1. Bækur og afþreying
  2. Bækur og kort
  3. Rafbækur
  4. Fræði- og handbækur
  5. Tölvunarfræði

Python for Scientists

Vörumerki: Cambridge
Vörunúmer: 9781108184687
Rafræn bók. Uppl. sendar á netfangið þitt eftir kaup
5.290 kr.

Python for Scientists

Rafræn bók. Uppl. sendar á netfangið þitt eftir kaup
Rafbók til eignar. Rafbók til eignar þarf að hlaða niður á þau tæki sem þú vilt nota innan eins árs frá því bókin er keypt. Útgáfa: 2

Efnisyfirlit

  • Half-title page
  • Title page
  • Copyright page
  • Contents
  • Preface to the Second Edition
  • Preface to the First Edition
  • 1 Introduction
    • 1.1 Scientific Software
    • 1.2 The Plan of This Book
    • 1.3 Can Python Compete with Compiled Languages?
    • 1.4 Limitations of This Book
    • 1.5 Installing Python and Add-ons
  • 2 Getting Started with IPython
    • 2.1 Tab Completion
    • 2.2 Introspection
    • 2.3 History
    • 2.4 Magic Commands
    • 2.5 IPython in Action: An Extended Example
      • 2.5.1 An IPython terminal workflow
      • 2.5.2 An IPython notebook workflow
  • 3 A Short Python Tutorial
    • 3.1 Typing Python
    • 3.2 Objects and Identifiers
    • 3.3 Numbers
      • 3.3.1 Integers
      • 3.3.2 Real numbers
      • 3.3.3 Boolean numbers
      • 3.3.4 Complex numbers
    • 3.4 Namespaces and Modules
    • 3.5 Container Objects
      • 3.5.1 Lists
      • 3.5.2 List indexing
      • 3.5.3 List slicing
      • 3.5.4 List mutability
      • 3.5.5 Tuples
      • 3.5.6 Strings
      • 3.5.7 Dictionaries
    • 3.6 Python if Statements
    • 3.7 Loop Constructs
      • 3.7.1 The Python for loop
      • 3.7.2 The Python continue statement
      • 3.7.3 The Python break statement
      • 3.7.4 List comprehensions
      • 3.7.5 Python while loop
    • 3.8 Functions
      • 3.8.1 Syntax and scope
      • 3.8.2 Positional arguments
      • 3.8.3 Keyword arguments
      • 3.8.4 Variable number of positional arguments
      • 3.8.5 Variable number of keyword arguments
      • 3.8.6 Python input/output functions
      • 3.8.7 The Python print function
      • 3.8.8 Anonymous functions
    • 3.9 Introduction to Python Classes
    • 3.10 The Structure of Python
    • 3.11 Prime Numbers: A Worked Example
  • 4 NumPy
    • 4.1 One-Dimensional Arrays
      • 4.1.1 Ab initio constructors
      • 4.1.2 Look-alike constructors
      • 4.1.3 Arithmetical operations on vectors
      • 4.1.4 Ufuncs
      • 4.1.5 Logical operations on vectors
    • 4.2 Two-Dimensional Arrays
      • 4.2.1 Broadcasting
      • 4.2.2 Ab initio constructors
      • 4.2.3 Look-alike constructors
      • 4.2.4 Operations on arrays and ufuncs
    • 4.3 Higher-Dimensional Arrays
    • 4.4 Domestic Input and Output
      • 4.4.1 Discursive output and input
      • 4.4.2 NumPy text output and input
      • 4.4.3 NumPy binary output and input
    • 4.5 Foreign Input and Output
      • 4.5.1 Small amounts of data
      • 4.5.2 Large amounts of data
    • 4.6 Miscellaneous Ufuncs
      • 4.6.1 Maxima and minima
      • 4.6.2 Sums and products
      • 4.6.3 Simple statistics
    • 4.7 Polynomials
      • 4.7.1 Converting data to coefficients
      • 4.7.2 Converting coefficients to data
      • 4.7.3 Manipulating polynomials in coefficient form
    • 4.8 Linear Algebra
      • 4.8.1 Basic operations on matrices
      • 4.8.2 More specialized operations on matrices
      • 4.8.3 Solving linear systems of equations
    • 4.9 More NumPy and Beyond
      • 4.9.1 SciPy
      • 4.9.2 SciKits
  • 5 Two-Dimensional Graphics
    • 5.1 Introduction
    • 5.2 Getting Started: Simple Figures
      • 5.2.1 Front-ends
      • 5.2.2 Back-ends
      • 5.2.3 A simple figure
      • 5.2.4 Interactive controls
    • 5.3 Object-Oriented Matplotlib
    • 5.4 Cartesian Plots
      • 5.4.1 The Matplotlib plot function
      • 5.4.2 Curve styles
      • 5.4.3 Marker styles
      • 5.4.4 Axes, grid, labels and title
      • 5.4.5 A not-so-simple example: partial sums of Fourier series
    • 5.5 Polar Plots
    • 5.6 Error Bars
    • 5.7 Text and Annotations
    • 5.8 Displaying Mathematical Formulae
      • 5.8.1 Non-LATEX users
      • 5.8.2 LATEX users
      • 5.8.3 Alternatives for LATEX users
    • 5.9 Contour Plots
    • 5.10 Compound Figures
      • 5.10.1 Multiple figures
      • 5.10.2 Multiple plots
    • 5.11 Mandelbrot Sets: A Worked Example
  • 6 Multi-Dimensional Graphics
    • 6.1 Introduction
      • 6.1.1 Multi-dimensional data sets
    • 6.2 The Reduction to Two Dimensions
    • 6.3 Visualization Software
    • 6.4 Example Visualization Tasks
    • 6.5 Visualization of Solitary Waves
