Learning Scientific Programming with Python

Focus on software

Data clustering is an important technique for data visualization and statistical data analysis. Using data clustering technique for geo-located data analysis and visualization has been shown to be useful in many practical domains. Although there are numerous studies for data clustering algorithms, the implementations and applications based on these algorithms are rare and inadequate, especially for web-based data visualization services. In this research, a new web-based interactive data clustering prototype is proposed for geo-located data visualization. The necessary web-based infrastructure for geo-located data analysis and visualization is designed and implemented at first. And then the new interactive data clustering prototype using multi-dimensional K-Means algorithm is implemented on the existing infrastructure. In order to assess whether the new prototype can contribute meaningfully to architectural design and urban studies, qualified users with urban study or architectural backgrounds are invited to use the prototype and then make their comments and evaluations. To make it clear how this new prototype might apply in real world settings, an application scenario in which the user might interact with the prototype is provided and analyzed.

In a variety of disciplines, from biology and physics to computer science and the social sciences, computational topology has become a potent tool for data analysis and visualisation. This study explores computational topology's uses in data analysis and visualisation, showing its potential for revealing hidden structures and patterns in large, complicated datasets.The paper starts out by giving a general introduction of computational topology and outlining its core ideas and methods. The use of it for extracting important information from big, high-dimensional datasets is then explored in relation to data analysis. Computational topology allows for the discovery of topological properties such as holes, voids, and connection patterns by modelling data as topological structures, such as simplicial complexes or persistent homology diagrams.The study also explores the function of computational topology in data visualisation, highlighting its capacity to offer clear and insightful visual representations of challenging datasets. Computational topology enables the construction of simpler and aesthetically pleasing representations while preserving the fundamental topological properties of the data through methods like topological simplification and dimensionality reduction.The usefulness of computational topology in several application domains, such as genomics, image analysis, and network analysis, is demonstrated through a number of case studies. These instances show how computational topology can improve activities like data exploration, clustering, classification, and anomaly detection, resulting in fresh perceptions and learnings.This study emphasises the enormous potential of computational topology for applications in data processing and visualisation. Computational topology provides a new perspective that complements current approaches by utilising the inherent geometric and topological properties of data. This allows researchers and practitioners to better understand complex datasets and make decisions based on the knowledge gleaned from them.

Data visualization and data mining are transdisciplinary tools for predictive and descriptive analytics. This presentation shows the abundance of data visualization tools currently available that are applicable for multi-disciplinary data. Some examples of visualization are presented as applied for multi-disciplines. Results of applying the most commonly used data visualization tools of Tableau [1] and Power BI [2] are presented as preliminary outputs of a funded Seed Money Grant that applies data for transmissible diseases for humans and also plant pathology. Tableau Software, LLC is an American interactive data visualization software company focused on business intelligence and can be used across all disciplines. Tableau products query relational databases, online analytical processing cubes, cloud databases, and spreadsheets to generate graph-type data visualizations.[1] Power BI (Business Intelligence) is a suite of business analytics tools developed by Microsoft that is widely used across various industries for business reporting, data analysis, and decision-making. [2] Future directions of this research are discussed that include using other data mining and visualization methods such as image processing techniques for applications to data for plant pathology and human diseases.

Aim. To characterize the clinical manifestations and myocardial morphological changes in patients with cardiovascular complications of post-Covid syndrome.Material and methods. A total of 38 patients (Group I) with cardiac symptoms in the post-COVID-19 period underwent endomyocardial biopsy (EMB). Patients from group I were divided into 2 subgroups depending on the presence of morphologically verified myocarditis. For morphological comparison, group II was formed (41 patients without COVID-19 and with EMB performed before 2019). Group II was also divided into two subgroups with and without morphologically verified myocarditis. In addition to the analysis of clinical and paraclinical data, a histological and immunohistochemical study of myocardial tissue was carried out with anti-CD3, CD68, CD16, HLA-DR, MHC1, C1q, enterovirus VP1, SARS-CoV-2 spike protein, ACE2, Ang1, von Willebrand factor, VEGF antibodies, as well as a PCR of myocardial tissue after reverse transcription. Additionally, an immunofluorescent study of myocardial tissue was performed using antibody cocktails to SARS-CoV-2/ CD16 spike protein, SARS-CoV-2/CD68 spike protein, CD80/CD163. Statistical processing was performed using the NumPy, SciPy, Pandas and Matplotlib libraries in the Python programming language.Results. Among patients in group I, 65% had morphologically verified lymphocytic myocarditis (subgroup 1, n=29). Other 35% of patients had no signs of myocardial inflammation (subgroup 2, n=9). The comparison group (group II) included 33 patients with myocarditis (80%) and 8 patients (20%) without it. There was a pronounced expression of CD68+ macrophages in the myocardial tissue in patients with postCOVID-19 syndrome in comparison with group II, both with morphological criteria for myocarditis (p=0,014) and without it (p=0,007), mainly due to M2 macrophages. More pronounced expression of CD68+ macrophages was observed when EMB was performed at earlier stages. If SARS-CoV-2 spike protein was detected in group I on the endothelium and inflammatory infiltrate by the immunohistochemistry, PCR of myocardial tissue for SARS-CoV-2 (n=5) was performed, which did not reveal SARS-CoV-2 particles in any of the analyzed samples.Conclusion. Cardiac symptoms of post-COVID-19 syndrome are caused by myocardial remodeling, including in patients without morphologically verified myocarditis. The direct involvement of SARS-CoV-2 in the development of myocarditis in the post-acute period of COVID-19 has not been proven.

