Cocalc Pythontutorial Ipynb

Online Supplement for "Numerical Methods in Physics with Python" (Cambridge University Press, 2020) We will assume you know how to launch Python and how to use a text editor (like vim or emacs), or even an integrated development environment (IDE), to save code into a file. While some of the details vary based on which operating system (OS) you employ, most of what we have to say is OS-agnostic. You can run Python code interactively: once you’ve launched a Python shell you get the Python prompt >>> (also known as a chevron). Since this tutorial is in the form of a Jupyter Notebook, the Python prompt will not explicitly appear below. Here are a few examples of things you could type in.

You should press Enter after each line you input at the Python interpreter (or Shift-Enter if you're working on the Notebook). This is similar to other interactive environments that you may have seen before, like Mathematica. It is sometimes called Python’s read-evaluate-print loop (REPL). Note that the first two lines show us we could (if we wanted to) use Python as a calculator. The next line provided us with our first example of using a variable: in this case we created a variable x and assigned the value 42 to it. (Numbers like 3 or 42 are known as literals).

We then used the print() function to print out the variable’s value to the screen. This is our first use of a function: as in mathematics, we use parentheses to show that we are passing in an argument. We won’t go into the different versions of Python at this point, assuming for now that you are using Python 3, where the above example is legitimate. (We discuss Python 2 vs Python 3 in a section near the end of this tutorial). By the end of this lecture you will be able to: Define variables and differentiate between global and local variables.

Identify and use different object types in python. Use some of the python's default functions and define your own functions. Introduction to numpy and matplotlib libraries In this lesson, we'll learn about the Python programming language and the Jupyter notebook, as well as the Python packages numpy and matplotlib. This isn't a very thorough introduction -- we'll just learn the things that are essential for the course. Some resources for learning more about Python and its use in scientific computing are linked at the end of this notebook.

To run the code in this notebook, you'll need an installation of Python, numpy, and matplotlib. The easiest way to get them all is to use CoCalc -- just create a free account, start a new project, and upload this notebook file. Then open this notebook there and you're off. You can also install everything locally on your own machine. For local installation, Anaconda is convenient, or you can just use pip. All of these are free.

If you're new to Python, I recommend using Python version 3.7 or later. The code for this course is written in Python, which is a programming language designed to promote code that is easy to read and write. Python has become one of the most important languages in scientific computing, and is arguably the most popular programming language in the world. It is high-level like MATLAB, but unlike MATLAB it is free and is intended as a general-purpose language. Note that many exercises are followed by a block with some assert statements. These assertions may be preceded by some setup code.

They are provided to give you feedback that you are on the right path -- receiving an AssertionError probably means you've done something wrong. Let's start with a very simple, undirected network. A path in a network is a sequence of edges connecting two nodes. In this simple example, we can easily see that there is indeed at least one path that connects nodes 3 and 4. We can verify this with NetworkX: There can be more than one path between two nodes.

Again considering nodes 3 and 4, there are two such "simple" paths: A simple path is one without any cycles. If we allowed cycles, there would be infinitely many paths because one could always just go around the cycle as many times as desired. In the context of programming languages, a procedure is a collection of instructions to be performed by the computer to accomplish a given task, packaged as a unit. Procedures are also called functions or (sub)routines. Examples: We may implement procedures to perform the following tasks:

Take a positive real number (more precisely, a floating-point number) as argument and return an approximation to its square root. Take a positive integer as input and decide whether it is prime. Take two strings, representing a person's bank account ID and password, and a float, representing an amount to be withdrawn from that account, and if the password matches that associated with the client's as... Jupyter notebook python_in_jupyter_1/Python in Jupyter Notebook a tutorial.ipynb Some basic data analysis and plotting with pandas and matplotlib Installing python/anaconda/etc.

(we can help you offline) Serious python programming (functions, classes, lambda functions, list comprehensions) How to write, manage, deploy software (e.g. git) This IPython / Jupyter notebook is an interactive tutorial in the use of Python for data analysis. First of all, the interface.

The text you see in boxes like this is editable and is written in a format called Markdown similar to the syntax used to edit Wikipedia. If you double-click on this text you will convert it to the raw Markdown for editing. If you want to go back to the nicely-rendered form, click on the editable text and press shift-enter. Second, code to execute appears in editable textboxes, nicely syntax-highlighted for Python: When you evaluate the cell above (click on it and press shift-enter), output should appear below the cell. Yay.

Python is telling you 1+1=2. Not exactly earth-shattering, but that's the basic mode of interaction at work here: Edit the snippet or write another and repeat This class, Probability & Information Theory, introduces the mathematical fields that enable us to quantify uncertainty as well as to make predictions despite uncertainty. These fields are essential because machine learning algorithms are both trained by imperfect data and deployed into noisy, real-world scenarios they haven’t encountered before. Through the measured exposition of theory paired with interactive examples, you’ll develop a working understanding of variables, probability distributions, metrics for assessing distributions, and graphical models.

You’ll also learn how to use information theory to measure how much meaningful signal there is within some given data. The content covered in this class is itself foundational for several other classes in the Machine Learning Foundations series, especially Intro to Statistics and Optimization. Over the course of studying this topic, you'll: Develop an understanding of what’s going on beneath the hood of predictive statistical models and machine learning algorithms, including those used for deep learning. Understand the appropriate variable type and probability distribution for representing a given class of data, as well as the standard techniques for assessing the relationships between distributions. In this lecture, we will cover methods for calculating derivatives and integrals numerically.

While we will cover some of the theory behind these methods, the main practical takeaway is to gain familiarity with existing functions within the numpy and scipy libraries that implement these tasks. In Python, most basic tasks have been coded up already in a very efficient way, so knowing your way around Python's libraries saves a lot of work. We have already introduced numpy. scipy is another important and vast library with many algorithms for scientific computing. By the end of this lecture you will be able to: Do numerical differentiation and understand the principles behind it.

Perform basic numeric integration using python integration libraries Use several integration techniques including: the rectangle, trapezoidal, and Simpson's rules.

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