Data Visualization with Leather

Grating-based interferometry/tomography is being rapidly developed for non-destructive evaluation of additive manufacturing test articles. An application requiring an efficient workflow is extremely necessary for stress and fatigue testing samples. At present, scientific workflows play an important role for computational experiments in additive manufacturing 3D printing and interferometry/tomography imaging analysis. A clear workflow template allows scientists to process experiments easier and faster. Work flow library grows, but to find an appropriate workflow for their task is challenging. In our research, there are mainly three portions in the workflow, interferometry analysis, image reconstruction and 3D visualization. Currently, the hierarchy of workflows in interferom etry/tomography projects is Mathematica, TomoPy/ASTRA/Jupyter notebook, VisTrails and Dragonfly. In general, two methods of interferometry analysis have been used in the first portion of workflow, single-shot interferometry and stepped-grating interferometry. As for the second portion, with a Jupyter notebook, the reconstruction method ’Gridrec’ in TomoPy and ’SIRT’ (Simultaneous Iterative Reconstruction Technique) in ASTRA gener ated a powerful reconstruction volume for absorption projections and dark-field projections separately. For the last portion, Dragonfly developed by ORS (Object Research System) company is a 3D visualization software with powerful scripting capabilities implemented in Python macros. Meanwhile, the VisTrails workflow incorporated both interferometry anal ysis and image reconstruction portions into VisTrails modules. Workflows in VisTrails hide much of the complexity of Mathematica or Python programming from users. Instead, with a simple GUI, it is possible for users to make their interferometry/tomography workflows through VisTrails modules. Especially, for DPC (differential phase contrast) images in grating-based interferome try/tomography, we addressed the phase unwrapping issue with the method of 2D integra tion through generating phase images. With the algorithm, we have demonstrated the 2D integrated phase images denote a clearer contrast than DPC images.

Many people today are unsure whether they have COVID-19. The frequent fever, dry cough, and sore throat are all signs and symptoms of COVID-19. If a person has signs or symptoms of coronavirus disease 2019 (COVID-19), he/she should see the doctor or go to a clinic as soon as possible. As a result, it's vital to learn and comprehend the fundamental differences. COVID-19 can cause a wide range of symptoms. The experiments were carried out using two Machine Learning Classification Algorithms, namely Decision Tree (DT) and Logistic Regression (LR). Both algorithms were written and analyzed using the Python program in Jupyter Notebook 6.4.5. From the results obtained in the experiments of covid symptoms dataset, on average, the DT model has obtained the best cross-validation average and the testing performance average compared to the LR machine learning models. For cross-validation results, the DT model has achieved an accuracy of 98.0%. For performance testing, the DT model has achieved an accuracy of 98.0%. The LR has obtained the second-best result on the average of cross-validation performance and the testing results. For cross-validation results, the LR model has achieved an accuracy of 96.0%. For performance testing, the LR model has achieved an accuracy of 97.0%. Consequently, the DT for the COVID-19 symptoms dataset is outperforming the LR for cross-validation and testing results.

Superhost Airbnb is an experienced host and provides excellent service to its customers. Superhost has features that can increase the number of bookings and revenue. SuperHost profile is one thing that can be used as a reference for other hosts to improve the quality of service. In this study, an analysis of exploration and visualization of Airbnb hosts' data in the city of Amsterdam and the city of Madrid to find out the profile of superhost from the aspect of price and consumer reviews. The city of Amsterdam and the city of Madrid are chosen because the two cities are the leading destinations for tourists in Europe. The study was conducted using a machine learning approach that has four work steps, namely understanding business processes, data retrieval, data processing, and exploratory analysis and data visualization. The tools used in this study are Jupyter Notebook with the Python programming language. The results obtained from this study are superhost in Madrid, mostly offering rental prices in the price range of $60 - $80. They get the highest reviews from customers based on cleanliness, communication, and check-in. As for Amsterdam, the superhost offers the most rental prices in the price range above $ 140. Superhosts in this price range gets the most reviews from customers in all review groups.

