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Last modified by makuadm on 2026-01-07 06:21
From version 35.1
edited by sndueste
on 2023-09-28 16:54
Change comment: There is no comment for this version
To version 42.3
edited by rangeadm
on 2025-02-21 11:47
Change comment: Update document after refactoring.

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1 +FS-FLASH USER tmp.Offline data analysis (DAQ).WebHome
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1 -Experimental data is recorded as HDF files[link] on the GPFS file system[link]. The access rights[link to ACLs] are linked to the user's DESY account and can be managed by the PI via the GAMMA portal[link]. 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[link], Maxwell-Display Server[link] or JuyterHub[link]. We recommmend using the JupyterHub for data exploration and the SLURM resources[link] for high performances computing.
1 +Experimental data is recorded as HDF files[[~[link~]>>doc:FLASHUSER.Data Acquisition and controls.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.
2 2  
3 -For simplified acccess we provide a conda module flashh5[link] which can be installed in a personal conda environment[link] on the Maxwell Cluster. Example on the usage can be found here [link - repo + binder]
3 +{{info title="How to login JupyterHub"}}
4 +=== ===
4 4  
5 -\\
6 +{{view-file att--filename="tmp.mp4" height="150"/}}
7 +{{/info}}
6 6  
7 -{{expand title="How to login JupyterHub"}}
8 -
9 -
10 -{{view-file att--filename="tmp.mp4" height="250"/}}
11 -{{/expand}}
12 -
13 -\\
14 -
15 15  {{info}}
16 -=== Reference implementation (Python) ===
10 +=== There are different options that help you to work with the FLASH HDF5 data in Python ===
17 17  
18 -(imperative)  (% class="Object" %)[[https:~~/~~/gitlab.desy.de/christopher.passow/flash-daq-hdf>>url:https://gitlab.desy.de/christopher.passow/flash-daq-hdf||shape="rect"]]
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 -(% class="Object" %)(object oriented) [[https:~~/~~/gitlab.desy.de/christopher.passow/fdh-builder>>url:https://gitlab.desy.de/christopher.passow/fdh-builder.git||shape="rect"]]
21 -
22 -\\
15 +(% class="Object" %)See also the collection of Demo data and sample scripts : [[doc:FLASHUSER.Data Acquisition and controls.DAQ and controls overview.Offline data analysis (DAQ).Collection of HDF5 sample data from different beamlines.WebHome]]
23 23  {{/info}}
24 24  
25 -link to Repos and Fab
26 26  
27 -\\
19 +[[~[~[image:attach:image2023-9-29_11-1-37.png~]~]>>url:https://hasfcpkg.desy.de/fab/fab.html||shape="rect"]]
28 28  
29 -\\
30 -
31 -\\
32 -
33 33  {{expand title="older ideas ..."}}
34 -\\
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"]]
35 35  
36 36  ----
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40 40  (% class="task-list" %)
41 41  (((
42 42  {{task reference="/Tasks/Task_18" status="InProgress"}}
43 -Short descriptions including Links:   → as Text\\
31 +Short descriptions including Links:   → as Text
44 44  
45 45  (% class="task-list" %)
46 46  (((
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77 77  {{/task}}
78 78  )))
79 79  
80 -\\
81 81  
82 82  (% class="task-list" %)
83 83  (((
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99 99  {{/task}}
100 100  )))
101 101  
102 -\\
103 103  
104 104  (% class="task-list" %)
105 105  (((
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117 117  {{/task}}
118 118  )))
119 119  
120 -\\
121 121  
122 122  (% class="task-list" %)
123 123  (((
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135 135  {{/task}}
136 136  )))
137 137  
138 -\\
139 139  
140 140  (% class="task-list" %)
141 141  (((
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169 169  {{/task}}
170 170  )))
171 171  
172 -\\
173 173  
174 -\\
175 175  
176 176  ----
177 177  
178 178  ==== under review ====
179 179  
180 -\\
181 181  
182 182  {{code language="bash"}}
183 183  conda create -n flashh5 python=3.10 # 3.10 not necessary, but would prefer 3.8 or higher
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194 194  ## delete from: /home/$USER/.local/share/jupyter/kernels/
195 195  {{/code}}
196 196  
197 -\\
198 -
199 -{{code language="py" title="moved to repository?"}}
178 +{{code language="py" title="
179 +moved to repository?"}}
200 200  class RunDirectory:
201 201  
202 202   def get_run_table(): # more or less information? RunComment | Number of Files | start & stop time ?
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241 241  # run.to_xarray(daq_map)
242 242  {{/code}}
243 243  
244 -\\
245 245  
246 -\\
225 +
247 247  {{/expand}}
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