Book Image

Learning SciPy for Numerical and Scientific Computing

By : Francisco J. Blanco-Silva
Book Image

Learning SciPy for Numerical and Scientific Computing

By: Francisco J. Blanco-Silva

Overview of this book

<p>It's essential to incorporate workflow data and code from various sources in order to create fast and effective algorithms to solve complex problems in science and engineering. Data is coming at us faster, dirtier, and at an ever increasing rate. There is no need to employ difficult-to-maintain code, or expensive mathematical engines to solve your numerical computations anymore. SciPy guarantees fast, accurate, and easy-to-code solutions to your numerical and scientific computing applications.<br /><br />"Learning SciPy for Numerical and Scientific Computing" unveils secrets to some of the most critical mathematical and scientific computing problems and will play an instrumental role in supporting your research. The book will teach you how to quickly and efficiently use different modules and routines from the SciPy library to cover the vast scope of numerical mathematics with its simplistic practical approach that's easy to follow.<br /><br />The book starts with a brief description of the SciPy libraries, showing practical demonstrations for acquiring and installing them on your system. This is followed by the second chapter which is a fun and fast-paced primer to array creation, manipulation, and problem-solving based on these techniques.<br /><br />The rest of the chapters describe the use of all different modules and routines from the SciPy libraries, through the scope of different branches of numerical mathematics. Each big field is represented: numerical analysis, linear algebra, statistics, signal processing, and computational geometry. And for each of these fields all possibilities are illustrated with clear syntax, and plenty of examples. The book then presents combinations of all these techniques to the solution of research problems in real-life scenarios for different sciences or engineering — from image compression, biological classification of species, control theory, design of wings, to structural analysis of oxides.</p>
Table of Contents (15 chapters)

Matrix creation


In SciPy, a matrix structure is given to any one- or two-dimensional ndarray, with either the matrix or mat command. The complete syntax is as follows:

numpy.matrix(data=object, dtype=None, copy=True)

In the creation of matrices, the data may be given as a string or as ndarray, which is very convenient. When using strings, the semicolon denotes change of row, and the comma denotes change of column:

>>> A=numpy.matrix("1,2,3;4,5,6")
>>> A
matrix([[1, 2, 3],
        [4, 5, 6]])
>>> A=numpy.matrix([[1,2,3],[4,5,6]])
>>> A
matrix([[1, 2, 3],
        [4, 5, 6]])

Another way of creating a matrix from a two-dimensional array is by enforcing the matrix structure on a new object, copying the data of the former with the asmatrix routine.

We say that a matrix is sparse if most of its entries are zeros. It is a waste of memory to input such matrices in the usual way, especially if the dimensions are large, and SciPy contemplates different procedures...