This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science. Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.
Plotting
Loading...
Do curso por Rice University
Principles of Computing (Part 1)
354 classificações
Na lição
Testing, plotting, and grids
This week, we will explain the importance of testing. We will also learn to solve problems with grids.
Conheça os instrutores
Scott RixnerProfessor
Computer Science
Joe WarrenProfessor
Computer Science
Luay NakhlehAssociate Professor
Computer Science; Biochemistry and Cell Biology
[MUSIC]
[MUSIC]
Welcome to class, in this lecture we're going to talk about Plotting,
Plotting is a process in which you take a collection of data.
And make a picture that allows us to visualized the structure of that data.
For this class we use Plotting to understand the performance of
your program.
In particular, we'll look at the relationship between the size of
the input that you provided the program and the running time of the program.
This will help us structure the difference between good and bad programs.
So before we talk about how to plot,
let's talk little bit more about why we should like to build plots.
There's a old saying, a picture is worth a thousand words.
Let me give you an example here we see a picture and
this is a plot of Scott's aggravation level as a function of
the number of global variables that you use in your program.
Actually, I just made that up.
This is really just a plot of Global Temperature versus time.
And it's clear that this picture has inspired a lot
more than 1,000 words of discussion.
We're going to use Plotting here to understand large amounts of data and
organize it in a way where we can visualize the trend and
the behavior of the data without actually having to
wade through a massive amounts of individual numerical values.
I think as your career in Computer Science advances,
you'll find that Plotting is going to be a very powerful tool.
All right, what we'll do now is we'll look at some example code for
creating plots inside CodeSkulptor.
This will code, we'll use the simple module that we've built for CodeSkulptor.
There's lots of very similar modules that available for Desktop Python, so
anything you learn here should be applicable to Desktop Python very easily.
Simpleplot takes as input a list of points.
So it's going to be something that looks like this, where each point is
represented by a two pole that consists of an x0 and y0 for that point.
In our example,
we're going to plot points generated from the graphs of three functions.
A function that doubles the number, a function that squares the number, and
a function that raises 2 to a particular power.
All right.
The heart of our example code is the function create_plots.
This function is going to take.
The functions double, square and x, and
compute us at a points that lie on the grass of these functions.
The parameters begin and stride are going to control the position and
spacing of the x-coordinates of these points.
The x-coordinate will start at begin end at end, and
be spaced using this parameter stride.
This is very much like what range expression would be included in Python.
The difference here, is the stride doesn't have to be an integer.
This code actually builds a list with the corresponding x-coordinates, here.
Once we have this list, x cords.
We can then use three list comprehensions here to compute
the corresponding y-values for these x-values and build list of points
corresponding to graph of each function, so that's what takes place here.
To illustrate how these three list comprehensions work,
I've added a print statement that actually prints out double plot,
the list of points generated by this statement right here.
So let's run that.
And what you can see here is we have a list of tuples with a property that each
tuple consists of two numbers, an x and a y-coordinate.
Where the y-coordinate is actually twice the x-coordinate.
So this really is the graph of two times a number.
To turn that list of points into a plot,
I'm going to call the plot lines method from the simple plot module.
You can go up here and
use the Docs to actually find out the syntax of how plot lines works.
It takes a collection of parameters.
The most important thing is it allows you to give a list of, list of points.
So we're going to plot all three of these functions simultaneously.
So if I run that, what I see here is, I see a nice plot
that consists of the plot for the function double.
It's this yellow plot here.
Then I see a plot of the function square here.
And then I see the plot of this function exp, the Exponential function.
So between zero and two, all these functions look fairly similar, but
notice that I can actually change the range of the plots here.
This is why we have this parameter here.
What I'm going to do, is I'm going to extend the range of
the functions that we're plotting here to give it from 0 to 5.
So if I do that.
What comes out is I now see that well,
the linear function is actually staying here fairly low.
But the quadratic function and
the exponential function are actually staying very similar.
Let's keep going, let's plot a little larger range.
So let's plot this to 10.
And now when we look at our plot, we've actually gained some valuable information.
What we can see here is that the linear function is
somehow less than this quadratic function, but it's just a little bit less.
But this exponential function is growing very, very fast, it's a lot larger.
And so by looking at this plot we gain some valuable information about Linear,
Quadratic and Exponential Functions that somehow Exponential Functions grow a lot
faster than Quadratic and Linear functions.
And so for example later in the class, we're looking at various methods for
solving problems, we're going to try to focus on things where the running time is
kind of linear in the input size, or quadratic in the input size.
We're going to avoid things where the running time is exponential in
the input size.
Those are just not going to be tractable in practice.
So this is kind of a first start at building some plotting code and
learning to analyze the behavior of sets of points and functions.
We're going to do this more and more as we go through the class.
I'll see you next lecture.
Explore nosso catálogo
Registre-se gratuitamente e obtenha recomendações, atualizações e ofertas personalizadas.
O Coursera proporciona acesso universal à melhor educação do mundo fazendo parcerias com as melhores universidades e organizações para oferecer cursos on-line.
© 2018 Coursera Inc. Todos os direitos reservados.
