Title: TXM-Sandbox : an open-source software for transmission X-ray microscopy data analysis
A transmission X-ray microscope (TXM) can investigate morphological and chemical information of a tens to hundred micrometre-thick specimen on a length scale of tens to hundreds of nanometres. It has broad applications in material sciences and battery research. TXM data processing is composed of multiple steps. A workflow software has been developed that integrates all the tools required for general TXM data processing and visualization. The software is written in Python and has a graphic user interface in Jupyter Notebook . Users have access to the intermediate analysis results within Jupyter Notebook and have options to insert extra data processing steps in addition to those that are integrated in the software. The software seamlessly integrates ImageJ as its primary image viewer, providing rich image visualization and processing routines. As a guide for users, several TXM specific data analysis issues and examples are also presented. more »« less
This paper introduces a lidar and radar meteorology science gateway deployed on the NSF Jetstream2 cloud, designed to enhance educational and research activities in atmospheric science. Utilizing the "Zero to JupyterHub with Kubernetes" workflow, we have created a science gateway that integrates lidar and radar meteorology software packages, notably the Lidar Radar Open Software Environment (LROSE). This integration allows users to execute applications directly from the JupyterLab terminal, streamlining the creation of datasets for further anal- ysis and visualization within Jupyter notebooks. By combining traditional command-line operations with modern Python-based tools for data analysis and visualization, this gateway provides a robust end-to-end solution that caters to both educational and research needs. The gateway has already been vital in facilitating LROSE instructional workshops and will see future enhancements such as GPU acceleration to boost performance. Our work demonstrates the significant potential of merging established scientific computing techniques with advanced Python environments, opening new avenues for computational science education and research.
Chung, Trinity; Sego, T. J.; Glazier, James A.
(, Journal of advanced technological education)
CompuCell3D (CC3D) is an open-source software framework for building and executing multi-cell biological virtual-tissue models. It represents cells using the Glazier–Graner–Hogeweg model, also known as Cellular Potts model. The primary CC3D application consists of two separate tools, a smart model editor (Twedit++) and a tool for model execution, visualization and steering (Player). The CompuCell3D version 4.x release introduces support for Jupyter Notebooks, an interactive computational environment, which brings the benefits of reproducibility, portability, and self-documentation. Since model specifications in CC3D are written in Python and CC3DML and Jupyter supports Python and other languages, Jupyter can naturally act as an integrated development environment (IDE) for CC3D users as well as a live document with embedded text and simulations. This update follows the trend in software to move away from monolithic freestanding applications to the distribution of methodologies in the form of libraries that can be used in conjunction with other libraries and packages. With these benefits, CC3D deployed inJupyter Notebook is a more natural and efficient platform for scientific publishing and education using CC3D.
Lai, Larry Syu-Heng; Booth, Adam M; Duvall, Alison R; Herzig, Erich
(, Earth Surface Dynamics)
Abstract. pyTopoComplexity is a Python package designed for efficient and customizable quantification of topographic complexity using four advanced methods: two-dimensional continuous wavelet transform analysis, fractal dimension estimation, rugosity index, and terrain position index calculations. This package addresses the lack of open-source software for these advanced terrain analysis techniques essential for modern geomorphology and geohazard research, enhancing data comparison and reproducibility. By assessing topographic complexity across multiple spatial scales, pyTopoComplexity allows users to identify characteristic morphological scales of studied landforms. The software repository also includes a Jupyter Notebook that integrates components from the surface-process modeling platform Landlab (Hobley et al., 2017), facilitating the exploration of how terrestrial processes, such as hillslope diffusion and stream power incision, drive the evolution of topographic complexity over time. When these complexity metrics are calibrated with absolute age dating, they offer a means to estimate in situ hillslope diffusivity and fluvial erodibility, which are critical factors in determining the efficiency of landscape recovery after significant geomorphic disturbances such as landslides. By integrating these features, pyTopoComplexity expands the analytical toolkit for measuring and simulating the time-dependent persistence of geomorphic signatures against environmental and geological forces.
