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Scientific Programmer's Toolkit

Author: M.H Beilby

Publisher: CRC Press

Published: 2022-02-15

Total Pages: 447

ISBN-13: 1000111377

The Scientific Programmer's Toolkit: Turbo Pascal Edition presents a complete software environment for anyone writing programs in mathematical, engineering, or science areas. This toolkit package is designed for use with Turbo Pascal, the de facto standard Pascal system for PC and compatible machines. The book and its software provides an integrated software library of programming tools. The programs and routines fall into three categories: graphical, mathematical, and utilities. Routines are further subdivided into three levels that reflect the experience of the user. For graphics and text handling routines there is also a Level 0, which provides an interface to the machine operating system. By using hierarchically structured routines, the clearly written text, and a wide range of example programs, software users can construct a user-friendly interface with minimal effort. The levels structure makes it easy for newcomers to use the Toolkit, and with growing experience, users can achieve more elaborate effects. The Scientific Programmer's Toolkit will be useful to consultants, researchers, and students in any quantitative profession or science, in private or public sector research establishments, or in secondary and higher education.

Scientific programmer's toolkit [Source code]

Author: Michael Harry Beilby

Publisher:

Published: 1991

Total Pages: 438

ISBN-13: 9780750301282

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Introduction to Scientific Programming with Python

Author: Joakim Sundnes

Publisher:

Published: 2020

Total Pages: 157

ISBN-13: 3030503569

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This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

C++ Toolkit for Engineers and Scientists

Author: James T. Smith

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 399

ISBN-13: 1461214742

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This concise guide covers the fundamental aspects of the numerical analysis, basing upon it the construction of its routines for solving nonlinear equations, linear and nonlinear systems of equations, and eigenvalue problems. Focusing on software development, this book emphasizes software tools, OOP techniques for handling vectors, polynomials, and matrices. Using actual examples to demonstrate reusable tools, the book enables readers to solve broad classes of software development and programming challenges. It adopts a balanced approach between OOP techniques and quick and dirty number crunching, and emphasizes the use of OOP features in implementing vector, polynomial and matrix algebra. As a practical reference, it will help developers and consultants setting up applications programs for electrical, electronic engineering and physical sciences who need to develop clean, efficient C++ programs in minimal time.

A Primer on Scientific Programming with Python

Author: Hans Petter Langtangen

Publisher: Springer

Published: 2016-07-28

Total Pages: 942

ISBN-13: 3662498871

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The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science. From the reviews: Langtangen ... does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. ... Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010 Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.” John D. Cook, The Mathematical Association of America, September 2011 This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science. Alex Small, IEEE, CiSE Vol. 14 (2), March /April 2012 “This fourth edition is a wonderful, inclusive textbook that covers pretty much everything one needs to know to go from zero to fairly sophisticated scientific programming in Python...” Joan Horvath, Computing Reviews, March 2015

Python Data Science Handbook

Author: Jake VanderPlas

Publisher: "O'Reilly Media, Inc."

Published: 2016-11-21

Total Pages: 743

ISBN-13: 1491912138

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For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Computational Science — ICCS 2003

Author: Peter M.A. Sloot

Publisher: Springer

Published: 2003-08-03

Total Pages: 1188

ISBN-13: 3540448640

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The four-volume set LNCS 2657, LNCS 2658, LNCS 2659, and LNCS 2660 constitutes the refereed proceedings of the Third International Conference on Computational Science, ICCS 2003, held concurrently in Melbourne, Australia and in St. Petersburg, Russia in June 2003. The four volumes present more than 460 reviewed contributed and invited papers and span the whole range of computational science, from foundational issues in computer science and algorithmic mathematics to advanced applications in virtually all application fields making use of computational techniques. These proceedings give a unique account of recent results in the field.

Scientific Programming

Author: Luciano Maria Barone

Publisher: World Scientific

Published: 2014

Total Pages: 718

ISBN-13: 9814513415

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The book teaches students to model a scientific problem and write a computer program in C language to solve that problem. It introduces the basics of C language, and then describes and discusses algorithms commonly used in scientific applications (e.g. searching, graphs, statistics, equation solving, Monte Carlo methods etc.).

SOFSEM 2010: Theory and Practice of Computer Science

Author: Jan van Leeuwen

Publisher: Springer Science & Business Media

Published: 2010-01-20

Total Pages: 795

ISBN-13: 364211265X

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This book constitutes the refereed proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2010, held in Špindleruv Mlýn, Czech Republic, in January 2009. The 53 revised full papers, presented together with 11 invited contributions, were carefully reviewed and selected from 134 submissions. SOFSEM 2010 was organized around the following four tracks: Foundations of computer science, principles of software construction, Data, knowledge, and intelligent systems and Web science.

Introduction to the Tools of Scientific Computing

Author: Einar Smith

Publisher: Springer Nature

Published: 2022-11-28

Total Pages: 429

ISBN-13: 3031169727

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The book provides an introduction to common programming tools and methods in numerical mathematics and scientific computing. Unlike standard approaches, it does not focus on any specific language, but aims to explain the underlying ideas. Typically, new concepts are first introduced in the particularly user-friendly Python language and then transferred and extended in various programming environments from C/C++, Julia and MATLAB to Maple and Mathematica. This includes various approaches to distributed computing. By examining and comparing different languages, the book is also helpful for mathematicians and practitioners in deciding which programming language to use for which purposes. At a more advanced level, special tools for the automated solution of partial differential equations using the finite element method are discussed. On a more experimental level, the basic methods of scientific machine learning in artificial neural networks are explained and illustrated.