MISR_View and MISR_Plot: software tools for accessing and visualizing MISR's multispectral, multiangle data
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The MISR Interactive eXplorer (MINX) software functions both as a general-purpose tool to visualize Multiangle Imaging SpectroRadiometer (MISR) instrument data, and as a specialized tool to analyze properties of smoke, dust, and volcanic plumes. It includes high-level options to create map views of MISR orbit locations; scrollable, single-camera RGB (red-greenblue) images of MISR level 1B2 (L1B2) radiance data; and animations of the nine MISR camera images that provide a 3D perspective of the scenes that MISR has acquired. NASA Tech Briefs, September 2008 55 The plume height capability provides an accurate estimate of the injection height of plumes that is needed by air quality and climate modelers. MISR provides global high-quality stereo height information, and this program uses that information to perform detailed height retrievals of aerosol plumes. Users can interactively digitize smoke, dust, or volcanic plumes and automatically retrieve heights and winds, and can also archive MISR albedos and aerosol properties, as well as fire power and brightness temperatures associated with smoke plumes derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Some of the specialized options in MINX enable the user to do other tasks. Users can display plots of top-of-atmosphere bidirectional reflectance factors (BRFs) versus camera-angle for selected pixels. Images and animations can be saved to disk in various formats. Also, users can apply a geometric registration correction to warp camera images when the standard processing correction is inadequate. It is possible to difference the images of two MISR orbits that share a path (identical ground track), as well as to construct pseudo-color images by assigning different combinations of MISR channels (angle or spectral band) to the RGB display channels. This software is an interactive application written in IDL and compiled into an IDL Virtual Machine (VM) ".sav" file.
The goals of this work were to develop tools to visualize and characterize materials information, to explore new alloy compositions and/or processing, to better predict tensile strength and other mechanical properties. The 9–12 wt% Cr (9Cr) steel test data were compiled from several sources. About 3000 records with 30 predictor variables represent 82 unique steel alloy compositions, variations of thermo-mechanical processing steps and temperatures (homogenization, normalization and tempering cycles), test conditions and outcomes. Detailed data processing steps such as visualization and exploratory analysis, including univariate and bivariate analysis, different clustering techniques used to segment the data, statistical post-hoc analysis to verify significance of the findings, feature engineering to identify predictors of importance, and predictive modeling with cross-validation were performed. The outcome of analysis at each step was reviewed in the context of the domain knowledge of this class of steel, to see if there were underlying physical mechanisms that explain statistical relationships. Data analytics techniques and their parameters were fine-tuned to facilitate interpretation of the results as aligned with the insights from domain experts. Here, the ensemble predictive modeling using Random Forest regressor and post-hoc means comparison corroborated domain knowledge on the role of Co which is known to increase strength of steel alloys through solid-solution strengthening and to affect diffusion of alloying elements and precipitates. Alloys with Co content of 0.7–8 wt% had significantly higher mean strength than the ones without (while having Cr locked within 10–11 wt% range in either group and keeping other compositional element ratios fixed). End products of the computational techniques exploration are presented as the tools that can be used in iterative workflow of materials development and testing.
With the realization of NASA's era of great observatories, there are now more than three space-based telescopes operating in different wavebands. This situation provides astronomers with a unique opportunity to simultaneously observe with multiple observatories. Yet scheduling multiple observatories simultaneously is highly inefficient when compared to observations using only one single observatory. Thus, programs using multiple observatories are limited not due to scientific restrictions, but due to operational inefficiencies. At present, multi-observatory programs are conducted by submitting observing proposals separately to each concerned observatory. To assure that the proposed observations can be scheduled, each observatory's staff has to check that the observations are valid and meet all the constraints for their own observatory; in addition, they have to verify that the observations satisfy the constraints of the other observatories. Thus, coordinated observations require painstaking manual collaboration among the observatory staff at each observatory. Due to the lack of automated tools for coordinated observations, this process is time consuming, error-prone, and the outcome of the requests is not certain until the very end. To increase observatory operations efficiency, such manpower intensive processes need to undergo re-engineering. To overcome this critical deficiency, Goddard Space Flight Center's Advanced Architectures and Automation Branch is developing a prototype effort called the Visual Observation Layout Tool (VOLT). The main objective of the VOLT project is to provide visual tools to help automate the planning of coordinated observations by multiple astronomical observatories, as well as to increase the scheduling probability of all observations.
NTV is a tool used to visualize execution traces from message passing systems. It is designed to be ported easily to different trace formats. The first version of NTV uses trace files produced using AIMS Version 2.2. Versions for the native traces from the IBM SP2 and Intel Paragon are nearing completion. A distinctive characteristic of NTV is that it uses static displays rather then time varying displays. This, in combination with mouse controlled zooming, gives the user the ability to get a global view of the execution and a focused view of details. The principal display is a time line display that shows a color coded time line for each processor. The color coding shows the processor status. Messages between processors are indicated by lines connecting the processors involved in the communications. Controls for these message edges let the user show all messages, no messages, or only message between specified sets of processors. Summary (profiling) displays are also produced. These are of two types. One allocates information by processor, e.g., the time the processor was blocked sending, and the other allocates information by function, e.g., the time a function was blocked sending.
