Semesterside for AST2210 - Høst 2016

Exam results for AST2210 autumn 2016 are ready. You will find your examination results in Studentweb.

Explanations and appeals:

Before you submit an appeal, it is recommended that you ask for an explanation of the grades.

More about examinations at UiO can be found here.

22. des. 2016 09:03

Hello everyone!

Some clarifications and updates for the Hinode lab, I realize this is quite a bit of text, but please take the couple of minutes to read it. At the bottom there are also some tips for actually doing parts of the lab.


Administrative stuff:

1) The deadline for delivery is set for the 30th of November, one week from now. Note also that since we are closing in on the end of the semester and results have to be registered with the faculty, the deadline is strict, so please be sure to deliver on time.

2) As has been said at the lecture and group session, this report will not have to follow the article format of before a...

22. nov. 2016 15:44

Hi everyone!

The final lab is upon us, and you can find the text for it here. Please take a look at it before the next lab session this Friday! We will be working with IDL exclusively on this one (due to needed libraries etc), as mentioned early on in the semester.

 

Cheers,

Ainar

14. nov. 2016 11:11

Hello everyone,

The curriculum for the final exam is now codified, and consists of:

- The content of all the lecture slides published (see the individual lecture entries)

- The hand-written notes also published on the course website

- The content of the lab-texts you will have worked with before the exam

 

Cheers,

Ainar

4. nov. 2016 15:45

Hello everyone! Below is a partially finished code meant to illustrate how to find the probabilities P(d | n) and P( d | Q ) and the means as well as uncertainties of n and Q.

You will first need to load n_values, Q_values, as well as lnL, from the output of cmb_likelihood.py. For pointers on how to do this - check plot_contours.py  where this is done in the beginning of the file! Also not shown is how to compute dn and dQ - these are the step distances in n_values and Q_values - so here you can simply check the distance in the steps in the arrays you loaded (subtract two adjacent steps). Below it is shown how to find P(n|d) (P_n), finding P_Q is a matter of integrating over the other dimension of P(d | Q,n) (P) in the for loop.

The code:

# First, exponentiate, and subtract off the biggest

# value to avoid overflow and find P(d| Q,n)

P = np.exp(lnL-np.max(lnL))  

# N...

1. nov. 2016 19:44