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Programming for Computations – Python - A Gentle Introduction to Numerical Simulations with Python 3.6, Volume Second Edition
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2.3 NumericalPythonArrays 47 True toFalse and vice versa. Continuing the preceding session with a few more exampleswill illustrate thesepoints, In [9]: x < 5 and x > 3 # x less than 5 AND x larger than 3 Out[9]: True In [10]: x == 5 or x == 4 # x equal to 5 OR x equal to 4 Out[10]: True In [11]: not x == 4 # not x equal to 4 Out[11]: False The first of these compound expressions, i.e., x < 5 and x > 3, could alterna- tivelybewritten3 < x < 5. Itmayalsobeaddedthat thefinalbooleanexpression, i.e.,not x == 4 is equivalent tox != 4 fromabove,whichmostof usfind easier to read. Wewillmeetbooleanexpressionsagainsoon,whenweaddresswhile loopsand branchingin Chap.3. 2.3 NumericalPythonArrays We have seen simple use of arrays before, in ball_plot.py (Sect.1.5), when the height of a ball was computed a thousand times. Corresponding heights and times were handled with arrays y and t, respectively. The kind of arrays used in ball_plot.py is the kind we will use in this book. They are not part of standard Python,8 however, so we import what is needed fromnumpy. The arrays will be of typenumpy.ndarray, referred to asN-dimensionalarrays in NumPy. Arrays are created and treated according to certain rules, and as a programmer, you may direct Python to compute and handle arrays as a whole, or as individual arrayelements. All arrayelementsmust be of the same type, e.g., all integersor all floatingpointnumbers. 2.3.1 ArrayCreationandArrayElements We saw previously how the linspace function from numpy could be used to generate an array of evenly distributed numbers from an interval [a,b].As a quick reminder, we may interactively create an array xwith three real numbers, evenly distributedon [0,2]: In [1]: from numpy import linspace In [2]: x = linspace(0, 2, 3) In [3]: x Out[3]: array([ 0., 1., 2.]) 8 Standard Python does have an array object, but we will stick tonumpy arrays, since they allow moreefficientcomputations.Thus,wheneverwewrite“array”, it isunderstood tobeanumpyarray.
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Programming for Computations – Python A Gentle Introduction to Numerical Simulations with Python 3.6, Volume Second Edition
Title
Programming for Computations – Python
Subtitle
A Gentle Introduction to Numerical Simulations with Python 3.6
Volume
Second Edition
Authors
Svein Linge
Hans Petter Langtangen
Publisher
Springer Open
Date
2020
Language
English
License
CC BY 4.0
ISBN
978-3-319-32428-9
Size
17.8 x 25.4 cm
Pages
356
Keywords
Programmiersprache, Informatik, programming language, functional, imperative, object-oriented, reflective
Category
Informatik
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Programming for Computations – Python