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Last modified by makuadm on 2026-01-07 06:21
From version 34.1
edited by sndueste
on 2023-09-28 16:52
Change comment: There is no comment for this version
To version 42.4
edited by sndueste
on 2025-04-23 14:40
Change comment: Renamed back-links.

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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.
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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"}}
9 +{{info}}
10 +=== There are different options that help you to work with the FLASH HDF5 data in Python ===
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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"]]
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10 -{{view-file att--filename="tmp.mp4" height="250"/}}
11 -{{/expand}}
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}}
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13 -\\
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15 -link to Repos and Fab
19 +[[~[~[image:attach:image2023-9-29_11-1-37.png~]~]>>url:https://hasfcpkg.desy.de/fab/fab.html||shape="rect"]]
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22 -
23 23  {{expand title="older ideas ..."}}
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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"]]
25 25  
26 26  ----
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30 30  (% class="task-list" %)
31 31  (((
32 32  {{task reference="/Tasks/Task_18" status="InProgress"}}
33 -Short descriptions including Links:   → as Text\\
31 +Short descriptions including Links:   → as Text
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35 35  (% class="task-list" %)
36 36  (((
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67 67  {{/task}}
68 68  )))
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72 72  (% class="task-list" %)
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89 89  {{/task}}
90 90  )))
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94 94  (% class="task-list" %)
95 95  (((
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107 107  {{/task}}
108 108  )))
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112 112  (% class="task-list" %)
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125 125  {{/task}}
126 126  )))
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130 130  (% class="task-list" %)
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159 159  {{/task}}
160 160  )))
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162 -\\
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164 -\\
165 165  
166 166  ----
167 167  
168 168  ==== under review ====
169 169  
170 -\\
171 171  
172 172  {{code language="bash"}}
173 173  conda create -n flashh5 python=3.10 # 3.10 not necessary, but would prefer 3.8 or higher
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184 184  ## delete from: /home/$USER/.local/share/jupyter/kernels/
185 185  {{/code}}
186 186  
187 -\\
188 -
189 -{{code language="py" title="moved to repository?"}}
178 +{{code language="py" title="
179 +moved to repository?"}}
190 190  class RunDirectory:
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192 192   def get_run_table(): # more or less information? RunComment | Number of Files | start & stop time ?
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231 231  # run.to_xarray(daq_map)
232 232  {{/code}}
233 233  
234 -\\
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225 +
237 237  {{/expand}}
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