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Last modified by makuadm on 2026-01-07 06:21
From version 15.1
edited by cpassow
on 2022-04-04 11:29
Change comment: There is no comment for this version
To version 52.1
edited by makuadm
on 2026-01-07 06:21
Change comment: Task [xwiki:FLASHUSER.Offline data analysis (DAQ).DEMO - Working with FLASH data.Tasks.Task_31.WebHome] has been updated!

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Title
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1 -DEMO - Documentation
1 +DEMO - Working with FLASH data
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1 +FLASHUSER.Offline data analysis (DAQ).WebHome
Author
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1 -XWiki.cpassow
1 +XWiki.makuadm
Content
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1 -(% style="list-style-type: square;" %)
2 -* Short descriptions including Links:
3 -(% style="list-style-type: square;" %)
4 -** GPFS
5 -** JupyterHub
6 -** conda ?
1 +Experimental data is recorded as HDF files[[~[link~]>>doc:FS-FLASH USER tmp.Data Acquisition and controls 1.DAQ and controls overview.Offline data analysis (DAQ).The FLASH HDF5 structure.WebHome]] on the GPFS file system[[~[link~]>>https://docs.desy.de/asap3/]]. The access rights are linked to the user's DESY account and can be managed by the PI via the GAMMA portal[[~[link~]>>url:https://gamma-portal.desy.de/||shape="rect"]]. The experimental data can be downloaded via the GAMMA portal, but it is advised to use the DESY computing infrastructure. Access point are via ssh, Maxwell-Display Server[[~[link~]>>url:https://confluence.desy.de/display/MXW/Maxwell+Cluster||shape="rect"]] or JuyterHub[[~[link~]>>url:https://confluence.desy.de/display/MXW/JupyterHub+on+Maxwell||shape="rect"]]. We recommend using the JupyterHub for data exploration and the SLURM resources for high performances computing - see FAB for easy usage of the infrastructure.
7 7  
8 -\\
3 +{{info title="How to login JupyterHub"}}
4 +=== ===
9 9  
10 -(% style="list-style-type: square;" %)
11 -* Links Repository
12 -(% style="list-style-type: square;" %)
13 -** including Method Description?
6 +{{view-file att--filename="tmp.mp4" height="150"/}}
7 +{{/info}}
14 14  
15 -\\
9 +{{info}}
10 +=== There are different options that help you to work with the FLASH HDF5 data in Python ===
16 16  
17 -(% style="list-style-type: square;" %)
18 -* Links to Binder
12 +* The currently developed option for large data sets: [[the FAB package>>url:https://hasfcpkg.desy.de/fab/fab.html||shape="rect"]] ... see below
13 +* and for smaller projects:  (% class="Object" %)[[https:~~/~~/gitlab.desy.de/christopher.passow/flash-daq-hdf>>url:https://gitlab.desy.de/christopher.passow/flash-daq-hdf||shape="rect"]]
19 19  
20 -\\
15 +(% class="Object" %)See also the collection of Demo data and sample scripts : [[doc:FS-FLASH USER tmp.Data Acquisition and controls 1.DAQ and controls overview.Offline data analysis (DAQ).Collection of HDF5 sample data from different beamlines.WebHome]]
16 +{{/info}}
21 21  
22 -(% style="list-style-type: square;" %)
23 -* Screencast
24 24  
25 -\\
19 +[[~[~[image:attach:image2023-9-29_11-1-37.png~]~]>>url:https://hasfcpkg.desy.de/fab/fab.html||shape="rect"]]
26 26  
27 -\\
21 +{{expand title="older ideas ..."}}
22 +(% class="Object" %)(object oriented) [[https:~~/~~/gitlab.desy.de/christopher.passow/fdh-builder>>url:https://gitlab.desy.de/christopher.passow/fdh-builder.git||shape="rect"]]
28 28  
29 -Questions:
24 +----
30 30  
31 -(% style="list-style-type: square;" %)
32 -* for whom
33 -* where Maxwell / local / extern
34 -* distribution\\
35 -(% style="list-style-type: square;" %)
36 -** channel?
