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Last modified by makuadm on 2026-01-07 06:21
From version 32.1
edited by sndueste
on 2022-10-27 11:19
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To version 41.3
edited by sndueste
on 2025-02-05 14:41
Change comment: There is no comment for this version

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1 +FLASHUSER.Data Acquisition and controls.DAQ and controls overview.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~]>>doc:ASAP3.ASAP3 Data Storage for PETRA III]]. 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}}
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7 -{{expand title="TEST - 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:FLASHUSER.Data Acquisition and controls.DAQ and controls overview.Offline data analysis (DAQ).Collection of HDF5 sample data from different beamlines.WebHome]]
16 +{{/info}}
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19 +[[~[~[image:attach:image2023-9-29_11-1-37.png~]~]>>url:https://hasfcpkg.desy.de/fab/fab.html||shape="rect"]]
16 16  
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"]]
23 +
17 17  ----
18 18  
19 19  === TODO ===
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21 21  (% class="task-list" %)
22 22  (((
23 23  {{task reference="/Tasks/Task_18" status="InProgress"}}
24 -Short descriptions including Links:   → as Text\\
31 +Short descriptions including Links:   → as Text
25 25  
26 26  (% class="task-list" %)
27 27  (((
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58 58  {{/task}}
59 59  )))
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61 -\\
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63 63  (% class="task-list" %)
64 64  (((
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80 80  {{/task}}
81 81  )))
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84 84  
85 85  (% class="task-list" %)
86 86  (((
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98 98  {{/task}}
99 99  )))
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103 103  (% class="task-list" %)
104 104  (((
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116 116  {{/task}}
117 117  )))
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121 121  (% class="task-list" %)
122 122  (((
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150 150  {{/task}}
151 151  )))
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153 -\\
154 154  
155 -\\
156 156  
157 157  ----
158 158  
159 159  ==== under review ====
160 160  
161 -\\
162 162  
163 163  {{code language="bash"}}
164 164  conda create -n flashh5 python=3.10 # 3.10 not necessary, but would prefer 3.8 or higher
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175 175  ## delete from: /home/$USER/.local/share/jupyter/kernels/
176 176  {{/code}}
177 177  
178 -\\
179 -
180 -{{code language="py" title="moved to repository?"}}
178 +{{code language="py" title="
179 +moved to repository?"}}
181 181  class RunDirectory:
182 182  
183 183   def get_run_table(): # more or less information? RunComment | Number of Files | start & stop time ?
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222 222  # run.to_xarray(daq_map)
223 223  {{/code}}
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226 +{{/expand}}
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