Recreate example data sets

[arktur85]

Hi,

first of all thanks for sharing your code!

To get familiar with it, I'm trying to rebuild the test cases located in the Examples directory. Could you please tell me step by step how I have to proceed to extract the clusters from the outputted list of reachability distances using the Python script located in "Gradient-clustering", e.g., to reproduce GaussianSevenClustersWithBackground/DataSet.pdf? I must admit that I'm not very experienced with using Python yet ...

Is this version already capable of processing a list with data uncertainties included, say
object-ID x y z stddev_x stddev_y stddev_z ?

Thank you in advance for your help and keep up the great work!

Cheers,
arktur

[AndrewStSp]

Hi,

next 4 weeks are heavy on conferences and with some 10 days of holidays in between. I will do my best to create a "test suite" with a step-by-step explanation on how to run as soon as possible.

Andrew.

[arktur85]

Hi Andrew,

next 4 weeks are heavy on conferences and with some 10 days of holidays in between. I will do my best to create a "test suite" with a step-by-step explanation on how to run as soon as possible.

that sounds great! Thank you in advance for your effort. Best regards Michael

…

--- Dr. Michael M. Schulreich Department of Astronomy and Astrophysics Berlin Institute of Technology Hardenbergstr. 36 10623 Berlin, Germany Phone: +49 30 31422093 Email: [email protected] Web: https://astro.physik.tu-berlin.de/~schulreich

[arktur85]

Hi Andrew,

how is your work going?
Would you mind to post the commands (incl. the parameters used) for generating the outputs/plots, e.g., of the GaussianSevenClustersWithBackground example? Such a quick demo would be really helpful for the project I'm working on.

Best regards,
Michael

[AndrewStSp]

Hi Michael,

Jeroen Audenaert, a PhD student of mine and a developer of the cluster extraction algorithm, will guide you trough the process next week.

Andrew.

[arktur85]

Hi Andrew, wow, that would be great! Thanks! Michael.

…

On 27. Feb 2020, at 18:51, AndrewStSp ***@***.***> wrote: Hi Michael, Jeroen Audenaert, a PhD student of mine and a developer of the cluster extraction algorithm, will guide you trough the process next week. Andrew. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#3?email_source=notifications&email_token=AMULJI4PPA7TXR5FQ5L75ETRE74RLA5CNFSM4IEOTSFKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOENFJQ5A#issuecomment-592091252>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AMULJI446DWIV7IHP3GP2LLRE74RLANCNFSM4IEOTSFA>.

[jeraud]

Hi Michael,

1. The first step is to compile the code, if not done yet you can find how to do that here: https://github.com/IvS-KULeuven/FOPTICS/blob/master/FOPTICS/Sources/README.md
2. The second step is to create a file which describes the path to the input file (let's call this file foptics_list). The only thing that needs to be in this file is thus the path. E.g. /home/Documents/Data/ArtificialData_Background7ClustersSet.dat.
3. The third step is to run the command /foptics arg1 arg2 arg3 arg4 arg5 for which the arguments are also specified in the readme file. So, this would for example then be ./foptics foptics_list outputfile.txt 2 1 20
   -> The 2 comes from the fact that we are using two features in the gaussian demo (col 2 and 3 in the input file)
   -> The 1 is just the value of undefined distance and can be any large number
   -> The 20 is the the minimum number of objects in the neighborhood. Usually around 20 or 30 is ok, unless you have a really large dataset and don't want to see the smaller substructures. In that case it should be increased.

This should give you the reachability diagram without the clusters being indicated. The clusters are just the valleys in the diagram.

Let me know if this works, then I can also help you with using the cluster extraction algorithm.

Jeroen