37 -(% style="list-style-type: square;" %)
38 -*** where official hosted (DESY, privat, conda-forge)
39 -** enviroment?
40 -(% style="list-style-type: square;" %)
41 -*** via files / already created?
26 +=== TODO ===
42 42  
43 -\\
28 +(% class="task-list" %)
29 +(((
30 +{{task reference="/Tasks/Task_18" status="InProgress" completeDate="" createDate="2026-01-06" reporter=""}}
31 +Short descriptions including Links:   → as Text
32 +\\
33 +{{/task}}
34 +)))
44 44  
36 +
37 +(% class="task-list" %)
38 +(((
39 +{{task reference="/Tasks/Task_22" status="InProgress" completeDate="" createDate="2026-01-07" reporter=""}}
40 +distribution
41 +\\
42 +{{/task}}
43 +)))
44 +
45 +
46 +(% class="task-list" %)
47 +(((
48 +{{task reference="/Tasks/Task_25" status="InProgress" completeDate="" createDate="2026-01-07" reporter=""}}
49 +Documentation
50 +\\
51 +{{/task}}
52 +)))
53 +
54 +
55 +(% class="task-list" %)
56 +(((
57 +{{task reference="/Tasks/Task_27" status="InProgress" completeDate="" createDate="2026-01-07" reporter=""}}
58 +Binder
59 +\\
60 +{{/task}}
61 +)))
62 +
63 +
64 +(% class="task-list" %)
65 +(((
66 +{{task reference="/Tasks/Task_29" status="InProgress" completeDate="" createDate="2026-01-07" reporter=""}}
67 +Screencast
68 +\\
69 +{{/task}}
70 +)))
71 +
72 +
73 +
74 +----
75 +
76 +==== under review ====
77 +
78 +
45 45  {{code language="bash"}}
46 46  conda create -n flashh5 python=3.10 # 3.10 not necessary, but would prefer 3.8 or higher
47 47  source activate flashh5
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57 57  ## delete from: /home/$USER/.local/share/jupyter/kernels/
58 58  {{/code}}
59 59  
60 -\\
61 -
62 -{{code language="py" title="moved to repository?"}}
94 +{{code language="py" title="
95 +moved to repository?"}}
63 63  class RunDirectory:
64 64  
65 - def get_run_table(): # more or less information?
98 + def get_run_table(): # more or less information? RunComment | Number of Files | start & stop time ?
66 66   ...
67 67  
68 - def get_run(daq, run_number): # daq is not needed
101 + def get_run(daq, run_number): # daq is not needed!
102 +
69 69   ...
70 70  
71 71  
72 -class Run:
106 +class Run: # constructor optional without RunDirectory or use there self.path
73 73  
74 74   def get_files():
75 75   ...
76 76  
77 - def get_channels(): # of file #1?
111 + def get_channels(): # of file #1
78 78   ...
79 79  
80 80   def get_start_time(): # better as attribute?
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82 82  
83 83   def get_stop_time(): # which? | better as attribute?
84 84   ...
85 -
86 -### for following methods to restrict number of files or separate method
87 -### e.g. create_df(files)
88 88  
89 - def to_df(daq_map):
120 + def to_df(daq_map): # to_df(daq_map, slice) slice=[0:4] -> throw Exception
90 90   ...
91 91  
92 92   def to_series(channel):
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101 101  run.to_series(daq_adr or daq_map) # on channel only?
102 102  run.to_array(daq_adr) # on channel only?
103 103  
104 -## is this interesting?
135 +## interesting?
105 105  # run.to_dask(daq_map)
106 106  # run.to_xarray(daq_map)
107 107  {{/code}}
108 108  
109 -\\
140 +
141 +
142 +{{/expand}}
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