Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Getting started with IPython
- 3 A short Python tutorial
- 4 Numpy
- 5 Two-dimensional graphics
- 6 Three-dimensional graphics
- 7 Ordinary differential equations
- 8 Partial differential equations: a pseudospectral approach
- 9 Case study: multigrid
- Appendix A Installing a Python environment
- Appendix B Fortran77 subroutines for pseudospectral methods
- References
- Index
Appendix A - Installing a Python environment
Published online by Cambridge University Press: 05 August 2014
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Getting started with IPython
- 3 A short Python tutorial
- 4 Numpy
- 5 Two-dimensional graphics
- 6 Three-dimensional graphics
- 7 Ordinary differential equations
- 8 Partial differential equations: a pseudospectral approach
- 9 Case study: multigrid
- Appendix A Installing a Python environment
- Appendix B Fortran77 subroutines for pseudospectral methods
- References
- Index
Summary
In order to use Python we need to install at least two distinct types of software. The first type is relatively straightforward, consisting of core Python itself, and associated packages, and is discussed in Section A.1 below. The second type addresses a much more complex issue, the interaction of human and machine, i.e., how to instruct the machine to do what we want it to do. An optimal resolution depends in part on what other computer software you wish to use. Some pointers are offered in Section A.2.
Most users of Matlab or Mathematica never see these issues. Invoking either application sets up an editor window or notebook, and an integrated editor accepts instructions from the keyboard. Then a key press invokes the interpreter, and delivers the desired output with an automatic return to the editor window. They obviously get top marks for immediate convenience and simplicity, but in the long term they may not win out on efficiency and versatility.
Installing Python packages
As has been outlined in Section 1.2, we shall need not only core Python, but also the add-on packages IPython (see Chapter 2), numpy, scipy (packages discussed in Chapter 4), matplotlib (see Chapter 5) and potentially mayavi (discussed in Chapter 6). Although data analysis is merely mentioned in Section 4.5, that section recommends the pandas package, and for many this will be a must-have.
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- Chapter
- Information
- Python for Scientists , pp. 205 - 209Publisher: Cambridge University PressPrint publication year: 2014