So now it is your turn.
Have talked a lot about derivatives and about different methods on how we can implement these derivatives.
We have learned about the forwhat differences, central differences, methods and about the Richardson
methods and also others.
And now is the time to practice this.
But you you're not supposed to just repeat this for different function.
But instead, we will now deal with data files.
So here you see in this template file, I have loaded already a file that you can download in the course
and it is called exercise velocity acceleration data file.
And you see, this is how you can load it and how you can store this in the variable called data when
you call data.
Then you see it's an array with data points and the first column goes from zero to 100 in steps of 0.1.
This will correspond to the time in seconds, and the second column will go here from one to fifteen
point four.
But it is really long, monotonous in between.
And this describes the position of some car, for example, and it's a position in meters.
And when we clocked this, you see, it looks like this we have here at a time in seconds, coordinates
x and meters and you see increases, decreases and so on.
So your task is now to to calculate the velocity, which is of course, the first on a time derivative
of the position and the acceleration, which is the second all the time derivative for the load of data
set that describes the set of points, Ti and X.
As you see here in the plot and you see we have 1000 points which describe to time from zero to 100
and you're supposed to calculate the and I'm using the forward differences, central differences and
the Richardson methods and then the bonus task, which is pretty short, but maybe you don't really
know how to do it.
So maybe you have to google some of the commands of Nampai how you can accomplish this.
You're supposed to find then the maximum value of the acceleration A.I. and the corresponding point
of time I.
So here, just one more hint you have to be very careful here.
So at first, you may think this is pretty easy.
You just use the functions that we have programmed in the previous lectures and then you can apply this.
But the problem is now we do not know the function f of X that describes this curve.
We only have two data points, so you can only work with the data points.
And this means you may have to program the routines that we have previously programmed a bit differently.
So you really have to work with data and so on your functions.
The only input parameter will be data.
There will be no step size, no h because this is all determined by the data itself.
So, for example, h in this case will always be zero point one, so please give it a try.
But if you don't succeed, it's not a big deal.
No problem.
I will provide you the solution in the following lecture.
So now click to the next lecture.
There, you can download the template file and also the data file.
So good luck and have fun, and I see you soon with the solution you.