problem?Through this book, you will learn the Domain-Driven Design (DDD) methodology which provides a set of core patterns, principles, and practices for analyzing business domains, understanding business strategy, and, most importantly, aligning software design with its business needs. These include Ubiquitous Language, Bounded Contexts, Event Storming, and others. You will see how these practices not only lead to robust implementation of business logic, but also to future-proof software design and architecture. You will also learn the relationship between DDD and other methodologies to ensure that you are able to make architectural decisions

進行測試驅動開發、領域驅動設計及製作事件驅動微服務「這是一本社群期待已久的書籍，讓大家看到現代app架構的核心在Python裡面究竟長怎樣！Harry與Bob展示了依賴反轉原則在這種時尚、動態的語言中有多麼優雅。」—Brandon RhodesPython-patterns.guide作者「截至目前為止，坊間幾乎沒有介紹使用Python編寫可維護大型系統的Python原生教材。本書展示Python

Digital Architecture Beyond Computers explores the deep history of digital architecture, tracing design concepts as far back as the Renaissance and connecting them with the latest software used by des

Use Traefik as a load balancer or a reverse proxy for microservices-based architecture. This book covers Traefik integration for microservices architecture concerns such as service discovery, telemetr

Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it.' Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.The book also introduces modern topics that do not normally appear in mathematical statistics texts but ar

Thoroughly updated with improved pedagogy, the fifth edition of this classic textbook continues to provide students with a clear and comprehensive introduction the fundamentals of the finite element method. New features include enhanced coverage of introductory topics in the context of simple 1D problems, providing students with a solid base from which to advance to 2D and 3D problems; expanded coverage of more advanced concepts, to reinforce students' understanding; over 30 additional solved problems; and downloadable MATLAB, Python, C, Javascript, Fortran and Excel VBA code packages, providing students with hands-on experience, and preparing them for commercial software. Accompanied by online solutions for instructors, this is the definitive text for senior undergraduate and graduate students studying a first course in the finite element method and finite element analysis, and for professional engineers keen to shore up their understanding of finite element fundamentals.

Implement real-time data processing applications on the Raspberry Pi. This book uniquely helps you work with data science concepts as part of real-time applications using the Raspberry Pi as a localized cloud. You'll start with a brief introduction to data science followed by a dedicated look at the fundamental concepts of Python programming. Here you'll install the software needed for Python programming on the Pi, and then review the various data types and modules available. The next steps are to set up your Pis for gathering real-time data and incorporate the basic operations of data science related to real-time applications. You'll then combine all these new skills to work with machine learning concepts that will enable your Raspberry Pi to learn from the data it gathers. Case studies round out the book to give you an idea of the range of domains where these concepts can be applied. By the end of Data Science with the Raspberry Pi, you'll understand that many applications are now de

Master the usage of s-parameters in signal integrity applications and gain full understanding of your simulation and measurement environment with this rigorous and practical guide. Solve specific signal integrity problems including calculation of the s-parameters of a network, linear simulation of circuits, de-embedding, and virtual probing, all with expert guidance. Learn about the interconnectedness of s-parameters, frequency responses, filters, and waveforms. This invaluable resource for signal integrity engineers is supplemented with the open-source software SignalIntegrity, a Python package for scripting solutions to signal integrity problems.