Download e-book for kindle: IPython Interactive Computing and Visualization Cookbook by Cyrille Rossant

By Cyrille Rossant

Meant to a person attracted to numerical computing and information technological know-how: scholars, researchers, academics, engineers, analysts, hobbyists...Basic wisdom of Python/NumPy is suggested. a few talents in arithmetic may help you know the idea at the back of the computational equipment.

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Let's start by importing a few objects. py, rather than in the notebook's input! magic import (Magics, magics_class, line_magic) import numpy as np 2. We create a RandomMagics class deriving from Magics. This class contains a few configurable parameters: @magics_class class RandomMagics(Magics): text = Unicode(u'{n}', config=True) max = Int(1000, config=True) seed = Int(0, config=True) 36 Chapter 1 3. We need to call the parent's constructor. seed or None) 4. max)) 5. register_magics(RandomMagics) 6.

Python is typically slower than C because of its interpreted and dynamically-typed nature. ff The data in a NumPy array is stored in a contiguous block of memory in RAM. This property leads to more efficient use of CPU cycles and cache. There's more... There's obviously much more to say about this subject. Our previous book, Learning IPython for Interactive Computing and Data Visualization, contains more details about basic array operations. We will use the array data structure routinely throughout this book.

How to do it... 0! 1. py file. This file will contain the implementation of our custom kernel. py, rather than in the notebook's input! pyplot as plt from io import BytesIO import urllib, base64 40 Chapter 1 2. getvalue())) 3. Now, we write a function that parses a code string, which has the form y = f(x), and returns a NumPy function. strip(), _numpy_namespace, {'x': x}) 4. For our new wrapper kernel, we create a class that derives from Kernel. 0' language = 'python' # will be used for # syntax highlighting language_version = '' banner = "Simple plotting" 5.

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