The exponentially increasing volumes of health data of different types is a challenge for reaching robust conclusions. The Health Vision platform overcomes this issue by providing advanced and multi-scaling data analysis and visualisation. The platform functions through user defined workflows, each organised into four distinct layers: 1. Raw Data, 2. Data Analytics, 3. Decision Support and 4. Data visualisation. In the Raw Data Layer, data from medical records, healthcare platforms, smart homes, monitoring sensors, and medical devices, clinical features from Electronic Health Records (EHRs), etc. can be considered, while the Data Analytics Layer provides data analysis services for data mining. The Decision Support Layer facilitates the selection of appropriate visualisation methods taking into consideration the specifications of the raw data and the output of the data analysis. Finally, the Data visualisation Layer provides intelligent visualisation methodology in order for the user to interact with the data and gain meaningful results. Visualisation is supported by a reactive workflow mechanism, allowing the composition of analysis and visualisation services in a custom application-specific data flow. We demonstrate and evaluate this novel approach in several use cases, where different types of data are involved, including: Clustering of Sjogren's syndrome patients, visualisation of Parkinson's disease patients, mutation association analysis in Chronic Lymphocytic Leukemia patients, behavioral analysis of asthma patients and clinical analysis based on clinical similarities of older adults.

Python Powered Intelligence And ML is designed to be your essential companion in your journey through the world of Artificial Intelligence and Python programming. We understand the importance of building a solid foundation in AI concepts, as well as mastering the tools and techniques needed to implement AI solutions effectively. What You’ll Find Inside: Foundation of Artificial Intelligence: In Chapter 1, we lay the groundwork for your AI education, providing a strong understanding of the fundamentals. Knowledge Presentation: Chapter 2 delves into how knowledge is represented in AI systems, a crucial element for creating intelligent machines. Informed / Heuristic Search Strategies: Chapter 3 explores strategies for problem-solving and decision-making, crucial in the AI domain. Natural Language Processing: In Chapter 4, we dive into the world of language understanding and processing, a key area of AI. Soft Computing: Chapter 5 introduces the concept of soft computing, which enables AI systems to work with uncertainty and imprecision. Neural Networks: Chapter 6 covers neural networks, a fundamental technology in modern AI, inspired by the human brain. Fuzzy Systems: Chapter 7 is all about fuzzy logic, which allows AI systems to deal with vagueness and uncertainty. History of Genetic Algorithms: Chapter 8 takes you through the fascinating history and principles of genetic algorithms. Regression: In Chapter 9, we explain regression techniques for predictive modeling, a crucial tool in AI. Python Programming: The second part of Python Powered Intelligence And ML focuses on Python, one of the most versatile and popular programming languages. Numpy: Chapter 11 introduces the powerful library for numerical computing in Python. Pandas: In Chapter 12, we explore Pandas, a tool for data manipulation and analysis. Matplotlib: Chapter 13 introduces you to data visualization using Matplotlib. Regression: We revisit regression in Chapter 14, providing more insights and applications. Our aim is to empower you with the knowledge and skills to excel in Artificial Intelligence and Python programming. Whether you are a beginner or an experienced programmer, this book is your go-to resource for AI and Python. We hope you enjoy this journey with us, and may it inspire you to explore the exciting world of Artificial Intelligence and Python programming. Happy learning!

In the rapidly developing modern world, population growth and urbanization have driven the prioritization of industrial settlements over the natural environment in land use. Besides losing ecological services, many inherent problems emerge in cities due to inefficient planning. This study presents an application of a grey linear program to maximize the socio-economic and environmental benefits of the land use structure in Los Angeles County. A technique in operations research, linear programming fits the purpose of this study since it optimizes a function concerning constraints. For each of the fourteen land types in this research, a monetary value is assigned to its economic output and environmental services, and the scalar sum is the goal to be maximized. The constraints consist of inequalities built from historical trends, developmental needs, and targets. On the other hand, based on past data, the grey model employs differential equations to predict the value of several parameters for the linear program, such as GDP output. The results are calculated by the Scipy library in a Python program and improve the quantified benefit by 8.26%, with an increase in residential, commercial, and industrial land and a slower decrease in natural landscape. Environmental initiatives and improvements in LA Countys livability are necessary to fulfill long-term values.

Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), bridges to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/.

Purpose: The research aims to visualize and analyze co-word network, and thematic clusters of the intellectual structure in the field of digital content management during 2010-2020.Method: The study is applied research with a descriptive approach which is conducted by techniques of co-word, and social network analysis. Data analysis and visualization of the co-word network were represented by SPSS, UCINet, and Python programming language.Findings: 8 main clusters are identified. The cluster multimedia content management & retrieval is the most mature and central thematic cluster. The USA and various sub-categories of Computer Science are located in the top ranks of WOS in the field. Most productions were published in 2020. Generally, the Clusters were labeled in two contexts of health and LAM (Libraries, Archives, Museums, and cultural heritage).Conclusion: Content-based management and retrieval are focused on artificial intelligence, decision-supported, knowledge-based and ontological techniques which are conducted as novel approaches and underlying trends in the field.

SummaryPathway analysis is widely used in genomics and omics research, but the data visualization has been highly limited in function, pathway coverage and data format. Here, we develop SBGNview a comprehensive R package to address these needs. By adopting the standard SBGN format, SBGNview greatly extend the coverage of pathway-based analysis and data visualization to essentially all major pathway databases beyond KEGG, including 5200 reference pathways and over 3000 species. In addition, SBGNview substantially extends or exceeds current tools (esp. Pathview) in both design and function, including standard input format (SBGN), high-quality output graphics (SVG format) convenient for both interpretation and further update, and flexible and open-end workflow for iterative editing and interactive visualization (Highlighter module). In addition to pathway analysis and data visualization, SBGNview provides essential infrastructure for SBGN data manipulation and processing.Availability and implementationThe data underlying this article are available as part of the SBGNview package is available on both GitHub and Bioconductor: https://github.com/datapplab/SBGNview, https://bioconductor.org/packages/SBGNview.Supplementary informationSupplementary data are available at Bioinformatics online.

Visualization is a part of data science, and essential to enable sophisticated analysis of data. The visualization ensures the human participation in most decisions when analyzing data. In this paper, we review methods and software for visualization of multidimensional data. The emphasis is put on the web-based DAMIS solution for data analysis, allowing researchers to carry out the primary data analysis and to investigate the projection of multidimensional data on a plane, the similarities between the data items, the influence of individual features, and their relationships by visual analysis techniques, using the high-performance computing resources. DAMIS is applied to the visual efficiency analysis of regional economic development to evaluate how regional resources are reflected in the economic results. The projection methods (principal component analysis, multidimensional scaling) and artificial neural networks (self-organizing map, SAMANN) are the core strategies for the analysis.

Design and development of data visualization systems in a workstation environment

The article describes the modern escalation of “big” and open data; situations where sociologist needs new competences such as coding and programming are analyzed. The author puts attention to two key research strategies when dealing with big data: self-localization and dipping into data. The first strategy refers to the researcher’s focus on traditional sociological analysis restricted by small sample size; the second one implies maximal use of the growing open source data. The challenges a researcher has to face when dealing with big data are as follows: (1) data extraction; (2) data processing and improvements; (3) data visualization. This is why a researcher should master coding and programming. Sociologists with humanitarian education should choose R statistical environment and Python programming language becasue they have simple syntax and allow solving complicated data processing, analysis and visualization tasks. The article provides examples of scripts as a demonstration of the possibilities of the R and Python programming languages.

Today's fluid simulations deal with complex geometries and numerical data on an extreme scale. As computation approaches the exascale, it will no longer be possible to write and store the full-sized data set. In situ data analysis and scientific visualisation provide feasible solutions to the analysis of complex large scaled CFD simulations. To bring pre- and postprocessing to the exascale we must consider modifications to data structure and memory layout, and address latency and error resiliency. In this respect, a particular challenge is the exascale data processing for the sparse geometry lattice-Boltzmann code HemeLB, intended for hemodynamic simulations. In this paper, we assess the needs and challenges of HemeLB users and sketch a co-design infrastructure and system architecture for pre- and post-processing the simulation data. To enable in situ data visualisation and analysis during a running simulation, post-processing needs to work on a reduced subset of the original data. Particular choices of data structure and visualisation techniques need to be co-designed with the application scientists in order to achieve efficient and interactive data processing and analysis. In this work, we focus on the hierarchical data structure and suitable visualisation techniques which provide possible solutions to interactive in situ data processing at exascale. Architectural challenges and road-maps will be presented as the major focus of this paper. We sketch a software architecture which integrates pre- and post-processing techniques that can provide in situ analysis and ultimately computational steering to HemeLB.