Since the early 2000s, a growing body of scientific studies in neuropathology, neurology, neurosurgery, biomechanics, statistics, criminology and psychology has cast doubt on the forensic reliability of medical determinations of Shaken Baby Syndrome (SBS), more recently termed Abusive Head Trauma (AHT). Studies have increasingly documented that accidental short falls and a wide range of medical conditions, can cause the same symptoms and findings associated with this syndrome. Nevertheless, inaccurate diagnoses, unrealistic confidence expression, and wrongful convictions continue to this day. Bringing together contributions from a multidisciplinary expert panel of 32 professionals across 8 countries in 16 different specialties, this landmark book tackles the highly controversial topic of SBS, which lies at the intersection of medicine, science, and law. With comprehensive coverage across multiple disciplines, it explains the scientific evidence challenging SBS and advances efforts to evaluate how deaths and serious brain injuries in infants should be analysed and investigated.

Learn to use IPython and Jupyter Notebook for your data analysis and visualization work. Key Features Leverage the Jupyter Notebook for interactive data science and visualization Become an expert in high-performance computing and visualization for data analysis and scientific modeling A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations Book DescriptionPython is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. What you will learn Master all features of the Jupyter Notebook Code better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible interactive computing experiments Visualize data and create interactive plots in the Jupyter Notebook Write blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and more Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, and R), and learn from actual data through machine learning (scikit-learn) Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV Simulate deterministic and stochastic dynamical systems in Python Familiarize yourself with math in Python using SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory Who this book is forThis book is intended for anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, and hobbyists. A basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.

Get started with Python for data analysis and numerical computing in the Jupyter notebook About This Book * Learn the basics of Python in the Jupyter Notebook * Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn * Perform highly-efficient numerical computations with Numba, Cython, and ipyparallel Who This Book Is For This book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know. What You Will Learn * Install Anaconda and code in Python in the Jupyter Notebook * Load and explore datasets interactively * Perform complex data manipulations effectively with pandas * Create engaging data visualizations with matplotlib and seaborn * Simulate mathematical models with NumPy * Visualize and process images interactively in the Jupyter Notebook with scikit-image * Accelerate your code with Numba, Cython, and IPython.parallel * Extend the Notebook interface with HTML, JavaScript, and D3 In Detail Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data. Style and approach This is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook.

Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists...Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.