This notebook contains an excerpt from the Python Programming and Numerical Methods - A Guide for Engineers and Scientists, the content is also available at Berkeley Python Numerical Methods. The copyright of the book belongs to Elsevier.

This chapter gets you started with Python, using it as a calculator, managing Python packages, getting familiar with the Jupyter Notebook. As you will see, Python has a great community with packages that could potentially do anything.

We could see that defining a Python function need the following two components: Function header: A function header starts with a keyword def , followed by a pair of parentheses with the input arguments inside, and ends with a colon (:)

The easiest way to run Python code is through the Python shell or Ipython Shell (which stands for Interactive Python). The Ipython shell is richer than Python shell, such as Tab autocompletion, color-highlighted error messages, basic UNIX shell integration and so on.

There are three families of linear multistep methods are commonly used: Adams–Bashforth methods, Adams–Moulton methods, and the backward differentiation formulas (BDFs).

This chapter describes several methods of numerically integrating functions. By the end of this chapter, you should understand these methods, how they are derived, their geometric interpretation, and their accuracy.

There are more complicated cases in real world, it would be great if we have a method that we can use to analyze the characteristics of the wave. The Fourier Transform can be used for this purpose, which it decompose any signal into a sum of simple sine and cosine waves that we can easily measure the frequency, amplitude and phase.

This notebook contains an excerpt from the Python Programming and Numerical Methods - A Guide for Engineers and Scientists, the content is also available at Berkeley Python Numerical Methods. The copyright of the book belongs to Elsevier. We also have this interactive book online for a better learning experience.

Now we see how we can train a model to do the classification in Python, there are also many other models that you can use in scikit-learn, we leave this for you to explore. For example, you can use an artificial neural network (ANN) to do the …

For example, the math.sin function in Python is a set of tasks (i.e., mathematical operations) that computes an approximation for sin (x). Rather than having to retype or copy these instructions every time you want to use the sin function, it is useful to store this sequence of instruction as a function that you can call over and over again.