Book Image

Scientific Computing with Python 3

By : Claus Führer, Jan Erik Solem, Olivier Verdier
Book Image

Scientific Computing with Python 3

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more.
Table of Contents (23 chapters)
Scientific Computing with Python 3
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Acknowledgement
Preface
References

Appendix . References

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  2. Anaconda – Continuum Analytics Download page. URL: https://www.continuum.io/downloads.
  3. Michael J. Cloud, Moore Ramon E., and R. Baker Kearfott, Introduction to Interval Analysis, Society for Industrial and Applied Mathematics (SIAM), 2009. ISBN: 0-89871-669-1.
  4. Python Decorator Library. URL: http://wiki.python.org/moin/PythonDecoratorLibrary.
  5. Z. Bai E. Anderson, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Du Croz, A. Greenbaum, S.  Hammarling, A. McKenney, and D. Sorensen, LAPACK Users’ Guide, SIAM, 1999. ISBN: 9780898714470.
  6. fraction – Rational Numbers Library. URL: http://docs.python.org/library/fractions.html.
  7. Claus Führer, Jan Erik Solem, and Olivier Verdier, Computing with Python,  Pearson, 2014. ISBN:  978-0-273-78643-6.
  8. functools – Higher order functions and operations on callable objects. URL: http://docs.python.org/library/functools.html.
  9. Python Generator Tricks. URL: http://linuxgazette.net/100/pramode.html.
  10. G.H. Golub and C.F.V. Loan, Matrix computations,  Johns Hopkins studies in the mathematical sciences. Johns Hopkins University Press, 1996. ISBN: 9780801854149.
  11. Ernst Hairer and Gerhard Wanner, Analysis by its history, Springer, 1995.
  12. Python versus Haskell. URL: http://wiki.python.org/moin/PythonVsHaskell.
  13. The IEEE 754-2008 standard. URL: http://en.wikipedia.org/wiki/IEEE_754-2008.
  14. Interval arithmetic. URL: http://en.wikipedia.org/wiki/Interval_arithmetic.
  15. IPython: Interactive Computing. URL: http://ipython.org/.
  16. H.P. Langtangen, Python scripting for computational science (Texts in computational science and engineering),  Springer, 2008. ISBN:9783540739159.
  17. H.P. Langtangen, A Primer on Scientific Programming with Python (Texts in Computational Science and Engineering), Springer, 2009. ISBN: 9783642024740.
  18. D. F. Lawden, Elliptic Functions and Applications, Springer, 1989. ISBN: 9781441930903.
  19. M. Lutz,  Learning Python: Powerful Object-Oriented Programming, O’Reilly, 2009. ISBN: 9780596158064.
  20. NumPy Tutorial – Mandelbrot Set Example. URL:http://www.scipy.org/Tentative_NumPy_Tutorial/Mandelbrot_Set_Example.
  21. matplotlib. URL: http://matplotlib.sourceforge.net.
  22. Standard: Memoized recursive Fibonacci in Python. URL: http://ujihisa.blogspot.se/2010/11/memoized-recursive-fibonacciin-python.html.
  23. Matplotlib mplot3d toolkit. URL: http://matplotlib.sourceforge.net/mpl_toolkits/mplot3d.
  24. James M. Ortega and Werner C. Rheinboldt, Iterative solution of nonlinear equations in several variables, SIAM, 2000. ISBN: 9780898714616.
  25. pdb – The Python Debugger, documentation: http://docs.python.org/library/pdb.html.
  26. Fernando Pérez and Brian E. Granger. IPython: a System for Interactive Scientific Computing.” In: Comput. Sci. Eng. 9.3 (May 2007), pp. 21–29. URL: http://ipython.org.
  27. Michael J.D. Powell. “An efficient method for finding the minimum of a function of several variables  without calculating derivatives.” In: Computer Journal 7 (2 1964), pp. 155–162. doi: doi:10.1093/comjnl/7.2.155.
  28. Timothy Sauer, Numerical Analysis, Pearson, 2006.
  29. L.F. Shampine, R.C. Allen, and S. Pruess, Fundamentals of Numerical Computing, John Wiley, 1997. ISBN: 9780471163633.
  30. Jan Erik Solem, Programming Computer Vision with Python, O’Reilly Media, 2012. URL: http://programmingcomputervision.com.
  31. Python Documentation – Emulating numeric types. URL: http://docs.python.org/reference/datamodel.html#emulating-numeric-types.
  32. Sphinx: Python Documentation Generator. URL: http://sphinx.pocoo.org/.
  33. J. Stoer and R. Bulirsch, Introduction to numerical analysis. Texts in applied mathematics, Springer, 2002. ISBN: 9780387954523.
  34. Python Format String Syntax. URL: http://docs.python.org/library/string.html#format-string-syntax.
  35. S. Tosi,  Matplotlib for Python Developers, Packt Publishing, 2009. ISBN: 9781847197900.
  36.  Lloyd N. Trefethen and David Bau, Numerical Linear Algebra, SIAM: Society for Industrial and Applied Mathematics, 1997. ISBN: 0898713617.
  37. visvis – The object oriented approach to visualization. URL: http://code.google.com/p/visvis/.
  38. The full list of built-in exceptions can be found at http://docs.python.org/library/exceptions.html