Wiki source code of DEMO - Working with FLASH data

Version 41.2 by sndueste on 2025/02/05 11:21
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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.
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3 {{info title="How to login JupyterHub"}}
4 === ===
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6 {{view-file att--filename="tmp.mp4" height="150"/}}
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11 {{info}}
12 === 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"]]
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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]]
18 {{/info}}
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22 [[~[~[image:attach:image2023-9-29_11-1-37.png~]~]>>url:https://hasfcpkg.desy.de/fab/fab.html||shape="rect"]]
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26 {{expand title="older ideas ..."}}
27 (% 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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29 ----
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31 === TODO ===
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36 Short descriptions including Links:   → as Text\\
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43 GPFS
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47 Access rights
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51 Gamma Portal
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55 Maxwell
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59 JupyterHub
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63 conda ?
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67 explain install from channel instead of fixed environment, but can use environment file from example repository
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85 channel  (where to host?)
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89 environment file (repository with examples)
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100 Documentation
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151 use slix
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155 use hdfview plugin in jupterLab
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159 create conda env with flashh5
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169 ----
170
171 ==== under review ====
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175 {{code language="bash"}}
176 conda create -n flashh5 python=3.10 # 3.10 not necessary, but would prefer 3.8 or higher
177 source activate flashh5
178 conda install ipython numpy pandas #TODO: fix dependcies
179 conda install -c https://www.desy.de/~cpassow/condarepo/ flashh5
180
181 ## on jhub
182 conda install ipykernel
183 python -m ipykernel install --user --name flashh5 --display-name "flashh5"
184
185
186 ## to remove on jhub
187 ## delete from: /home/$USER/.local/share/jupyter/kernels/
188 {{/code}}
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192 {{code language="py" title="moved to repository?"}}
193 class RunDirectory:
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195 def get_run_table(): # more or less information? RunComment | Number of Files | start & stop time ?
196 ...
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198 def get_run(daq, run_number): # daq is not needed!
199
200 ...
201
202
203 class Run: # constructor optional without RunDirectory or use there self.path
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205 def get_files():
206 ...
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208 def get_channels(): # of file #1
209 ...
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211 def get_start_time(): # better as attribute?
212 ...
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214 def get_stop_time(): # which? | better as attribute?
215 ...
216
217 def to_df(daq_map): # to_df(daq_map, slice) slice=[0:4] -> throw Exception
218 ...
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220 def to_series(channel):
221 ...
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223 def to_array(channel):
224 ...
225 {{/code}}
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227 {{code language="py" title="ideas"}}
228 run.to_df(daq_map)
229 run.to_series(daq_adr or daq_map) # on channel only?
230 run.to_array(daq_adr) # on channel only?
231
232 ## interesting?
233 # run.to_dask(daq_map)
234 # run.to_xarray(daq_map)
235 {{/code}}
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240 {{/expand}}