Cocalc Function Overview Ipynb

CoCalc: Collaborative Calculations and Data Science Overview 02: Sensitivity Analysis and Abstract Modeling Abstract modeling for setting up larger LP problems code a linear programming problem with the model and data separated using dense arrays write reusable models that can be repurposed by simply changing the data identify sensitive parameters in a linear programming problem.

This lecture will consist of explaining what a function is in Python and how to create one. Functions will be one of our main building blocks when we construct larger and larger amounts of code to solve problems. Formally, a function is a useful device that groups together a set of statements so they can be run more than once. They can also let us specify parameters that can serve as inputs to the functions. On a more fundamental level, functions allow us to not have to repeatedly write the same code again and again. If you remember back to the lessons on strings and lists, remember that we used a function len() to get the length of a string.

Since checking the length of a sequence is a common task you would want to write a function that can do this repeatedly at command. Functions will be one of most basic levels of reusing code in Python, and it will also allow us to start thinking of program design (we will dive much deeper into the ideas of... Put simply, you should use functions when you plan on using a block of code multiple times. The function will allow you to call the same block of code without having to write it multiple times. This in turn will allow you to create more complex Python scripts. To really understand this though, we should actually write our own functions!

Overview 01: Linear Programming and Pyomo formulate a linear program for a given problem scenario. determine if the four assumptions of linear program are satisfied. identify corner point feasible solutions, optimal solutions, the feasible region, and feasible solutions for linear programs with either bounded or unbounded feasible regions. solve a small linear program graphically. 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. < Control Flow | Contents | Errors and Exceptions > So far, our scripts have been simple, single-use code blocks. One way to organize our Python code and to make it more readable and reusable is to factor-out useful pieces into reusable functions. Here we'll cover two ways of creating functions: the def statement, useful for any type of function, and the lambda statement, useful for creating short anonymous functions.

Functions are groups of code that have a name, and can be called using parentheses. We've seen functions before. For example, print in Python 3 is a function: Here print is the function name, and 'abc' is the function's argument. ... 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... This notebook shows how to define functions with one (or several) inputs and outputs. It also shows how to use to dot (.) syntax to apply a function to each element of an array. The basic approach to define a function with the name fn is Once you have defined a function, you can use it (call on it) by, for instance, y1 = fn(2,1) or by y1 = fn(b1,1) if b1=2 is already defined.

This will generate a y1 variable (not a y variable) in the workspace. Inside the function, x is then 2 and b is 1. Wrapping performance critical code in functions often speeds up the computation, since it helps Julia/the compiler to make optimisations. Do not use any global variables inside a function. It slows down the computation and can lead to surprising behaviour (see further down in this notebook). Instead, make all the inputs explicit function arguments.

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...

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