Human knowledge about the cosmos is rapidly increasing as instruments and simulations are generating new data supporting the formation of theory and understanding of the vastness and complexity of the universe. OpenSpace is a software system that takes on the mission of providing an integrated view of all these sources of data and supports interactive exploration of the known universe from the millimeter scale showing instruments on spacecrafts to billions of light years when visualizing the early universe. The ambition is to support research in astronomy and space exploration, science communication at museums and in planetariums as well as bringing exploratory astrographics to the class room. There is a multitude of challenges that need to be met in reaching this goal such as the data variety, multiple spatio-temporal scales, collaboration capabilities, etc. Furthermore, the system has to be flexible and modular to enable rapid prototyping and inclusion of new research results or space mission data and thereby shorten the time from discovery to dissemination. To support the different use cases the system has to be hardware agnostic and support a range of platforms and interaction paradigms. In this paper we describe how OpenSpace meets these challenges in an open source effort that is paving the path for the next generation of interactive astrographics.

Results of planetary mapping are often shared openly for use in scientific research and mission planning. In its raw format, however, the data is not accessible to non-experts due to the difficulty in grasping the context and the intricate acquisition process. We present work on tailoring and integration of multiple data processing and visualization methods to interactively contextualize geospatial surface data of celestial bodies for use in science communication. As our approach handles dynamic data sources, streamed from online repositories, we are significantly shortening the time between discovery and dissemination of data and results. We describe the image acquisition pipeline, the pre-processing steps to derive a 2.5D terrain, and a chunked level-of-detail, out-of-core rendering approach to enable interactive exploration of global maps and high-resolution digital terrain models. The results are demonstrated for three different celestial bodies. The first case addresses high-resolution map data on the surface of Mars. A second case is showing dynamic processes, such as concurrent weather conditions on Earth that require temporal datasets. As a final example we use data from the New Horizons spacecraft which acquired images during a single flyby of Pluto. We visualize the acquisition process as well as the resulting surface data. Our work has been implemented in the OpenSpace software [8], which enables interactive presentations in a range of environments such as immersive dome theaters, interactive touch tables, and virtual reality headsets.

Abstract In this work, we address the challenge of seamlessly visualizing astronomical data exhibiting huge scale differences in distance, size, and resolution. One of the difficulties is accurate, fast, and dynamic positioning and navigation to enable scaling over orders of magnitude, far beyond the precision of floating point arithmetic. To this end we propose a method that utilizes a dynamically assigned frame of reference to provide the highest possible numerical precision for all salient objects in a scene graph. This makes it possible to smoothly navigate and interactively render, for example, surface structures on Mars and the Milky Way simultaneously. Our work is based on an analysis of tracking and quantification of the propagation of precision errors through the computer graphics pipeline using interval arithmetic. Furthermore, we identify sources of precision degradation, leading to incorrect object positions in screen‐space and z‐fighting. Our proposed method operates without near and far planes while maintaining high depth precision through the use of floating point depth buffers. By providing interoperability with order‐independent transparency algorithms, direct volume rendering, and stereoscopy, our approach is well suited for scientific visualization. We provide the mathematical background, a thorough description of the method, and a reference implementation.

We present a recursion scheme based on attribute grammars that can be transparently applied to trees and acyclic graphs. Our recursion scheme allows the programmer to implement a tree traversal and then apply it to compact graph representations of trees instead. The resulting graph traversals avoid recomputation of intermediate results for shared nodes -- even if intermediate results are used in different contexts. Consequently, this approach leads to asymptotic speedup proportional to the compression provided by the graph representation. In general, however, this sharing of intermediate results is not sound. Therefore, we complement our implementation of the recursion scheme with a number of correspondence theorems that ensure soundness for various classes of traversals. We illustrate the practical applicability of the implementation as well as the complementing theory with a number of examples.

This pearl presents a novel technique for constructing a first-order syntax tree directly from a higher-order interface. We exploit circular programming to generate names for new variables, resulting in a simple yet efficient method. Our motivating application is the design of embedded languages supporting variable binding, where it is convenient to use higher-order syntax when constructing programs, but first-order syntax when processing or transforming programs.

Representing a syntax tree using a data type often involves having many similar-looking constructors. Functions operating on such types often end up having many similar-looking cases. Different languages often make use of similar-looking constructions. We propose a generic model of abstract syntax trees capable of representing a wide range of typed languages. Syntactic constructs can be composed in a modular fashion enabling reuse of abstract syntax and syntactic processing within and across languages. Building on previous methods of encoding extensible data types in Haskell, our model is a pragmatic solution to Wadler's "expression problem". Its practicality has been confirmed by its use in the implementation of the embedded language Feldspar.

A new language, Feldspar, is presented, enabling high-level and platform-independent description of digital signal processing (DSP) algorithms. Feldspar is a pure functional language embedded in Haskell. It offers a high-level dataflow style of programming, as well as a more mathematical style based on vector indices. The key to generating efficient code from such descriptions is a high-level optimization technique called vector fusion. Feldspar is based on a low-level, functional core language which has a relatively small semantic gap to machine-oriented languages like C. The core language serves as the interface to the back-end code generator, which produces C. For very small examples, the generated code performs comparably to hand-written C code when run on a DSP target. While initial results are promising, to achieve good performance on larger examples, issues related to memory access patterns and array copying will have to be addressed.

We develop a Haskell library for functional-logic programming, motivated by the implementation of Wired, a relational embedded domain-specific language for describing and analysing digital circuits at the VLSI-layout level. Compared to a previous library for logic programming by Claessen and Ljunglöf, we support residuation, easier creation of logical data types, and pattern matching. We discuss other applications of our library, including test-data generation, and various extensions, including lazy narrowing.