Programmatic processing, analysis, and visualization of scientific research data necessitate computational skills that scientists do not consistently acquire during their education. Python is a programming language currently in the ascendant among the scientific community. In early 2022, 25 marine scientists and marine biology graduate students were introduced to the fundamentals of Python programming in a 10.5-hour workshop series offered over seven weeks. A functional, web-based Python environment using Jupyter Notebooks, JupyterLab, Binder, and GitHub was paired with scaffolding and active learning pedagogy, to ensure an engaging learning experience. We describe the course design, adaptations, and outcomes, and offer recommendations for developing a similar introductory programming workshop.

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d23104e81"> <b>Objectives:</b> The development of a high-quality, analysis-ready dataset is crucial for any pharmacometric analysis. While SAS and R have been traditionally used for Population Pharmacokinetic (popPK), Pharmacokinetic and Pharmacodynamic (PKPD), and Exposure Response (ER) dataset creation, this abstract explores Python as an alternative programming language for generating CDISC-compliant, analysis-ready pharmacometric datasets. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d23104e86"> <b>Methods:</b> Using Jupyter Notebook with the Python 3 kernel and widely used packages such as pandas, NumPy, and the os and datetime modules, we developed Python programs following the CDISC Implementation Guide for creating popPK, PKPD, and ER datasets from ADaM source data in sas7bdat format. We highlight visual and tabular quality control checks integrated within Jupyter Notebook. Programming steps including datetime processing of SAS-formatted source data, flagging of records with issues, derivations of relative time variables, imputation of missing dosing times, and derivation of ER endpoints like progression-free survival, immune-mediated adverse events, and baseline covariates will be discussed. Python functions used to convert lab units and adding variable labels to the output dataset will be presented. Python code to handle the stacked structure of events in popPK and PKPD datasets will also be shown. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d23104e91"> <b>Results:</b> Python successfully produced CDISC-compliant, analysis-ready popPK, PKPD, and ER datasets. The use of Jupyter Notebooks facilitated both the quality control of source and output datasets and seamless, programmer-friendly documentation of the dataset creation process. Python effectively managed complex algorithms related to source data issues and the intricacies of programming pharmacometric datasets. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d23104e96"> <b>Conclusions:</b> Python is a suitable and versatile environment for programming CDISC-compliant, popPK, PKPD, and ER datasets and merits further exploration. More research into ensuring reproducibility between Python versions and packages is required. Python’s flexibility, open-source nature, and widespread use make it a promising tool for pharmacometric programming.

Writing source code for programs with lightweight text editors or fully featured integrated development environments is considered the main method of programming. Notebooks, however, are an extremely practical tool. In contrast to IDEs, projects are set up more easily and they allow for running programs in a read-eval-print loop (REPL) environment. The Jupyter Notebooks Quick Start Guide [1] describes notebook documents as "… both human-readable documents containing the analysis description and the results (figures, tables, etc..) as well as executable documents which can be run to perform data analysis." Basically, markdown text can be mixed with program source code in a sequence of sections, each dedicated to either programming or description and documentation. Source code sections can be executed and the output is appended to the section, even formatted in the form of graphs, diagrams, or tables. REPL and notebook based environments have proven to be useful in many scenarios including when exploring new libraries and frameworks, to prototype code, or as an educational tool to create interactive lecture material. A prominent example is Jupyter, which is the highly successful project behind Jupyter Notebooks and the recent JupyterLab, i.e. web-based systems to run and share notebooks that contain code, equations, data visualizations, and data exploration and narrative text.

