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Wiki source code of DEMO - Working with FLASH data

Last modified by makuadm on 2026-01-07 06:21
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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.
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3 {{info title="How to login JupyterHub"}}
4 === ===
5
6 {{view-file att--filename="tmp.mp4" height="150"/}}
7 {{/info}}
8
9 {{info}}
10 === There are different options that help you to work with the FLASH HDF5 data in Python ===
11
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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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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19 [[~[~[image:attach:image2023-9-29_11-1-37.png~]~]>>url:https://hasfcpkg.desy.de/fab/fab.html||shape="rect"]]
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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"]]
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24 ----
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26 === TODO ===
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28 (% class="task-list" %)
29 (((
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31 Short descriptions including Links:   → as Text
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40 distribution
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48 {{task reference="/Tasks/Task_25" status="InProgress" completeDate="" createDate="2026-01-07" reporter=""}}
49 Documentation
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58 Binder
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66 {{task reference="/Tasks/Task_29" status="InProgress" completeDate="" createDate="2026-01-07" reporter=""}}
67 Screencast
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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?"}}
96 class RunDirectory:
97
98 def get_run_table(): # more or less information? RunComment | Number of Files | start & stop time ?
99 ...
100
101 def get_run(daq, run_number): # daq is not needed!
102
103 ...
104
105
106 class Run: # constructor optional without RunDirectory or use there self.path
107
108 def get_files():
109 ...
110
111 def get_channels(): # of file #1
112 ...
113
114 def get_start_time(): # better as attribute?
115 ...
116
117 def get_stop_time(): # which? | better as attribute?
118 ...
119
120 def to_df(daq_map): # to_df(daq_map, slice) slice=[0:4] -> throw Exception
121 ...
122
123 def to_series(channel):
124 ...
125
126 def to_array(channel):
127 ...
128 {{/code}}
129
130 {{code language="py" title="ideas"}}
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?
134
135 ## interesting?
136 # run.to_dask(daq_map)
137 # run.to_xarray(daq_map)
138 {{/code}}
139
140
141
142 {{/expand}}