Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Preface
Basic Packages, Functions, and Concepts
Free Chapter
Mathematical Plotting with Matplotlib
Working with Randomness and Probability
Geometric Problems
Finding Optimal Solutions
Miscellaneous Topics
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Saving Matplotlib figures

When you work in an interactive environment, such as an IPython console or a Jupyter notebook, displaying a figure at runtime is perfectly normal. However, there are plenty of situations where it would be more appropriate to store a figure directly to a file, rather than rendering it on screen. In this recipe, we will see how to save a figure directly to a file, rather than displaying it on screen.

You will need the data to be plotted and the path or file object in which you wish to store the output. We store the result in savingfigs.png in the current directory. In this example, we will plot the following data:

`x = np.arange(1, 5, 0.1)y = x*x`

How to do it...

The following steps show how to save a Matplotlib plot directly to a file:

1. The first step is to create the figure, as usual, and add any labels, titles, and annotations that are necessary. The figure will be written to the file in its current state, so any changes to the figure should be made before saving:
`fig, ax = plt.subplots()ax.plot(x, y)ax.set_title("Graph of \$y = x^2\$", usetex=True)ax.set_xlabel("\$x\$", usetex=True)ax.set_ylabel("\$y\$", usetex=True)`
1. Then, we use the savefigmethod on fig to save this figure to a file. The only required argument is the path to output to or a file-like object that the figure can be written to. We can adjust various settings for the output format, such as the resolution, by providing the appropriate keyword arguments. We'll set the Dots per Inch (DPI) of the output figure to 300, which is a reasonable resolution for most applications:
`fig.savefig("savingfigs.png", dpi=300)`

Matplotlib will infer that we wish to save the image in the Portable Network Graphics (PNG) format from the extension of the file given. Alternatively, a format can be explicitly provided as a keyword argument (by using the formatkeyword), or it will fall back to the default from the configuration file.

How it works...

The savefig method chooses the appropriate backend for the output format and then renders the current figure in that format. The resulting image data is written to the specified path or file-like object. If you have manually created a Figure instance, the same effect can be achieved by calling the savefig method on that instance.

There's more...

The savefig routine takes a number of additional optional keyword arguments to customize the output image. For example, the resolution of the image can be specified using the dpi keyword. The plots in this chapter have been produced by saving the Matplotlib figures to the file.

The output formats available include PNG, Scalable Vector Graphics (SVG), PostScript (PS), Encapsulated PostScript (EPS), and Portable Document Format (PDF). You can also save to JPEG format if the Pillow package is installed, but Matplotlib does not support this natively since version 3.1. There are additional customization keyword arguments for JPEG images, such as quality and optimize. A dictionary of image metadata can be passed to the metadata keyword, which will be written as image metadata when saving.