CatCost™ enables catalyst researchers to develop comprehensive estimates of the cost to produce pre-commercial catalysts early in the catalyst development process. It includes both web browser and Excel-based tools, each with distinct strengths. The web tool includes powerful visualization capabilities, while the Excel tool allows for high degrees of control and customization by power users. The CatCost™ Python Data Tools are designed to facilitate the transfer of data from the Excel tool to the web tool, so that users can take advantage of both the powerful visualization in the web tool and the higher degree of customization available in the excel tool. Rather than painstakingly copying entries from estimates, the materials, equipment, and spent catalyst libraries from Excel to a JSON format for the web tool, the CatCost™ Data Tools provide an intuitive applet which allows them to simply choose which library or estimate they wish to transfer, and then returns files that integrate smoothly with the web version of the tool.
The JGI Genome Portal (http://genome.jgi.doe.gov) provides unified access to all JGI genomic databases and analytical tools. A user can search, download and explore multiple data sets available for all DOE JGI sequencing projects including their status, assemblies and annotations of sequenced genomes. Genome Portal in the past 2 years was significantly updated, with a specific emphasis on efficient handling of the rapidly growing amount of diverse genomic data accumulated in JGI. A critical aspect of handling big data in genomics is the development of visualization and analysis tools that allow scientists to derive meaning from what are otherwise terrabases of inert sequence. An interactive visualization tool developed in the group allows us to explore contigs resulting from a single metagenome assembly. Implemented with modern web technologies that take advantage of the power of the computer's graphical processing unit (gpu), the tool allows the user to easily navigate over a 100,000 data points in multiple dimensions, among many biologically meaningful parameters of a dataset such as relative abundance, contig length, and G+C content.
Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools.
NOTICE: Geocube has been replaced by EDX Spatial, a modern geospatial platform to map, visualize, query and explore spatial data. Online web mapping tool for visualization and simple analysis of Earth-energy data files from public and DOE related sources. Geocube allows users to upload and visualize their own datasets but also comes preloaded with individual spatial datasets as well as spatial data collections that align to topical themes.
The visual analytic system enables information retrieval within large text collections. Typically, users have to directly and explicitly query information to retrieve it. With this system and process, the reasoning of the user is inferred from the user interaction they perform in a visual analytic tool, and the appropriate information to query, process, and visualize is systematically determined.
The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an innovative, open-source tool for visualizing variable renewable resource forecasts and alerts for significant up- and down-ramps in renewable generation and the consequent system net load. The modular dashboard of RAVIS contains configurable panes for viewing probabilistic time-series forecasts, ramp event alerts on the look-ahead timeline, and spatially resolved resource sites, and forecasts. For comprehensive situational awareness, the tool can add additional data layers from simulation and independent system operator (ISO) market clearing data---including electric line utilization, nodal price, and available generation flexibility---in response to continuously updated renewable forecasts. This paper introduces the RAVIS technology suite employed to provide optimum visualization and flexible design characteristics. The paper also illustrates some use cases of the tool using site-specific solar power forecast data obtained from the IBM Watt-Sun forecasting platform for the California ISO and Midcontinent ISO footprints. The source code for RAVIS is public (https://github.com/ravis-nrel/ravis), and the intended users are forecasters, utility planners, ISO operators, and researchers.
The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an innovative, open-source tool for visualizing variable renewable resource forecasts and alerts for significant up- and down-ramps in renewable generation and the consequent system net load. The modular dashboard of RAVIS contains configurable panes for viewing probabilistic time-series forecasts, ramp event alerts on the look-ahead timeline, and spatially resolved resource sites, and forecasts. For comprehensive situational awareness, the tool can add additional data layers from simulation and independent system operator (ISO) market clearing data—including electric line utilization, nodal price, and available generation flexibility—in response to continuously updated renewable forecasts. This paper introduces the RAVIS technology suite employed to provide optimum visualization and flexible design characteristics. The paper also illustrates some use cases of the tool using site-specific solar power forecast data obtained from the IBM Watt-Sun forecasting platform for the California ISO and Midcontinent ISO footprints. The source code for RAVIS is public (https://github.com/ravis-nrel/ravis), and the intended users are forecasters, utility planners, ISO operators, and researchers.
