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This book contains mainly a selection of papers that were presented at the International Workshop on High Performance Computing/or Computer Graphics and Visualisation, held in Swansea, United Kingdom on 3-4 July 1995. The workshop was sponsored by the HEFCWI Initiative on ·Parallel Computing - Foundations and Applications·, and it has provided the international computer graphics community with a platform for: • assessing and reviewing the impact of the development of high performance computing on the progress of computer graphics and visualisation; • presenting the current use of high performance computing architecture and software tools in computer graphics and visualisation, and the development of parallel graphics algorithms; • identifying potential high performance computing applications in computer graphics and visualisation, and encouraging members of the graphics community to think about their problems from the perspective of parallelism. The book is divided into six sections. The first section, which acts as the introduction of the book, gives an overview of the current state of the art It contains a comprehensive survey, by Whitman, of parallel algorithms for computer graphics and visualisation; and a discussion, by Hansen, on the past, present and future high performance computing applications in computer graphics and visualisation. The second section is focused on the design and implementation of high performance architecture, software tools and algorithms for surface rendering.
This book presents efficient visualization techniques, a prerequisite for the interactive exploration of complex data sets. High performance is demonstrated as a process of devising algorithms for the fast graphics processing units (GPUs) of modern graphics hardware. Coverage includes parallelization on cluster computers with several GPUs, adaptive rendering methods, and non-photorealistic rendering techniques for visualization.
The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics.
Visualization and analysis tools, techniques, and algorithms have undergone a rapid evolution in recent decades to accommodate explosive growth in data size and complexity and to exploit emerging multi- and many-core computational platforms. High Performance Visualization: Enabling Extreme-Scale Scientific Insight focuses on the subset of scientifi
This is the age of data. There are more innovations and more opportunities for interesting work with data than ever before, but there is also an overwhelming amount of quantitative information being published every day. Data visualisation has become big business, because communication is the difference between success and failure, no matter how clever the analysis may have been. The ability to visualize data is now a skill in demand across business, government, NGOs and academia. Data Visualization: Charts, Maps, and Interactive Graphics gives an overview of a wide range of techniques and challenges, while staying accessible to anyone interested in working with and understanding data. Features: Focusses on concepts and ways of thinking about data rather than algebra or computer code. Features 17 short chapters that can be read in one sitting. Includes chapters on big data, statistical and machine learning models, visual perception, high-dimensional data, and maps and geographic data. Contains more than 125 visualizations, most created by the author. Supported by a website with all code for creating the visualizations, further reading, datasets and practical advice on crafting the images. Whether you are a student considering a career in data science, an analyst who wants to learn more about visualization, or the manager of a team working with data, this book will introduce you to a broad range of data visualization methods. Cover image: Landscape of Change uses data about sea level rise, glacier volume decline, increasing global temperatures, and the increasing use of fossil fuels. These data lines compose a landscape shaped by the changing climate, a world in which we are now living. Copyright © Jill Pelto (jillpelto.com).
Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.
In the design of any visual objects, the work becomes much easier if previous designs are utilized. Computer graphics is becoming increasingly important simply because it greatly helps in utilizing such previous designs. Here, "previous designs" signifies both design results and design procedures. The objects designed are diverse. For engineers, these objects could be machines or electronic circuits, as discussed in Chap. 3, ''CA~/CAM. '' Physicians often design models of a patient's organs from computed tomography images prior to surgery or to assist in diagnosis. This is the subject of Chap. 8, "Medical Graphics. " Chapter 7, "Computer Art," deals with the way in which artists use computer graphics in creating beautiful visual images. In Chap. 1, "Computational Geometry," a firm basis is provided for the definition of shapes in designed objects; this is a typical technical area in which computer graphics is constantly making worldwide progress. Thus, the present volume, reflecting international advances in these and other areas of computer graphics, provides every potential or actual graphics user with the essential up-to-date information. There are, typically, two ways of gathering this current information. One way is to invite international authorities to write on their areas of specialization. Usually this works very well if the areas are sufficiently established that it is possible to judge exactly who knows what. Since computer graphics, however, is still in its developmental stage, this method cannot be applied.
Get started with Python for data analysis and numerical computing in the Jupyter notebook About This Book Learn the basics of Python in the Jupyter Notebook Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn Perform highly-efficient numerical computations with Numba, Cython, and ipyparallel Who This Book Is For This book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know. What You Will Learn Install Anaconda and code in Python in the Jupyter Notebook Load and explore datasets interactively Perform complex data manipulations effectively with pandas Create engaging data visualizations with matplotlib and seaborn Simulate mathematical models with NumPy Visualize and process images interactively in the Jupyter Notebook with scikit-image Accelerate your code with Numba, Cython, and IPython.parallel Extend the Notebook interface with HTML, JavaScript, and D3 In Detail Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data. Style and approach This is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook.
In a society in which the use of information technology is becoming commonplace it is natural that pictures and images produced by elec tronic means should be increasing in importance as a means of com munication. Computer graphics have only recently come to the atten tion of the general public, mainly through animated drawings, advertise ments and video games. The quality of the pictures is often such that, unless informed of the fact, people are unaware that they are created with the help of computers. Some simulations, those developed in con nection with the space shuttle for example, represent a great and rapid progress. In industry, computer graphic techniques are used not only for the presentation of business data, but also in design and manufacture processes. Such computer-assisted systems are collectively represented by the acronym CAX. In CAD/CAM (computer-assisted design/manufacture), interactive graphic techniques have attained considerable importance. In CAD/CAM systems a dialogue can be established between the user and the machine using a variety of easy to operate communication devices. Due to the recent developments in hardware and software (for modelling, visual display, etc), a designer is now able to make decisions based on the information presented (plans, perspective drawings, graphics, etc) with the help of interactive, graphic techniques. These constitute the most visible and perhaps most spectacular aspect of CAD/CAM systems.