000			02846nam a22003858i 4500
001			CR9781107447875
005			20250919142053.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			130820s2014||||enk o ||1 0|eng|d
020			_a9781107447875 (ebook)
020			_z9781107061392 (hardback)
020			_z9781107686427 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQ183.9 _b.S865 2014
082	0	0	_a005.13/3 _223
100	1		_aStewart, John, _d1943 July 1- _eauthor.
245	1	0	_aPython for scientists / _cJohn M. Stewart, Department of Applied Mathematics & Theoretical Physics, University of Cambridge.
264		1	_aCambridge : _bCambridge University Press, _c2014.
300			_a1 online resource (xii, 220 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	8		_aMachine generated contents note: 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; 10. Appendix A. Installing a Python environment; Appendix B. Fortran77 subroutines for pseudospectral methods; References; Index.
520			_aPython 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.
650		0	_aScience _xData processing.
650		0	_aPython (Computer program language)
776	0	8	_iPrint version: _z9781107061392
856	4	0	_uhttps://eresourcesptsl.ukm.remotexs.co/user/login?url=https://doi.org/10.1017/CBO9781107447875
907			_a.b16848585 _b2022-10-31 _c2020-12-22
942			_n0
998			_a1 _b2020-12-22 _cm _da _feng _genk _y0 _z.b16848585
999			_c652201 _d652201