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Mastering Python scientific computing : a complete guide for Python programmers to master scientific computing using Python APIs and tools /
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2015.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: The Landscape of Scientific Computing
- and Why Python?; Definition of scientific computing; A simple flow of the scientific computation process; Examples from scientific/engineering domains; A strategy for solving complex problems; Approximation, errors, and associated concepts and terms; Error analysis; Conditioning, stability, and accuracy; Backward and forward error analysis; Is it okay to ignore these errors?; Computer arithmetic and floating-point numbers
- The background of the Python programming languageThe guiding principles of the Python language; Why Python for scientific computing?; Compact and readable code; Holistic language design; Free and open source; Language interoperability; Portable and extensible; Hierarchical module system; Graphical user interface packages; Data structures; Python's testing framework; Available libraries; The downsides of Python; Summary; Chapter 2: A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes; Mathematical components of scientific computations
- A system of linear equationsA system of nonlinear equations; Optimization; Interpolation; Extrapolation; Numerical integration; Numerical differentiation; Differential equations; The initial value problem; The boundary value problem; Random number generator; Python scientific computing; Introduction to NumPy; The SciPy library; The SciPy Subpackage; Data analysis using pandas; A brief idea of interactive programming using IPython; IPython parallel computing; IPython Notebook; Symbolic computing Using SymPy; The features of SymPy; Why SymPy?; The plotting library; Summary
- Chapter 3: Efficiently Fabricating and Managing Scientific DataThe basic concepts of data; Data storage software and toolkits; Files; Structured files; Unstructured files; Database; Possible operations on data; Scientific data format; Ready-to-use standard datasets; Data generation; Synthetic data generation (fabrication); Using Python's built-in functions for random number generation; Bookkeeping functions; Functions for integer random number generation; Functions for sequences; Statistical-distribution-based functions; Nondeterministic random number generator
- Designing and implementing random number generators based on statistical distributionsA program with simple logic to generate five-digit random numbers; A brief note about large-scale datasets; Summary; Chapter 4: Scientific Computing APIs for Python; Numerical scientific computing in Python; The NumPy package; The ndarrays data structure; File handling; Some sample NumPy programs; The SciPy package; The optimization package; The interpolation package; Integration and differential equations in SciPy; The stats module; Clustering package and spatial algorithms in SciPy