Cassini data #99

Hi Benjamin, thanks a lot for this.

I am looking for the ``common'' plasma parameters: (n, T, v, B) and the position of the satellite (+ which frame of reference is it in?).

I am very new to any data that's far from Earth, so there's a bit of a learning curve!

Thank you very much for your help.

@vgenot I guess you may be able to answer better than us 😅.

Merci Vincent!

Hello,

The link above is an image of the data tree, and I didn't know which folder was best to start.

If you are interested by Cassini's closest approach to Saturn, you should probably use a dataset defined in "orbit saturn".
There are 3 different datasets for 3 different sampling time: 1 min, 1 sec or 5 min (the most suitable depends on your study).

Is there a way of seeing what the exact values of local minima/maxima are on a graph? For example, Cassini's closest approach to Saturn.

You can use the "Statistics module" (not directly from the Plot Module).

statistics

statistics_result

You can take a look to this publication: https://doi.org/10.1016/j.pss.2021.105214 to have more information on AMDA with some use cases.
If you have more questions/feedbacks on AMDA, you received a contact email with your AMDA registration (this repository is dedicated to Speasy project).

But, you can do the same stuff with Speasy!

import speasy as spz
amda_tree = spz.inventories.data_tree.amda
# '5 min' dataset:
cassini_orbit_dataset = amda_tree.Parameters.Cassini.Ephemeris__Cassini.orbit_saturn.cass_orb_saturn
cass_r_sat = spz.get_data(cassini_orbit_dataset.cass_r_sat,'2010-01-01', '2011-01-01')
print(cass_r_sat.to_dataframe().describe())

The result will be:

 cass_r_sat
count 105107.000000
mean 31.526586
std 11.397203
min 2.574535
25% 24.254516
50% 34.832123
75% 40.224302
max 49.170737

@brenard-irap Thank you so so much for this, that's helped a lot!

Have a great day :)

Hi Luke, this is not so much speasy specific as it is a piece of code I wrote using speasy.

ace_mag contains all the information about the magnetic field measured by ace during a certain time period that I selected earlier in the code.

ace_mag.time will give you the time at which every "value" was measured.
ace_mag.values will give you the magnetic field (as a vector) at these times.
ace_mag.unit should be nano Teslas.

start_time = time.time() has nothing to do with speasy, and gets the clock time of your computer.
"IMF" means interplanetary magnetic field.
Bx = ace_mag.filter_colums(["imf[0]").values will give you an array containing the values of Bx at each of the above times.
By and Bz are the same.
print("--- %s seconds ---" % (time.time() - start_time)) prints the time it took to perform all the operations since you first defined start_time.

You can forget about the time.time() lines. I was simply trying to compare how long it would take to define the magnetic field as arrays using two different methods. The other one, which used "list comprehensions" (we can talk about this next Wednesday) is probably not in that piece of code anymore because it was much slower that the one above.

So at this point, you have arrays containing Bx, By, Bz.

The next few lines use... "list comprehensions" (probably not a great idea) to define arrays that contain the coordinates of the ACE satellite over time.

I can see how you would plot a 3D graph giving the trajectory of ACE based on these three lines. I am not sure what other 3D graph you may be referring to.

Was this the answer you were looking for?

Hi Clement. Thank you for the speedy reply!

I see now the 3D plot mapped the different positions of the Earth, moon and ACE satellite I think the other info just contains details about the plot aesthetics. It is a very cool plot!

This helps a lot thank you. I've started also to get the angles of Enceladus so hopefully I'm still on-track.

Many thanks

@Luke-Bicknell If you mean these products:
image
They are under this node spz.inventories.tree.amda.Parameters.Cassini.Ephemeris__Saturn_Moons___special.Enceladus in speasy. Speasy tries to keep the same names than AMDA as long as they are "Python compatible" or replace spaces and others forbidden characters by _.

@jeandet Thank you so much for the reply, much appreciated.

Yes so to put it more plainly I wanted to plug r and theta into the following equation/function:
image

I would say your code should work as long as you use numpy cos function and r, theta are numpy arrays.