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Last modified by makuadm on 2026-01-07 06:21
From version 41.1
edited by sndueste
on 2025-02-05 11:21
Change comment: Renamed back-links.
To version 36.1
edited by sndueste
on 2023-09-29 11:07
Change comment: There is no comment for this version

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1 -Experimental data is recorded as HDF files[[~[link~]>>doc:FLASHUSER.Data Acquisition and controls.Data Access at FLASH (DAQ, gpfs,\.\.\.).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.
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.
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3 -{{info title="How to login JupyterHub"}}
4 -=== ===
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]
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11 11  {{info}}
12 -=== There are different options that help you to work with the FLASH HDF5 data in Python ===
18 +=== There are different options that help you to work with the FLASH HDF5 data in Python ===
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14 -* The currently developed option for large data sets: [[the FAB package>>url:https://hasfcpkg.desy.de/fab/fab.html||shape="rect"]] ... see below
15 -* 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"]]
20 +The currently developed option for large data sets: [[the FAB package>>url:https://hasfcpkg.desy.de/fab/fab.html||shape="rect"]] ... see below
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17 -(% 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]]
22 +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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