Now we are at the stage where we are ready to consider how to handle our tomography data. When we're working with data and performing mathematical aberrations, we need some tools to help us do this in efficient way. You have probably already worked with various tools like MATLAB, Python programming, or even Excel, which can also be a useful tool for some data analysis tasks. In this course, we have chosen to work with Jupyter notebooks. And there are several reasons why this is a very effective and great tool for this kind of work. There are many good examples on how to use Jupyter notebooks. And now Nicolai Riis from DTU Compute, Technical University of Denmark, will give us very prestigious and very advanced example on how to use Jupyter Notebooks effectively in research. >> You may have heard about the gravitational wave detection. For example, LIGO, the Laser Interferometer Gravitational-Wave Observatory. And they have a Jupyter Notebook on GitHub that you can download. >> In case you are not familiar with the LIGO experiment, I should just mention that it is a large scale physics experiment and observatory, designed to detect cosmic gravitational waves. Gravitational waves are disturbances in the curvature of space time, generated by accelerated masses that propagate hours from the source at the speed of light. This means, actually, that LIGO as a facility is able to allow us to study, for example, colliding black holes that are sources of such gravitational waves. In this case, we will be seeing a presentation of how data from the LIGO vicinity recorded, and how they documented and presented in Jupyter Notebook that is publicly available. >> Okay, and once you get into this notebook here, the LIGO people have created a notebook describing all the dates of processing done from detecting. Or receiving the signal all the way to verifying that it came from a gravitational wave. And the cool thing is, of course, that you can go through every step of the data processing and really see what they have done to handle the data, and remove noise from it and so on. So you could spend a lot of time with this. I will just go all the way to the end, Down here, And then I will click Run All Above. And this may take a little while. Now the code has finished running. And if you run the final cell down here, now we have two audio files. We have the template audio file, which indicates sort of the expected sound that you would get. The wave has been translated now to sound that the human ear can hear. Then we have the detected, one of the detected possible waves also in the same format. So if we listen first to the the template sound, We get this little beep sound, all right? And now we listen to the actual detected sound where the people of LIGO, and now we have removed as much noise as we can from the signal. So listen closely. [SOUND] Now you hear this little beep, and then that's actually the gravitational wave detection that was released in the payback. >> So what we have learned from this example is that Jupyter Notebook can be a very effective tool for showing your data, working with your data. Analyzing your data in a interactive manner where you are growing on, for example, various data resources, and using different tools, for example, visualization. Or in this case, we're presenting your data in a way that is available for humans. And also, very advanced potentials for programming your own analysis code to treat your data using a variety of programming languages. This can be both MATLAB, Python, but it can also be a number of other programming languages of your choice. And it also makes very easy to both document for yourself, but also for the public to make, for example, Jupyter Notebook publicly available. And give everybody insight into how the analysis was actually performed. In the for this module, you will get a short introduction to how to work with Jupyter notebooks. And a very short primer on the Python language that we are using in the Jupyter notebooks. But you should note that you do not have to be an experienced programmer in order to follow the exercises, and do the assignments. Because they designed it in a way that you will only need to, for example, modify some parameters, and run it in snippets of program code to see what the effect is. But it will also be possible for you if you are more experience to build upon the thing that we present in the Jupyter notebooks to make more advanced analysis. And try, for example, also even to work with your data if you wish to do so. Now that we have seen how others are working with their experimental data in Jupyter notebooks, and you have been given an introduction to how the tomography methods, they work. What will happen next is that we will teach you how to work with real data, based on some practical examples, some cases that we will present to you. And the scientific background of them, but also in the give you actual data that will present these experimental cases. And teach you how to work with them, and analyze them in the Jupyter notebooks.