ConfigurationsForCasualUsers

The intention of this page is to provide a few simple configurations that new or casual users can use in order to test the operation of a small MiniDAQ system that uses emulators instead of real electronics.

After you have successfully followed the steps described in Instructions for casual or first-time users, we will focus now on a few variations to the default configuration used in those instructions.

After you have setup the environment and downloaded the fake input data file

1. cd <work_dir>

2. source /cvmfs/dunedaq.opensciencegrid.org/setup_dunedaq.sh

3. setup_dbt dunedaq-v2.8.0

4. dbt-workarea-env

5. download a raw data file, either by running "curl -o frames.bin -O https://cernbox.cern.ch/index.php/s/7qNnuxD8igDOVJT/download" or clicking on the CERNBox link) and put it into <work_dir>

Now we generate some sample system configurations and use nanorc to run MiniDAQ app with them. The tools to generate these configurations consist of a single Python script that generates MiniDAQ system configurations with different characteristics based on command-line parameters that are given to the script. This script is minidaqapp/python/minidaqapp/nanorc/mdapp_multiru_gen.py. The config_gen files under python/minidaqapp/nanorc directory were developed to work with nanorc package, which itself can be seen as a basic Finite State Machine that sends commands and drives the MiniDAQ app.

The created configurations will be called mdapp_fake and there will be a mdapp_fake directory created containing the produced configuration to be used with nanorc. The configurations can be run interactively with nanorc mdapp_fake from the <work_dir>.

1) In order to get the full set of configuration options and their help , run :
python -m minidaqapp.nanorc.mdapp_multiru_gen -h

2) The data Input and Output system configuration options allow the user to change the input data file location and the output data directory path as needed. To specify an input frames.bin file from the current directory, a user would use -d ./frames.bin. This file contains data frames that are replayed by fake cards in the current system, and as mentioned above, this file can be downloaded with "curl -o frames.bin -O https://cernbox.cern.ch/index.php/s/7qNnuxD8igDOVJT/download". The output data path option -o can be used to specify the directory where output data files are written. To write the output data files to the current directory, one would use -o . The Input and Output data system options can be used in the following way

python -m minidaqapp.nanorc.mdapp_multiru_gen -d ./frames.bin -o . mdapp_fake

3) The default trigger rate generated is 1 Hz per readout unit (RU). This can be changed with the option --trigger-rate-hz FLOAT (default 1.0 Hz), or alternatively with the option --hsi-event-period FLOAT (default value 1e9, provides 1.0 Hz). For example to increase the trigger rate generated to 2.0 Hz, the user can run with either of these two options:

python -m minidaqapp.nanorc.mdapp_multiru_gen -d ./frames.bin -o . --trigger-rate-hz 2.0 mdapp_fake

python -m minidaqapp.nanorc.mdapp_multiru_gen -d ./frames.bin -o . --hsi-event-period 500000000.0 mdapp_fake

4) Use option -s INTEGER to slow down the generated data rate by a factor of INTEGER, this is achieved by slowing down the simulated clock speed for generating data. This option is particularly useful when the user is running the system on a slow computer that can't create data fast enough to match what the real electronics can do. For example to slowdown the data production rate by a factor of 10, run the following commmand:

python -m minidaqapp.nanorc.mdapp_multiru_gen -d ./frames.bin -o . -s 10 mdapp_fake

5) Use option -r FLOAT to specify the run number, for example with run 111 :

python -m minidaqapp.nanorc.mdapp_multiru_gen -d ./frames.bin -o . -r 111 mdapp_fake

6) Use option -n INTEGER to specify the number of data producers (links) per RU (<10) or total. Since the maximum number of data producers per readout unit is 10, values for this parameter that are larger than 10 are interpreted to indicate the total number of data producers instead of the number per RU. When the total number of data producers are specified, they are spread equally among the RUs, as much as possible. Here is an example command specifying 4 data producers (per RU) :

python -m minidaqapp.nanorc.mdapp_multiru_gen -d ./frames.bin -o . -n 4 mdapp_fake

7) Use options --host-df TEXT , --host-ru TEXT , --host-trigger TEXT , --host-hsi TEXT to specify different hosts for the different applications (processes): host Data-Flow (host-df) host Readout Unit (host-ru) this is a repeatable option adding an additional RU process each time host Trigger app (host-trigger) host HSI app (--host-hsi)

for example using the following fake IP addresses for the different hosts : 127.0.0.1 , 127.0.0.2 , 127.0.0.3 , 127.0.0.4

python -m minidaqapp.nanorc.mdapp_multiru_gen -d ./frames.bin -o . --host-df 127.0.0.1 --host-ru 127.0.0.2 --host-trigger 127.0.0.3 --host-hsi 127.0.0.4 mdapp_fake

the default for all the host options will be localhost

8) Running nanorc can be done in interactively or in batch mode, for the later you can specify a sequence of commands to drive MiniDAQ app, for example run :

nanorc mdapp_fake boot init conf start 102 wait 2 resume wait 60 pause wait 2 stop scrap terminate

Where the start <run_number> command overrides the run_number value to be used. Any meaningful combination of commands is allowed. Note that the triggers will be issue only after the resume command is sent.

9) examine the contents of the HDf5 file with commands like the following: * h5dump-shared -H -A swtest_run000101_0000_*.hdf5 * and * python3 $DFMODULES_FQ_DIR/dfmodules/bin/hdf5dump/hdf5_dump.py -p both -f swtest_run000101_0000_*.hdf5

---

Last git commit to the markdown source of this page:

Author: Kurt Biery

Date: Fri Aug 27 09:52:26 2021 -0500

If you see a problem with the documentation on this page, please file an Issue at https://github.com/DUNE-DAQ/minidaqapp/issues