Dimensionality reduction techniques are essential in analyzing large 'omics' datasets in biochemistry and molecular biology. Principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are commonly used for data visualization. However, these methods can be challenging for students without a strong mathematical background. In this study, intuitive examples were created using COVID-19 data to help students understand the core concepts behind these techniques. In a 4-h practical session, we used these examples to demonstrate dimensionality reduction techniques to 15 postgraduate students from biomedical backgrounds. Using Python and Jupyter notebooks, our goal was to demystify these methods, typically treated as "black boxes", and empower students to generate and interpret their own results. To assess the impact of our approach, we conducted an anonymous survey. The majority of the students agreed that using computers enriched their learning experience (67%) and that Jupyter notebooks were a valuable part of the class (66%). Additionally, 60% of the students reported increased interest in Python, and 40% gained both interest and a better understanding of dimensionality reduction methods. Despite the short duration of the course, 40% of the students reported acquiring research skills necessary in the field. While further analysis of the learning impacts of this approach is needed, we believe that sharing the examples we generated can provide valuable resources for others to use in interactive teaching environments. These examples highlight advantages and limitations of the major dimensionality reduction methods used in modern bioinformatics analysis in an easy-to-understand way.

IPMVP is an acronym for International Performance Measurement and Verification Protocol, used for calculating energy savings by taking some external factors into account to give an equal base for a linear comparison between two periods. However, determining the significant independent variable for routine adjustment causes inconvenient repeated action of regression analysis modeling, which leads to the exhaustive calculation and human error risk. These exasperating actions led to the creation of this project, which aims to provide a user-friendly, insightful, and accurate energy-saving calculation program tool. Thus, the main objective of this project is to develop an energy-saving computational tool for option C IPMVP using web-based data analytic tools to improve efficiency and accuracy in each project. This paper used a web-based mathematical computation, Jupyter Notebook, to determine the significant independent variable for routine adjustment of the adjusted baseline. The program tool was validated using V-model software verification and validation methodology based on IEEE 1012 standards. The study can be concluded that the exhaustive calculation to determine the energy saving based on IPMVP standards can be significantly reduced with precision values at 50%, 80%, 90%, and 95% confidence levels using the proposed program which is equipped with insightful graphs and a user-friendly interface.

BioUML (https://www.biouml.org)—is a web-based integrated platform for systems biology and data analysis. It supports visual modelling and construction of hierarchical biological models that allow us to construct the most complex modular models of blood pressure regulation, skeletal muscle metabolism, COVID-19 epidemiology. BioUML has been integrated with git repositories where users can store their models and other data. We have also expanded the capabilities of BioUML for data analysis and visualization of biomedical data: (i) any programs and Jupyter kernels can be plugged into the BioUML platform using Docker technology; (ii) BioUML is integrated with the Galaxy and Galaxy Tool Shed; (iii) BioUML provides two-way integration with R and Python (Jupyter notebooks): scripts can be executed on the BioUML web pages, and BioUML functions can be called from scripts; (iv) using plug-in architecture, specialized viewers and editors can be added. For example, powerful genome browsers as well as viewers for molecular 3D structure are integrated in this way; (v) BioUML supports data analyses using workflows (own format, Galaxy, CWL, BPMN, nextFlow). Using these capabilities, we have initiated a new branch of the BioUML development—u-science—a universal scientific platform that can be configured for specific research requirements.

Academic departments, research clusters and evaluators analyze author and citation data to measure research impact and to support strategic planning. We created Scholar Metrics Scraper (SMS) to automate the retrieval of bibliometric data for a group of researchers. The project contains Jupyter notebooks that take a list of researchers as an input and exports a CSV file of citation metrics from Google Scholar (GS) to visualize the group's impact and collaboration. A series of graph outputs are also available. SMS is an open solution for automating the retrieval and visualization of citation data.

Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-point precision and algorithm stability, and extensive online resources support further study. This textbook represents a targeted package for students requiring a solid foundation in Python programming.

