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R and Python for Oceanographers

Author : Hakan Alyuruk

Publisher : Elsevier

Page : 188 pages

File Size : 34,80 MB

Release : 2019-06-09

Category : Science

ISBN : 0128134925

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Book Description

R and Python for Oceanographers: A Practical Guide with Applications describes the uses of scientific Python packages and R in oceanographic data analysis, including both script codes and graphic outputs. Each chapter begins with theoretical background that is followed by step-by-step examples of software applications, including scripts, graphics, tables and practical exercises for better understanding of the subject. Examples include frequently used data analysis approaches in physical and chemical oceanography, but also contain topics on data import/export and GIS mapping. The examples seen in book provide uses of the latest versions of Python and R libraries. - Presents much needed oceanographic data analysis approaches to chemical and physical oceanography - Includes examples with software applications (based on Python and R), including free software for the analysis of oceanographic data - Provides guidance on how to get started, along with guidance on example code and output



Oceanographic Analysis with R

Author : Dan E. Kelley

Publisher : Springer

Page : 303 pages

File Size : 13,28 MB

Release : 2018-10-17

Category : Medical

ISBN : 1493988441

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Book Description

This book presents the R software environment as a key tool for oceanographic computations and provides a rationale for using R over the more widely-used tools of the field such as MATLAB. Kelley provides a general introduction to R before introducing the ‘oce’ package. This package greatly simplifies oceanographic analysis by handling the details of discipline-specific file formats, calculations, and plots. Designed for real-world application and developed with open-source protocols, oce supports a broad range of practical work. Generic functions take care of general operations such as subsetting and plotting data, while specialized functions address more specific tasks such as tidal decomposition, hydrographic analysis, and ADCP coordinate transformation. In addition, the package makes it easy to document work, because its functions automatically update processing logs stored within its data objects. Kelley teaches key R functions using classic examples from the history of oceanography, specifically the work of Alfred Redfield, Gordon Riley, J. Tuzo Wilson, and Walter Munk. Acknowledging the pervasive popularity of MATLAB, the book provides advice to users who would like to switch to R. Including a suite of real-life applications and over 100 exercises and solutions, the treatment is ideal for oceanographers, technicians, and students who want to add R to their list of tools for oceanographic analysis.



Chemical Oceanography

Author : Steven R. Emerson

Publisher : Cambridge University Press

Page : 403 pages

File Size : 17,43 MB

Release : 2022-04-07

Category : Science

ISBN : 1107179890

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Book Description

A broad, clear introductory textbook on chemical oceanography for undergraduate and graduate students and a reference text for researchers.



Chemical Oceanography

Author : Frank J. Millero

Publisher : CRC Press

Page : 594 pages

File Size : 50,60 MB

Release : 2016-04-19

Category : Science

ISBN : 1482211823

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Book Description

Over the past ten years, a number of new large-scale oceanographic programs have been initiated. These include the Climate Variability Program (CLIVAR) and the recent initiation of the Geochemical Trace Metal Program (GEOTRACES). These studies and future projects will produce a wealth of information on the biogeochemistry of the world's oceans. Aut



Python for R Users

Author : Ajay Ohri

Publisher : John Wiley & Sons

Page : 369 pages

File Size : 13,70 MB

Release : 2017-11-13

Category : Computers

ISBN : 1119126762

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Book Description

The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.



Oceanographic and Marine Cross-Domain Data Management for Sustainable Development

Author : Diviacco, Paolo

Publisher : IGI Global

Page : 450 pages

File Size : 20,43 MB

Release : 2016-09-23

Category : Science

ISBN : 1522507019

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Book Description

As human activity makes a greater impact on the environment, sustainability becomes an increasingly imperative goal. With the assistance of current technological innovations, environmental systems can be better preserved. Oceanographic and Marine Cross-Domain Data Management for Sustainable Development is a pivotal resource for the latest research on the collection of environmental data for sustainability initiatives and the associate challenges with this data acquisition. Highlighting various technological, scientific, semantic, and semiotic perspectives, this book is ideally designed for researchers, technology developers, practitioners, students, and professionals in the field of environmental science and technology.



Evaluating Climate Change Impacts

Author : Vyacheslav Lyubchich

Publisher : CRC Press

Page : 395 pages

File Size : 19,40 MB

Release : 2020-10-06

Category : Mathematics

ISBN : 1351190822

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Book Description

Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem, social, and infrastructure resilience, given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies. This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts. The chapters unveil topics of atmospheric circulation, climate modeling, and long-term prediction; approach the problems of increasing frequency of extreme events, sea level rise, and forest fires, as well as economic losses, analysis of climate impacts for insurance, agriculture, fisheries, and electric and transport infrastructures. The reader will be exposed to the current research using a variety of methods from physical modeling, statistics, and machine learning, including the global circulation models (GCM) and ocean models, statistical generalized additive models (GAM) and generalized linear models (GLM), state space and graphical models, causality networks, Bayesian ensembles, a variety of index methods and statistical tests, and machine learning methods. The reader will learn about data from various sources, including GCM and ocean model outputs, satellite observations, and data collected by different agencies and research units. Many of the chapters provide references to open source software R and Python code that are available for implementing the methods.



Applied Spatial Data Analysis with R

Author : Roger S. Bivand

Publisher : Springer Science & Business Media

Page : 414 pages

File Size : 22,26 MB

Release : 2013-06-21

Category : Medical

ISBN : 1461476186

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Book Description

Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.



Ocean Solutions, Earth Solutions

Author : Dawn J. Wright

Publisher : ESRI Press

Page : 507 pages

File Size : 30,55 MB

Release : 2016

Category : NATURE

ISBN : 9781589484603

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Book Description

This book showcases the latest and best oceanography research using spatial analyses and geographic information systems. This is the leading, most up-to-date book on the subject.



Matplotlib for Python Developers

Author : Sandro Tosi

Publisher : Packt Publishing Ltd

Page : 438 pages

File Size : 34,86 MB

Release : 2009-11-09

Category : Computers

ISBN : 1847197914

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Book Description

This is a practical, hands-on book, with a lot of code and images. It presents the real code that generates every image and describes almost every single line of it, so that you know exactly what's going on. Introductory, descriptive, and theoretical parts are mixed with examples, so that reading and understanding them is easy. All of the examples build gradually with code snippets, their explanations, and plot images where necessary with the complete code and output presented at the end. This book is essentially for Python developers who have a good knowledge of Python; no knowledge of Matplotlib is required. You will be creating 2D plots using Matplotlib in no time at all.