Throughout the book, you'll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, you'll 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 computation
- Use quantile normalization to ensure that measurements fit a specific distribution
- Represent separate regions in an image with a Region Adjacency Graph
- Convert temporal or spatial data into frequency domain data with the Fast Fourier Transform
- Solve sparse matrix problems, including image segmentations, with SciPy's sparse module
- Perform linear algebra by using SciPy packages
- Explore image alignment (registration) with SciPy's optimize module
- Process large datasets with Python data streaming primitives and the Toolz library
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|Publisher:||O'Reilly Media, Incorporated|
|Product dimensions:||7.00(w) x 9.10(h) x 0.60(d)|
About the Author
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.