Architects look at thousands of buildings during their training, and study critiques of those buildings written by masters. In contrast, most software developers only ever get to know a handful of large programs well -- usually programs they wrote themselves -- and never study the great programs of history. As a result, they repeat one another's mistakes rather than building on one another's successes.This second volume of The Architecture of Open Source Applications aims to change that. In it, the authors of twenty-four open source applications explain how their software is structured, and why. What are each program's major components? How do they interact? And what did their builders learn during their development? In answering these questions, the contributors to this book provide unique insights into how they think.

Beschrijving van vijfentwintig open source applicaties.

Volume II of the Six Volume Remote Sensing Handbook, Second Edition, is focused on digital image processing including image classification methods in land cover and land use. It discusses object-based segmentation and pixel-based image processing algorithms, change detection techniques, and image classification for a wide array of applications including land use/land cover, croplands, urban studies, processing hyperspectral remote sensing data, thermal imagery, light detection and ranging (LiDAR), geoprocessing workflows, frontiers of GIScience, and future pathways. This thoroughly revised and updated volume draws on the expertise of a diverse array of leading international authorities in remote sensing and provides an essential resource for researchers at all levels interested in using remote sensing. It integrates discussions of remote sensing principles, data, methods, development, applications, and scientific and social context. Features Provides the most up-to-date comprehensive coverage of digital image processing. Highlights object-based image analysis (OBIA) and pixel-based classification methods and techniques of digital image processing. Demonstrates practical examples of image processing for a myriad of applications such as land use/land cover, croplands, and urban. Establishes image processing using different types of remote sensing data that includes multispectral, radar, LiDAR, thermal, and hyperspectral. Highlights change detection, geoprocessing, and GIScience. This volume is an excellent resource for the entire remote sensing and GIS community. Academics, researchers, undergraduate and graduate students, as well as practitioners, decision makers, and policymakers, will benefit from the expertise of the professionals featured in this book, and their extensive knowledge of new and emerging trends.

Get started with Twisted, the event-driven asynchronous networking engine written in Python, and work with higher-level protocols with ease. With this book, you’ll learn how to use Twisted to develop web services, email-enable your applications, write your own web server, and more. Through complete real-world examples, you’ll go deep into Twisted’s collection of object persistence systems, simple network protocols, and helper utilities. And you’ll get recommendations for various plug-ins and add-ons to enhance the basic package. Discover how Twisted brings more power and flexibility to Python programmers.

Organizations big and small have started to realize just how crucial system and application reliability is to their business. Theyâ??ve also learned just how difficult it is to maintain that reliability while iterating at the speed demanded by the marketplace. Site Reliability Engineering (SRE) is a proven approach to this challenge. SRE is a large and rich topic to discuss. Google led the way with Site Reliability Engineering, the wildly successful Oâ??Reilly book that described Googleâ??s creation of the discipline and the implementation thatâ??s allowed them to operate at a planetary scale. Inspired by that earlier work, this book explores a very different part of the SRE space. The more than two dozen chapters in Seeking SRE bring you into some of the important conversations going on in the SRE world right now. Listen as engineers and other leaders in the field discuss: Different ways of implementing SRE and SRE principles in a wide variety of settings How SRE relates to other approaches such as DevOps Specialties on the cutting edge that will soon be commonplace in SRE Best practices and technologies that make practicing SRE easier The important but rarely explored human side of SRE David N. Blank-Edelman is the bookâ??s curator and editor.

Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form. The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the “data-aptitude” built by the material in this book.

