Cocalc Beginner Ipynb

Once you are at the python command line the first step is to import basic functionality from SymPy and the Mechanics module, otherwise you will only have basic python commands available to work with. We will import the symbols function from SymPy core and with the * method bring in all functionality from the mechanics package. You can now see what functions and variables that are available to you with:: This is a long list of available functions. Read about the python import statement to learn about better ways to import only what you need. One good explanation is http://effbot.org/zone/import-confusion.htm.

To get started working with vectors we will need to create a reference frame, as all vectors need to be defined with respect to a reference frame in the mechanics package. If you know the name of the command that you want to use simply use the builtin help function to bring up the documentation for the function. In our case we need to use the ReferenceFrame class. The ReferenceFrame class manages everything about rotations, angular velocities, and angular accelerations with respect to other reference frames. Now create an inertial reference frame called N for "Newtonian" as was described in the the docstring: 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 ... stands for 'Collaboritive Calculation in the Cloud'.

Their platform allows you to: You can set up an account and do all this for free without installing any software on your own computer, other than a web browser. (Acknowledgment: This web page is a revision of one originally authored by Paul Meyer-Reimer.) A project is like a folder for your work. But you have to create one before you can do anything else. After you Create an account on CoCalc, or after you sign in, you'll land on the Projects page.

Your new notebook will open. Let's get oriented... Before you start, you need to create a Cocalc account and add your partner as a collaborator [TODO: Link to instructions] Navigate to www.cocalc.com and sign in using your university email address. Click on project NSCI0007_20_21 in the project list to load your project. Your project is essentially a virtual computer hosted in the cloud, and it comes preinstalled with all the software and tools you need to get Python programming straight away!

Click on the Handouts folder then the 01_Introduction folder. DEFINITIONS: Cocalc - the online platform we will be using, providing access to virtual computers hosted in the cloud. Project - Every student has a Cocalc account allowing access to a project, which is a virtual computer including operating system (Linux) and software libraries. Jupyter Notebook - a type of file which contains Python code and formatted text, allowing us to combine computations, results and descriptive text in a single file. It is also sometimes called an IPython Notebook, and has the extension .ipynb. Python - the programming language allowing us to perform scientific computing.

Python code is usually stored in text files with the file ending ".py": Every line in a Python program file is assumed to be a Python statement, or part thereof. The only exception is comment lines, which start with the character # (optionally preceded by an arbitrary number of white-space characters, i.e., tabs or spaces). Comment lines are usually ignored by the Python interpreter. To run our Python program from the command line we use: On UNIX systems it is common to define the path to the interpreter on the first line of the program (note that this is a comment line as far as the Python interpreter is...

This quickstart is intended for developers who are ready to dive into the code and see an example of how to integrate 🤗 Datasets into their model training workflow. If you're a beginner, we recommend starting with our tutorials, where you'll get a more thorough introduction. Each dataset is unique, and depending on the task, some datasets may require additional steps to prepare it for training. But you can always use 🤗 Datasets tools to load and process a dataset. The fastest and easiest way to get started is by loading an existing dataset from the Hugging Face Hub. There are thousands of datasets to choose from, spanning many tasks.

Choose the type of dataset you want to work with, and let's get started! Resample an audio dataset and get it ready for a model to classify what type of banking issue a speaker is calling about. Apply data augmentation to an image dataset and get it ready for a model to diagnose disease in bean plants. Tokenize a dataset and get it ready for a model to determine whether a pair of sentences have the same meaning. This notebook comes from A Whirlwind Tour of Python by Jake VanderPlas (OReilly Media, 2016). This content is licensed CC0.

The full notebook listing is available at https://github.com/jakevdp/WhirlwindTourOfPython. < How to Run Python Code | Contents | Basic Python Semantics: Variables and Objects > Python was originally developed as a teaching language, but its ease of use and clean syntax have led it to be embraced by beginners and experts alike. The cleanliness of Python's syntax has led some to call it "executable pseudocode", and indeed my own experience has been that it is often much easier to read and understand a Python script than... Here we'll begin to discuss the main features of Python's syntax. Syntax refers to the structure of the language (i.e., what constitutes a correctly-formed program).

For the time being, we'll not focus on the semantics – the meaning of the words and symbols within the syntax – but will return to this at a later point. This script is a bit silly, but it compactly illustrates several of the important aspects of Python syntax. Let's walk through it and discuss some of the syntactical features of Python The Jupyter notebook is a platform for using and writing code in a dynamic way that allows users to combine cells of code snippets that are executed with a persistent namespace and kernel alongside... In this notebook, we cover some basics of Jupyter functionality along with a discussion of some details of how you can use python in this environment and elsewhere. For this workshop, we've constructed an online environment for everyone to use in order to smooth out any platform-dependent installation issues, but you'll probably want to install the tools we use today locally on...

To do this, we recommend Anaconda, which is an effective tool for python package management that can create virtual environments, comes with a pre-installed IDE, and includes all of the Jupyter functionality that you'll... The Anaconda installer should be detailed on the page linked above, but here's another resource for installation that might be helpful. Jupyter notebooks are broken down into "cells" which might contain either code or markdown. If you select a cell with your mouse, it should be highlighted with a green border indicating that you are in "edit," mode and can edit the contents of the cell. If text reading In [ ]: is on the left hand side of the cell, it's a "code" cell. For example, type the following and press "Shift + Enter"

Pressing "Shift + Enter" executes the code in the cell, prints the output below the cell, and creates a new cell below that one. In addition to code cells, you can also write your own markdown cells by converting a cell using either the dropdown menu in the toolbar or pressing "Esc + m". In general, pressing escape enters "command mode" for which you can issue a number of commands, including Let's build up from the basics: what is a Jupyter Notebook? A notebook is a document made of cells. You can write in some of them (markdown cells) or you can perform calculations in Python (code cells) and run them like this:

Cool, huh? This combination of prose and code makes Jupyter Notebook ideal for experimentation: we can see the rationale for each experiment, the code, and the results in one comprehensive document. Try it yourself now. Click "Copy & Edit" in the top right to get your own editable version of this notebook, then click the cell above and hit Shift-Enter. Other renowned institutions in academia and industry use Jupyter Notebook, including Google, Microsoft, IBM, Bloomberg, Berkeley and NASA among others. Even Nobel-winning economists use Jupyter Notebooks for their experiments and some suggest that Jupyter Notebooks will be the new format for research papers.

A type of cell in which you can write text is called a Markdown cell. Markdown is a very popular markup language. To specify that a cell is Markdown you need to click in the drop-down menu in the toolbar and select Markdown.

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