      • 6.5.1 The interactivity task
      • 6.5.2 The animation task
      • 6.5.3 The movie task
    • 6.6 Visualization of Three-Dimensional Objects
    • 6.7 A Three-Dimensional Curve
      • 6.7.1 Visualizing the curve with mplot3d
      • 6.7.2 Visualizing the curve with mlab
    • 6.8 A Simple Surface
      • 6.8.1 Visualizing the simple surface with mplot3d
      • 6.8.2 Visualizing the simple surface with mlab
    • 6.9 A Parametrically Defined Surface
      • 6.9.1 Visualizing Enneper’s surface using mplot3d
      • 6.9.2 Visualizing Enneper’s surface using mlab
    • 6.10 Three-Dimensional Visualization of a Julia Set
  • 7 SymPy: A Computer Algebra System
    • 7.1 Computer Algebra Systems
    • 7.2 Symbols and Functions
    • 7.3 Conversions from Python to SymPy and Vice Versa
    • 7.4 Matrices and Vectors
    • 7.5 Some Elementary Calculus
      • 7.5.1 Differentiation
      • 7.5.2 Integration
      • 7.5.3 Series and limits
    • 7.6 Equality, Symbolic Equality and Simplification
    • 7.7 Solving Equations
      • 7.7.1 Equations with one independent variable
      • 7.7.2 Linear equations with more than one independent variable
      • 7.7.3 More general equations
    • 7.8 Solving Ordinary Differential Equations
    • 7.9 Plotting from within SymPy
  • 8 Ordinary Differential Equations
    • 8.1 Initial Value Problems
    • 8.2 Basic Concepts
    • 8.3 The odeint Function
      • 8.3.1 Theoretical background
      • 8.3.2 The harmonic oscillator
      • 8.3.3 The van der Pol oscillator
      • 8.3.4 The Lorenz equations
    • 8.4 Two-Point Boundary Value Problems
      • 8.4.1 Introduction
      • 8.4.2 Formulation of the boundary value problem
      • 8.4.3 A simple example
      • 8.4.4 A linear eigenvalue problem
      • 8.4.5 A non-linear boundary value problem
    • 8.5 Delay Differential Equations
      • 8.5.1 A model equation
      • 8.5.2 More general equations and their numerical solution
      • 8.5.3 The logistic equation
      • 8.5.4 The Mackey–Glass equation
    • 8.6 Stochastic Differential Equations
      • 8.6.1 The Wiener process
      • 8.6.2 The Itô calculus
      • 8.6.3 Itô and Stratonovich stochastic integrals
      • 8.6.4 Numerical solution of stochastic differential equations
  • 9 Partial Differential Equations: A Pseudospectral Approach
    • 9.1 Initial Boundary Value Problems
    • 9.2 Method of Lines
    • 9.3 Spatial Derivatives via Finite Differencing
    • 9.4 Spatial Derivatives by Spectral Techniques for Periodic Problems
    • 9.5 The IVP for Spatially Periodic Problems
    • 9.6 Spectral Techniques for Non-Periodic Problems
    • 9.7 An Introduction to f2py
      • 9.7.1 Simple examples with scalar arguments
      • 9.7.2 Vector arguments
      • 9.7.3 A simple example with multi-dimensional arguments
      • 9.7.4 Undiscussed features of f2py
    • 9.8 A Real-Life f2py Example
    • 9.9 Worked Example: Burgers’ Equation
      • 9.9.1 Boundary conditions: the traditional approach
      • 9.9.2 Boundary conditions: the penalty approach
  • 10 Case Study: Multigrid
    • 10.1 The One-Dimensional Case
      • 10.1.1 Linear elliptic equations
      • 10.1.2 Smooth and rough modes
    • 10.2 The Tools of Multigrid
      • 10.2.1 Relaxation methods
      • 10.2.2 Residual and error
      • 10.2.3 Prolongation and restriction
    • 10.3 Multigrid Schemes
      • 10.3.1 The two-grid algorithm
      • 10.3.2 The V-cycle scheme
      • 10.3.3 The full multigrid (FMG) scheme
    • 10.4 A Simple Python Multigrid Implementation
      • 10.4.1 Utility functions
      • 10.4.2 Smoothing functions
      • 10.4.3 Multigrid functions
  • Appendix A Installing a Python Environment
    • A.1 Installing Python Packages
    • A.2 Communication with IPython Using the Jupyter Notebook
      • A.2.1 Starting and stopping the notebook
      • A.2.2 Working in the notebook
        • A.2.2.1 Entering headers
        • A.2.2.2 Entering Markdown text
        • A.2.2.3 Converting notebooks to other formats
    • A.3 Communication with IPython Using Terminal Mode
      • A.3.1 Editors for programming
      • A.3.2 The two-windows approach
      • A.3.3 Calling the editor from within IPython
      • A.3.4 Calling IPython from within the editor
    • A.4 Communication with IPython via an IDE
    • A.5 Installing Additional Packages
  • Appendix B Fortran77 Subroutines for Pseudospectral Methods
  • References
  • Hints for Using the Index
  • Index

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Vörumerki: Cambridge
Vörunúmer: 9781108184687
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