Haskell in Depth unlocks a new level of skill with this challenging language. Going beyond the basics of syntax and structure, this book opens up critical topics like advanced types, concurrency, and data processing. Summary Turn the corner from “Haskell student” to “Haskell developer.” Haskell in Depth explores the important language features and programming skills you’ll need to build production-quality software using Haskell. And along the way, you’ll pick up some interesting insights into why Haskell looks and works the way it does. Get ready to go deep! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Software for high-precision tasks like financial transactions, defense systems, and scientific research must be absolutely, provably correct. As a purely functional programming language, Haskell enforces a mathematically rigorous approach that can lead to concise, efficient, and bug-free code. To write such code you’ll need deep understanding. You can get it from this book! About the book Haskell in Depth unlocks a new level of skill with this challenging language. Going beyond the basics of syntax and structure, this book opens up critical topics like advanced types, concurrency, and data processing. You’ll discover key parts of the Haskell ecosystem and master core design patterns that will transform how you write software. What's inside Building applications, web services, and networking apps Using sophisticated libraries like lens, singletons, and servant Organizing projects with Cabal and Stack Error-handling and testing Pure parallelism for multicore processors About the reader For developers familiar with Haskell basics. About the author Vitaly Bragilevsky has been teaching Haskell and functional programming since 2008. He is a member of the GHC Steering Committee. Table of Contents PART 1 CORE HASKELL 1 Functions and types 2 Type classes 3 Developing an application: Stock quotes PART 2 INTRODUCTION TO APPLICATION DESIGN 4 Haskell development with modules, packages, and projects 5 Monads as practical functionality providers 6 Structuring programs with monad transformers PART 3 QUALITY ASSURANCE 7 Error handling and logging 8 Writing tests 9 Haskell data and code at run time 10 Benchmarking and profiling PART 4 ADVANCED HASKELL 11 Type system advances 12 Metaprogramming in Haskell 13 More about types PART 5 HASKELL TOOLKIT 14 Data-processing pipelines 15 Working with relational databases 16 Concurrency

This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with Python, we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using pandas, numpy, and plotnine. Code is provided to prepare common open-source and proprietary financial data sources (CRSP, Compustat, Mergent FISD, TRACE) and organize them in a database. We reuse these data in all the subsequent chapters, which we keep as self-contained as possible. The empirical applications range from key concepts of empirical asset pricing (beta estimation, portfolio sorts, performance analysis, Fama-French factors) to modeling and machine learning applications (fixed effects estimation, clustering standard errors, difference-in-difference estimators, ridge regression, Lasso, Elastic net, random forests, neural networks) and portfolio optimization techniques. Key Features: Self-contained chapters on the most important applications and methodologies in finance, which can easily be used for the reader’s research or as a reference for courses on empirical finance. Each chapter is reproducible in the sense that the reader can replicate every single figure, table, or number by simply copying and pasting the code we provide. A full-fledged introduction to machine learning with scikit-learn based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods. We show how to retrieve and prepare the most important datasets financial economics: CRSP and Compustat, including detailed explanations of the most relevant data characteristics. Each chapter provides exercises based on established lectures and classes which are designed to help students to dig deeper. The exercises can be used for self-studying or as a source of inspiration for teaching exercises.

Doing Math with Python shows you how to use Python to delve into high school–level math topics like statistics, geometry, probability, and calculus. You’ll start with simple projects, like a factoring program and a quadratic-equation solver, and then create more complex projects once you’ve gotten the hang of things. Along the way, you’ll discover new ways to explore math and gain valuable programming skills that you’ll use throughout your study of math and computer science. Learn how to: –Describe your data with statistics, and visualize it with line graphs, bar charts, and scatter plots –Explore set theory and probability with programs for coin flips, dicing, and other games of chance –Solve algebra problems using Python’s symbolic math functions –Draw geometric shapes and explore fractals like the Barnsley fern, the Sierpinski triangle, and the Mandelbrot set –Write programs to find derivatives and integrate functions Creative coding challenges and applied examples help you see how you can put your new math and coding skills into practice. You’ll write an inequality solver, plot gravity’s effect on how far a bullet will travel, shuffle a deck of cards, estimate the area of a circle by throwing 100,000 "darts" at a board, explore the relationship between the Fibonacci sequence and the golden ratio, and more. Whether you’re interested in math but have yet to dip into programming or you’re a teacher looking to bring programming into the classroom, you’ll find that Python makes programming easy and practical. Let Python handle the grunt work while you focus on the math. Uses Python 3

In order to solve a long-standing problem with list fusion, a new compiler transformation, "Call Arity" is developed and implemented in the Haskell compiler GHC. It is formally proven to not degrade program performance; the proof is machine-checked using the interactive theorem prover Isabelle. To that end, a formalization of Launchbury's Natural Semantics for Lazy Evaluation is modelled in Isabelle, including a correctness and adequacy proof.