Perform big data analysis and visualisation on your own computer? Yes, you can! Commodity computers are now very powerful in comparison to only a few years ago. On top of that, the performance of today's software and data development techniques facilitates complex computation with fewer resources. Cloud computing is not always the solution, and reliability or even privacy is regularly a concern. While the Infrastructure as a Service (IaaS) and Software as a Service (SaaS) philosophies are a key part of current scientific endeavours, there is a misleading feeling that we need to have remote computers to do any kind of data analysis. One of the aims of the ATLAS Open Data project is to provide resources — data, software and documents — that can be stored and executed in computers with minimal or non-internet access, and in as many different operating systems as possible. This approach is viewed as complementary to the IaaS/SaaS approach, where local university, students and trainers' resources can be used in an effective and reproducible way — making the High Energy Physics and Computer Sciences fields accessible to more people. We present the latest developments in the production and use of local Virtual Machines and Docker Containers for the development of physics data analysis. We also discuss example software and Jupyter notebooks, which are in constant development for use in classrooms, and students and teachers computers around the world. Also, we showcase how those tools are developed and in use by international efforts (IPPOG, ICTP-PWF and CEVALE2VE.org) in Latin America and Europe to bring knowledge in countries like Mexico, Venezuela, Colombia, Peru, Ecuador, Argentina and Uruguay with support of the ICTP, CERN and many universities in the region.

Debugging is a critical but challenging task for programmers. This paper proposes ChatDBG, an AI-powered debugging assistant. ChatDBG integrates large language models (LLMs) to significantly enhance the capabilities and user-friendliness of conventional debuggers. ChatDBG lets programmers engage in a collaborative dialogue with the debugger, allowing them to pose complex questions about program state, perform root cause analysis for crashes or assertion failures, and explore open-ended queries like "why is x null?". To handle these queries, ChatDBG grants the LLM autonomy to "take the wheel": it can act as an independent agent capable of querying and controlling the debugger to navigate through stacks and inspect program state. It then reports its findings and yields back control to the programmer. By leveraging the real-world knowledge embedded in LLMs, ChatDBG can diagnose issues identifiable only through the use of domain-specific reasoning. Our ChatDBG prototype integrates with standard debuggers including LLDB and GDB for native code and Pdb for Python. Our evaluation across a diverse set of code, including C/C++ code with known bugs and a suite of Python code including standalone scripts and Jupyter notebooks, demonstrates that ChatDBG can successfully analyze root causes, explain bugs, and generate accurate fixes for a wide range of real-world errors. For the Python programs, a single query led to an actionable bug fix 67% of the time; one additional follow-up query increased the success rate to 85%. ChatDBG has seen rapid uptake; it has already been downloaded more than 75,000 times.

This chapter introduces essential systems and tools for leveraging various natural language processing (NLP) tasks and large language models (LLMs) in qualitative analysis, emphasizing accessibility for noncoders. It begins with an overview of the Python programming language and introduces tools such as the Pandas library, integrated development environments (IDEs), Jupyter notebooks, and application programming interfaces (APIs) for accessing LLMs. A second section provides a hands-on guide for setting up a computer environment, with step-by-step code examples designed to familiarize readers with NLP libraries and LLMs. It emphasizes the acquisition of programming skills to better adapt and utilize LLM capabilities for qualitative data analysis while providing multiple resources for further learning.

The Penguin program is an open-source Python program that enables molecular property and spectroscopy calculations using coupled cluster theory and perturbation theory approaches, such as cluster perturbation theory and Mo̷ller-Plesset perturbation theory. This is the first time that cluster perturbation theory approaches have been made available in open source software. The program is written in a modular and object-oriented fashion and features a matrix element library for evaluating commonly encountered terms in coupled cluster theory. The Penguin program supports interactive usage through Jupyter notebooks, making it ideal for educational purposes as well as rapid prototyping of novel coupled cluster and perturbation theory methods. In the current version, the program also features an OpenMP/MPI parallel version of the CC2 model highlighting the high performance computing potential that can be realized using this strategy of software design.