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Last modified by makuadm on 2026-01-07 06:21
From version 7.1
edited by cpassow
on 2022-04-04 09:12
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 -(% style="list-style-type: square;" %)
9 -* Links Repository
10 -(% style="list-style-type: square;" %)
11 -** including Method Description
3 +{{info title="How to login JupyterHub"}}
4 +=== ===
12 12  
13 -\\
6 +{{view-file att--filename="tmp.mp4" height="150"/}}
7 +{{/info}}
14 14  
15 -(% style="list-style-type: square;" %)
16 -* Links to Binder
9 +{{info}}
10 +=== There are different options that help you to work with the FLASH HDF5 data in Python ===
17 17  
18 -\\
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 -Questions:
23 23  
24 -(% style="list-style-type: square;" %)
25 -* for whom
26 -* where Maxwell / local / extern
27 -* distribution\\
28 -(% style="list-style-type: square;" %)
29 -** channel?
30 -(% style="list-style-type: square;" %)
31 -*** where official hosted (DESY, privat, conda-forge)
32 -** enviroment?
33 -(% style="list-style-type: square;" %)
34 -*** via files / already created?
19 +[[~[~[image:attach:image2023-9-29_11-1-37.png~]~]>>url:https://hasfcpkg.desy.de/fab/fab.html||shape="rect"]]
35 35  
36 -\\
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"]]
37 37  
38 -{{code language="py" title="moved to repository?"}}
24 +----
25 +
26 +=== TODO ===
27 +
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 +)))
35 +
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 +
79 +{{code language="bash"}}
80 +conda create -n flashh5 python=3.10 # 3.10 not necessary, but would prefer 3.8 or higher
81 +source activate flashh5
82 +conda install ipython numpy pandas #TODO: fix dependcies
83 +conda install -c https://www.desy.de/~cpassow/condarepo/ flashh5
84 +
85 +## on jhub
86 +conda install ipykernel
87 +python -m ipykernel install --user --name flashh5 --display-name "flashh5"
88 +
89 +
90 +## to remove on jhub
91 +## delete from: /home/$USER/.local/share/jupyter/kernels/
92 +{{/code}}
93 +
94 +{{code language="py" title="
95 +moved to repository?"}}
39 39  class RunDirectory:
40 40  
41 - def get_run_table():
98 + def get_run_table(): # more or less information? RunComment | Number of Files | start & stop time ?
42 42   ...
43 43  
44 - def get_run(daq, run_number):
101 + def get_run(daq, run_number): # daq is not needed!
102 +
45 45   ...
46 46  
47 47  
48 -class Run:
106 +class Run: # constructor optional without RunDirectory or use there self.path
49 49  
50 50   def get_files():
51 51   ...
52 52  
53 - def get_channels(): # of file #1?
111 + def get_channels(): # of file #1
54 54   ...
55 55  
56 - def get_start_time():
114 + def get_start_time(): # better as attribute?
57 57   ...
58 58  
59 - def get_stop_time(): # which?
117 + def get_stop_time(): # which? | better as attribute?
60 60   ...
61 -
62 - def to_df(channel_map):
119 +
120 + def to_df(daq_map): # to_df(daq_map, slice) slice=[0:4] -> throw Exception
63 63   ...
64 64  
65 65   def to_series(channel):
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70 70  {{/code}}
71 71  
72 72  {{code language="py" title="ideas"}}
73 -run.to_df(channel_map)
74 -run.to_series(channel_string or channel_map) # on channel only?
75 -run.to_array(channel_string) # on channel only?
131 +run.to_df(daq_map)
132 +run.to_series(daq_adr or daq_map) # on channel only?
133 +run.to_array(daq_adr) # on channel only?
76 76  
77 -# run.to_dask(dict)
78 -# run.to_xarray(dict)
135 +## interesting?
136 +# run.to_dask(daq_map)
137 +# run.to_xarray(daq_map)
79 79  {{/code}}
80 80  
81 -\\
140 +
141 +
142 +{{/expand}}
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