Juan Nunez-Iglesias, Stefan Van Der Walt, Harriet Dashnow

Elegant SciPy (eBook, PDF)

The Art of Scientific Python

• Format: PDF

Produktbeschreibung

Welcome to Scientific Python and its community. If youre a scientist who programs with Python, this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice. Youll learn how to write elegant code thats clear, concise, and efficient at executing the task at hand.Throughout the book, youll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, youll work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries.Explore the NumPy array, the data structure that underlies numerical scientific computationUse quantile normalization to ensure that measurements fit a specific distributionRepresent separate regions in an image with a Region Adjacency GraphConvert temporal or spatial data into frequency domain data with the Fast Fourier TransformSolve sparse matrix problems, including image segmentations, with SciPys sparse modulePerform linear algebra by using SciPy packagesExplore image alignment (registration) with SciPys optimize moduleProcess large datasets with Python data streaming primitives and the Toolz library

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 280
• Erscheinungstermin: 11. August 2017
• Englisch
• ISBN-13: 9781491922958
• Artikelnr.: 49153686

Autorenporträt

Juan Nunez-Iglesias is a freelance consultant and a Research Scientist at the University of Melbourne, Australia. Prior positions include Research Associate at HHMI Janelia Farm (where he worked with Mitya Chklovskii) and Research Assistant/PhD student at the University of Southern California (where he studied computational biology supervised by Xianghong Jasmine Zhou). His principal research interests are neuroscience and image analysis. He is also interested in graph methods in bioinformatics and in biostatistics. Stéfan van der Walt is an assistant researcher at the Berkeley Institute for Data Science at the University of California, Berkeley and a senior lecturer in applied mathematics at Stellenbosch University, South Africa. He has been involved in the development of scientific open-source software for more than a decade, and enjoys teaching Python at workshops and conferences. Stéfan is the founder of scikit-image and a contributor to numpy, scipy, and dipy. Harriet Dashnow is a bioinformatician and has worked at the Murdoch Childrens Research Institute, the Department of Biochemistry at the University of Melbourne and the Victorian Life Sciences Computation Initiative (VLSCI). Harriet obtained a BA (Psychology), a BS (Genetics and Biochemistry), and a MS (Bioinformatics) from the University of Melbourne. She is currently working towards a PhD. She organises and teaches computational skills workshops in such areas as genomics, Software Carpentry, Python, R, Unix, and Git version control.