To improve accessibility and community knowledge of applications in the Lidar Radar Open Software Environment (LROSE), a team from the National Science Foundation (NSF) National Center for Atmospheric Research, Colorado State University, and NSF Unidata has developed a lidar and radar meteorology science gateway deployed on the NSF Jetstream2 cloud. Utilizing the “Zero to JupyterHub with Kubernetes” workflow, the science gateway integrates LROSE with other lidar and radar meteorology software packages. This integration allows users to execute applications directly from the JupyterLab terminal, streamlining the creation of datasets for further analysis and visualization within Jupyter notebooks. By combining traditional command-line operations with modern Python-based tools for data analysis and visualization, this gateway provides a robust end-to-end solution that caters to both educational and research needs. The gateway has already facilitated LROSE instructional workshops and classroom exercises. Our work demonstrates the significant potential of merging established scientific computing techniques with advanced Python environments, opening new avenues for computational science education and research. The LROSE team has acquired successive allocations on the NSF Jetstream2 cloud at Indiana University through ACCESS. To develop the LROSE Science Gateway, we employed the “Zero to JupyterHub with Kubernetes” workflow ported to the NSF Jetstream2 cloud, enabling rapid and scalable deployment to accommodate a variable number of users. Authentication is managed through either GitHub OAuth or temporary credentials, depending on the situation. Since LROSE is a collection of C/C++ applications, we configured Docker containers based on the Jupyter Docker Stack to integrate the LROSE software, available via the JupyterLab terminal. These containers also include Conda package manager environments equipped with Python packages like Py-ART, CSU RadarTools, and Metpy for further data analysis. A shared drive accessible to all participants contains instructional datasets for lidar and radar data analysis. Tutorials take the form of Jupyter notebooks for use by individuals, in classroom exercises, or at instructional workshops. Some tutorials are complete with pre-loaded examples to quickly visualize workflows and results. Other tutorials guide students how to run the applications independently. All tutorials are hosted on the LROSE Science Gateway GitHub repository, which is open to contributions from colleagues and community members. Future plans include an "intermediate" level workshop on SAMURAI, one of the multi-Doppler wind applications of the LROSE suite. Additionally, work is currently underway to run GUI applications in the same browser-based JupyterLab environment. GUI applications for radar and lidar data visualization utilize the QT framework and present unique technical challenges. The techniques to accomplish GUI access have immediate applications for other GUI programs, such as NSF Unidata's IDV and their version of the AWIPS CAVE data visualization tools. Lastly, as demand for the resources found on the gateway increases, it becomes increasingly important to efficiently manage the Jetstream2 resources allocated by the ACCESS program. LROSE, NSF Unidata, San Diego Supercomputing Center (SDSC), and Indiana University staff are working together to deploy and evaluate Kubernetes cluster auto-scaling. With auto-scaling, resources will no longer sit idle while awaiting new logins and will instead be provisioned on-demand.
Neuroscientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. This requires expert knowledge of programming and cyberinfrastructure configuration, which is beyond the repertoire of most neuroscience programs. This paper presents early experiences from a one-credit graduate research training course titled ECE 8001 “Software and Cyber Automation in Neuroscience” at the University of Missouri for engendering multi-disciplinary collaborations between computational neuroscience and cyberinfrastructure students and faculty. Specifically, we discuss the course organization and exemplar outcomes involving a next-generation science gateway for training novice users on exemplar neuroscience use cases that involve using tools such as NEURON and MATLAB on local as well as Neuroscience Gateway resources. We also discuss our vision towards a course sequence curriculum for graduate/undergraduate students from biological/psychological sciences and computer science/engineering to jointly build “self- service” training modules using Jupyter Notebook platforms. Thus, our efforts show how we can create scalable and sustainable cyber and software automation for fulfilling a broad set of neuroscience research and education use cases.
Xiao, Xianghui, Xu, Zhengrui, Lin, Feng, and Lee, Wah-Keat. TXM-Sandbox : an open-source software for transmission X-ray microscopy data analysis. Retrieved from https://par.nsf.gov/biblio/10318709. Journal of Synchrotron Radiation 29.1 Web. doi:10.1107/S1600577521011978.
@article{osti_10318709,
place = {Country unknown/Code not available},
title = {TXM-Sandbox : an open-source software for transmission X-ray microscopy data analysis},
url = {https://par.nsf.gov/biblio/10318709},
DOI = {10.1107/S1600577521011978},
abstractNote = {A transmission X-ray microscope (TXM) can investigate morphological and chemical information of a tens to hundred micrometre-thick specimen on a length scale of tens to hundreds of nanometres. It has broad applications in material sciences and battery research. TXM data processing is composed of multiple steps. A workflow software has been developed that integrates all the tools required for general TXM data processing and visualization. The software is written in Python and has a graphic user interface in Jupyter Notebook . Users have access to the intermediate analysis results within Jupyter Notebook and have options to insert extra data processing steps in addition to those that are integrated in the software. The software seamlessly integrates ImageJ as its primary image viewer, providing rich image visualization and processing routines. As a guide for users, several TXM specific data analysis issues and examples are also presented.},
journal = {Journal of Synchrotron Radiation},
volume = {29},
number = {1},
author = {Xiao, Xianghui and Xu, Zhengrui and Lin, Feng and Lee, Wah-Keat},
}
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