Skip to Content

Wiki source code of DEMO - Working with FLASH data

Version 36.1 by sndueste on 2023-09-29 11:07
Show last authors
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.
2
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]
4
5 \\
6
7 {{expand title="How to login JupyterHub"}}
8
9
10 {{view-file att--filename="tmp.mp4" height="250"/}}
11 {{/expand}}
12
13 \\
14
15 \\
16
17 {{info}}
18 === There are different options that help you to work with the FLASH HDF5 data in Python ===
19
20 The currently developed option for large data sets: [[the FAB package>>url:https://hasfcpkg.desy.de/fab/fab.html||shape="rect"]] ... see below
21
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"]]
23 {{/info}}
24
25 \\
26
27 [[~[~[image:attach:image2023-9-29_11-1-37.png~]~]>>url:https://hasfcpkg.desy.de/fab/fab.html||shape="rect"]]
28
29 \\
30
31 {{expand title="older ideas ..."}}
32 (% class="Object" %)(object oriented) [[https:~~/~~/gitlab.desy.de/christopher.passow/fdh-builder>>url:https://gitlab.desy.de/christopher.passow/fdh-builder.git||shape="rect"]]
33
34 ----
35
36 === TODO ===
37
38 (% class="task-list" %)
39 (((
40 {{task reference="/Tasks/Task_18" status="InProgress"}}
41 Short descriptions including Links:   → as Text\\
42
43 (% class="task-list" %)
44 (((
45
46
47 {{task reference="/Tasks/Task_19" status="InProgress"}}
48 GPFS
49 {{/task}}
50
51 {{task reference="/Tasks/Task_40" status="InProgress"}}
52 Access rights
53 {{/task}}
54
55 {{task reference="/Tasks/Task_41" status="InProgress"}}
56 Gamma Portal
57 {{/task}}
58
59 {{task reference="/Tasks/Task_42" status="InProgress"}}
60 Maxwell
61 {{/task}}
62
63 {{task reference="/Tasks/Task_20" status="InProgress"}}
64 JupyterHub
65 {{/task}}
66
67 {{task reference="/Tasks/Task_21" status="InProgress"}}
68 conda ?
69 {{/task}}
70
71 {{task reference="/Tasks/Task_36" status="InProgress"}}
72 explain install from channel instead of fixed environment, but can use environment file from example repository
73 {{/task}}
74 )))
75 {{/task}}
76 )))
77
78 \\
79
80 (% class="task-list" %)
81 (((
82 {{task reference="/Tasks/Task_22" status="InProgress"}}
83 distribution
84
85 (% class="task-list" %)
86 (((
87
88
89 {{task reference="/Tasks/Task_23" status="InProgress"}}
90 channel  (where to host?)
91 {{/task}}
92
93 {{task reference="/Tasks/Task_24" status="InProgress"}}
94 environment file (repository with examples)
95 {{/task}}
96 )))
97 {{/task}}
98 )))
99
100 \\
101
102 (% class="task-list" %)
103 (((
104 {{task reference="/Tasks/Task_25" status="InProgress"}}
105 Documentation
106
107 (% class="task-list" %)
108 (((
109
110
111 {{task reference="/Tasks/Task_26" status="InProgress"}}
112 here VS repository VS sphinx
113 {{/task}}
114 )))
115 {{/task}}
116 )))
117
118 \\
119
120 (% class="task-list" %)
121 (((
122 {{task reference="/Tasks/Task_27" status="InProgress"}}
123 Binder
124
125 (% class="task-list" %)
126 (((
127
128
129 {{task reference="/Tasks/Task_28" status="InProgress"}}
130 examples with Stefan
131 {{/task}}
132 )))
133 {{/task}}
134 )))
135
136 \\
137
138 (% class="task-list" %)
139 (((
140 {{task reference="/Tasks/Task_29" status="InProgress"}}
141 Screencast
142
143 (% class="task-list" %)
144 (((
145
146
147 {{task reference="/Tasks/Task_30" status="InProgress"}}
148 login in jupyter hub
149 {{/task}}
150
151 {{task reference="/Tasks/Task_31" status="InProgress"}}
152 login maxwell display
153 {{/task}}
154
155 {{task reference="/Tasks/Task_32" status="InProgress"}}
156 use slix
157 {{/task}}
158
159 {{task reference="/Tasks/Task_33" status="InProgress"}}
160 use hdfview plugin in jupterLab
161 {{/task}}
162
163 {{task reference="/Tasks/Task_34" status="InProgress"}}
164 create conda env with flashh5
165 {{/task}}
166 )))
167 {{/task}}
168 )))
169
170 \\
171
172 \\
173
174 ----
175
176 ==== under review ====
177
178 \\
179
180 {{code language="bash"}}
181 conda create -n flashh5 python=3.10 # 3.10 not necessary, but would prefer 3.8 or higher
182 source activate flashh5
183 conda install ipython numpy pandas #TODO: fix dependcies
184 conda install -c https://www.desy.de/~cpassow/condarepo/ flashh5
185
186 ## on jhub
187 conda install ipykernel
188 python -m ipykernel install --user --name flashh5 --display-name "flashh5"
189
190
191 ## to remove on jhub
192 ## delete from: /home/$USER/.local/share/jupyter/kernels/
193 {{/code}}
194
195 \\
196
197 {{code language="py" title="moved to repository?"}}
198 class RunDirectory:
199
200 def get_run_table(): # more or less information? RunComment | Number of Files | start & stop time ?
201 ...
202
203 def get_run(daq, run_number): # daq is not needed!
204
205 ...
206
207
208 class Run: # constructor optional without RunDirectory or use there self.path
209
210 def get_files():
211 ...
212
213 def get_channels(): # of file #1
214 ...
215
216 def get_start_time(): # better as attribute?
217 ...
218
219 def get_stop_time(): # which? | better as attribute?
220 ...
221
222 def to_df(daq_map): # to_df(daq_map, slice) slice=[0:4] -> throw Exception
223 ...
224
225 def to_series(channel):
226 ...
227
228 def to_array(channel):
229 ...
230 {{/code}}
231
232 {{code language="py" title="ideas"}}
233 run.to_df(daq_map)
234 run.to_series(daq_adr or daq_map) # on channel only?
235 run.to_array(daq_adr) # on channel only?
236
237 ## interesting?
238 # run.to_dask(daq_map)
239 # run.to_xarray(daq_map)
240 {{/code}}
241
242 \\
243
244 \\
245 {{/expand}}