The Glenn Research Center Communication Analysis Suite (GCAS) includes many analysis tools that can be used to support a wide range of scenarios. It includes a visualization tool that implements the three.js graphics library to display a three-dimensional (3D) representation of its results. This software will enable researchers, engineers, and mission planners to interact intuitively with and understand the results of their analyses, which might not be apparent from raw data. With NASA’s efforts to return humans to the Moon as a part of the Artemis missions, the GCAS has been used extensively for lunar terrain and landing system development analysis, which is vital to ensuring mission achievability and safety. This has created the need to add several significant features to the visualization tool, such as the ability to display the terrain of the lunar surface accurately and to provide information demonstrating how a given region might impact mission objectives. Many of the changes made to the visualization tool can be separated into one of three general advancements: code restructuring to adhere to modern coding standards and practices, new user camera controls for first-person and third-person perspective views, and a terrain generation feature to enable rendering highly accurate terrains based on any celestial body’s digital elevation model (DEM) in GeoTIFF format. These improvements notably elevate the visualization tool's functionality, accuracy, and user interaction while providing a robust foundation for future development.
Ubiquitous web-based visualization is essential to delivering large-scale data visualization to various stakeholders, from the scientist to the board member. These stakeholders will not tolerate a stalled application or a pop-up window asking them to wait for the processing to complete. They require a responsive and interactive visualization environment with high-quality imagery suitable for detailed analysis and boardroom presentations. At Kitware, Inc., we have accomplished web visualization to this point, leveraging state-of-the-art tools like HTML5, CSS3, SVG, Canvas, and WebGL. Solutions that leverage a combination of these technologies are necessary to handle workloads that vary significantly in data size efficiently. However, it is not always practical to move large data to the web client for visualization. Kitware's ParaView as a Service combines client-side visualization using both distributed processing and remote rendering on big data impractical to move. Existing distributed processing and remote rendering solution's interactivity is below the expectations of web-based applications. Our project examined proposed solutions to the areas outlined above in ParaView as a Service. We have investigated concurrent pipelines, streaming images, progressive rendering, and optimization of algorithms and data movement to address these concerns. For the Phase I project, we completed the proposed work plan. As a result, the project produced three prototypes of essential importance for web visualization and the ParaView as a Service community. We created a simple desktop application for an interactive streamline placement prototype, a web-based interactive streamline placement prototype, and a web-based progressive rendering utilizing raytracing prototype. These prototypes relied on the hardening of emerging software toolkits funded by the Department of Energy (DOE) Advanced Scientific Computing Research (ASCR) program (such as ParaView, VTK-m, and Mochi). We blended these components into web-based visualization prototypes that meet the industry's expectations for interactivity and responsiveness. The Phase I project had four essential focus areas: 1. Develop prototype ParaView as a Service backend server using asynchronous, non-blocking design principles. 2. Develop a prototype web application that uses the ParaView as a Service backend server for remote data visualization. 3. Implement image streaming with encoding/compression and progressive rendering capabilities in the proposed platform. 4. Evaluate the prototype developed and summarize observations, including the challenges and pitfalls of our approach. After our successful completion of Phase I, we are strongly positioned to propose a successful Phase II project.
This document describes the test-bed designed for the investigation of passive direction finding, recognition, and classification of speech and sound sources using sensor arrays. The test-bed forms the experimental basis of the Intelligent Small-Scale Spatial Direction Finder (ISS-SDF) project, aimed at furthering digital signal processing and intelligent sensor capabilities of sensor array technology in applications such as rocket engine diagnostics, sensor health prognostics, and structural anomaly detection. This form of intelligent sensor technology has potential for significant impact on NASA exploration, earth science and propulsion test capabilities. The test-bed consists of microphone arrays, power and signal distribution modules, web-based data acquisition, wireless Ethernet, modeling, simulation and visualization software tools. The Acoustic Sensor Array Modeler I (ASAM I) is used for studying steering capabilities of acoustic arrays and testing DSP techniques. Spatial sound distribution visualization is modeled using the Acoustic Sphere Analysis and Visualization (ASAV-I) tool.
Package managers provide ease of access to applications by removing the time-consuming and sometimes completely prohibitive barrier of successfully building, installing, and maintaining the software for a system. A package dependency contains dependencies between all packages required to build and run the target software. Package management system developers, package maintainers, and users may consult the dependency graph when a simple listing is insufficient for their analyses. However, users working in a remote command line environment must disrupt their workflow to visualize dependency graphs in graphical programs, possibly needing to move files between devices or incur forwarding lag. Such is the case for users of Spack, an open source package management system originally developed to ease the complex builds required by supercomputing environments. To preserve the command line workflow of Spack, we develop an interactive ASCII visualization for its dependency graphs. Through interviews with Spack maintainers, we identify user goals and corresponding visual tasks for dependency graphs. We evaluate the use of our visualization through a command line-centered study, comparing it to the system's two existing approaches. We observe that despite the limitations of the ASCII representation, our visualization is preferred by participants when approached from a command line interface workflow.
The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an open-source tool for visualizing variable renewable resource forecasts and ramp alerts for significant up/down ramps in renewable resource and the consequent net-load. The modular dashboard of RAVIS contains configurable panes for viewing- probabilistic time series forecasts, ramp event alerts on the look-ahead timeline, spatially resolved resource sites and forecasts, and system simulation and market clearing data such as transmission lines utilization, nodal prices and available generation flexibility.