Skip to main content Accessibility help
×
Home
Python for Scientists
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 2
  • Export citation
  • Recommend to librarian
  • Buy the print book

Book description

Python is a free, open source, easy-to-use software tool that offers a significant alternative to proprietary packages such as MATLAB® and Mathematica®. This book covers everything the working scientist needs to know to start using Python effectively. The author explains scientific Python from scratch, showing how easy it is to implement and test non-trivial mathematical algorithms and guiding the reader through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the program's capabilities. In particular, readers are shown how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. Instead of exercises the book contains useful snippets of tested code which the reader can adapt to handle problems in their own field, allowing students and researchers with little computer expertise to get up and running as soon as possible.

Reviews

'… the practitioner who wants to learn Python will love it. This is the type of book I have been looking for to learn Python … concise, yet practical.'

Source: European Mathematical Society (euro-math-soc.eu)

Refine List

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Send to Kindle
  • Send to Dropbox
  • Send to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
×

Contents

References
Ascher, U. M., Mattheij, R. M. M. and Russell, R. D. (1995), Numerical Solution of Boundary Value Problems for Ordinary Differential Equations, SIAM.
Ascher, U. M., Mattheij, R. M. M., Russell, R. D. and Petzold, L. R. (1998), Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations, SIAM.
Bader, G. and Ascher, U. M. (1987), ‘A new basis implementation for a mixed order boundary value odesolver’, SIAM J. Sci. Stat. Comp. 8, 483-500.
Bellen, A. and Zennaro, M. (2003), Numerical Methods for Delay Differential Equations, Oxford.
Bogacki, P. and Shampine, L. F. (1989), ‘A 3(2) pair of Runge-Kutta formulas’, Appl. Math. Lett. 2, 321-325.
Boyd, J. P. (2001), Chebyshev and Fourier Spectral Methods, second edn, Dover.
Brandt, A. and Livne, O. E. (2011), Multigrid Techniques: 1984 Guide with Applications to Fluid Dynamics, revised edn, SIAM.
Briggs, W. L., Henson, V. E. and McCormick, S. (2000), A Multigrid Tutorial, second edn, SIAM.
Butcher, J. C. (2008), Numerical Methods for Ordinary Differential Equations, second edn, Wiley.
Coddington, E. A. and Levinson, N. (1955), Theory of Ordinary Differential Equations, McGraw-Hill.
Driver, R. D. (1997), Ordinary and Delay Differential Equations, Springer.
Erneux, Y. (2009), Appplied Delay Differential Applications, Springer.
Evans, L. C. (2013), Introduction to Stochastic Differential Equations, AMS.
Fornberg, B. (1995), A Practical Guide to Pseudospectral Methods, Cambridge.
Funaro, D. and Gottlieb, D. (1988), ‘A new method of imposing boundary conditions in pseudospectral approximations of hyperbolic equations’, Math. Comp. 51, 599-613.
Gardiner, C. W. (2009), Handbook ofStochastic Methods, fourth edn, Springer.
Gnuplot Community (2012), ‘Gnuplot 4.6, an interactive plotting program’, available from www.gnuplot.info/docs_4.6/gnuplot.pdf.
Hesthaven, J. S. (2000), ‘Spectral penalty methods’, Appl. Num. Maths. 33, 23-41.
Hesthaven, J. S., Gottlieb, S. and Gottlieb, D. (2007), Spectral Methods for Time-Dependent Problems, Cambridge.
Higham, D. J. (2001), ‘An algorithmic introduction to numerical solution of stochastic differential equations’, SIAM Rev. 43, 525-546.
Hull, J. (2009), Options, Futures and Other Derivatives, seventh edn, Pearson.
Janert, K. (2010), Gnuplot in Action, Manning Publications Co.
Kloeden, P. E. and Platen, E. (1992), Numerical Solution of Stochastic Differential Equations, Springer.
Lambert, J. D. (1992), Numerical Methods for Ordinary Differential Systems, Wiley.
Langtangen, H. P. (2008), Python Scripting for Computational Science, third edn, Springer.
Langtangen, H. P. (2009), A Primer on Scientific Programming with Python, Springer.
Lutz, M. (2009), Learning Python, fourth edn, O'Reilly.
Mackey, M. C. and Glass, L. (1977), ‘Oscillation and chaos in physiological control systems’, Science 197, 287-289.
Matplotlib Community (2013), ‘Matplotlib release 1.2.1’, available from http://matplotlib.org/Matplotlib.pdf.
Mayavi Community (2011), ‘Mayavi: 3d scientific data visualization and plotting in Python’, available from http://docs.enthought.com/mayavi/mayavi/.
McKinney, W. W. (2012), Python for Data Analysis, O'Reilly.
Murray, J. D. (2002), Mathematical Biology I. An Introduction, Springer.
Numpy Community (2013a), ‘Numpy reference release 1.7.0’, available from http://docs.scipy.org/doc/numpy/numpy-ref-1.7.8.pdf.
Numpy Community (2013b), ‘Numpy user guide release 1.7.0’, available from http://docs.scipy.org/doc/numpy/numpy-user-1.7.8.pdf.
Øksendal, B. (2003), Stochastic Differential Equations, sixth edn, Springer.
Peitgen, H-O. and Richter, P. H. (1986), The Beauty ofFractals: Images of Complex Dynamical Systems, Springer.
Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P. (2007), Numerical Recipes: The Art of Scientific Computing, third edn, Cambridge.
Ramachandandran, P. and Variquaux, G. (2009), ‘Mayavi user guide release 3.3.1’, available from http://code.enthought.com/projects/mayavi/docs/development/latex/mayavi/mayavi_user_guide.pdf.
Rossant, C. (2013), Learning IPython for Interactive Computing and Data Visualization, Packt Publishing.
Scipy Community (2012), ‘SciPy reference guide release 0.12.0’, available from http://docs.scipy.org/doc/scipy/scipy-ref.pdf.
Sparrow, C. (1982), The Lorenz Equations, Springer.
Tosi, S. (2009), Matplotlib for Python Developers, Packt Publishing.
Trefethen, L. N. (2000), Spectral Methods in MATLAB, SIAM.
Trottenberg, U., Oosterlee, C. W. and Schüller, A. (2001), Multigrid, Academic Press.
van Rossum, G. and Drake, F. L. Jr. (2011), An Introduction to Python, Network Theory Ltd.
Wesseling, P. (1992), An Introduction to Multigrid Methods, Wiley.

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Book summary page views

Total views: 0 *
Loading metrics...

* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

Usage data cannot currently be displayed.