Book Image

IPython Interactive Computing and Visualization Cookbook - Second Edition

By : Cyrille Rossant
Book Image

IPython Interactive Computing and Visualization Cookbook - Second Edition

By: Cyrille Rossant

Overview of this book

Python 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.
Table of Contents (19 chapters)
IPython Interactive Computing and Visualization CookbookSecond Edition
Contributors
Preface
Index

Using Matplotlib styles


Recent versions of Matplotlib have significantly improved the default style of its figures. Today, Matplotlib comes with a set of high-quality predefined styles along with a styling system that lets one customize all aspects of these styles.

How to do it...

  1. Let's import the libraries:

    >>> import numpy as np
        import matplotlib as mpl
        import matplotlib.pyplot as plt
        %matplotlib inline
  2. Let's see a list of all available styles:

    >>> sorted(mpl.style.available)
    ['bmh',
     'classic',
     'dark_background',
     'fivethirtyeight',
     'ggplot',
     'grayscale',
     'mycustomstyle',
     'seaborn',
     ...
     'seaborn-whitegrid']
  3. We create a plot:

    >>> def doplot():
            fig, ax = plt.subplots(1, 1, figsize=(5, 5))
            t = np.linspace(-2 * np.pi, 2 * np.pi, 1000)
            x = np.linspace(0, 14, 100)
            for i in range(1, 7):
                ax.plot(x, np.sin(x + i * .5) * (7 - i))
            return ax
  4. We can set a style with mpl.style.use(). All subsequent plots will...