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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 demo 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.

First, a reminder to set up your working software environment and download the fake input data file

  1. cd <work_dir>

  2. source ./dbt-env.sh

  3. dbt-workarea-env

  4. curl -o frames.bin -O https://cernbox.cern.ch/index.php/s/0XzhExSIMQJUsp0/download

Next we generate some sample system configurations and use nanorc to run a demo system with them.

The tools to generate these configurations consist of a single Python script that generates DAQ system configurations with different characteristics based on the configuration file given to the script. This script is daqconf/scripts/fddaqconf_gen. It uses daqconf/schema/daqconf/confgen.jsonnet to define the format for configuration JSON files. The configuration generation files under the daqconf/python/daqconf/apps directory were developed to work with the nanorc package, which itself can be seen as a basic Finite State Machine that sends commands and drives the DAQ system.

Here is an example command line which uses the provided JSON file that has all of the default values populated (so it is equivalent to running without any options at all!). Note for the reader that it scrolls horizontally. The command below assumes you also have a hardware map file available, e.g. this basic example. Further details on hardware map files can be found at the bottom of this page. 

fddaqconf_gen --hardware-map-file <your hardware map file> --config daqconf/config/daqconf_full_config.json daq_fake00
The created configurations will be called daq_fake<NN> and there will be a daq_fake<NN> directory created containing the produced configuration to be used with nanorc. The configurations can be run interactively with nanorc daq_fake<NN> <partition_name> from the <work_dir>.

In the following sections, we will use "dot" notation to indicate JSON paths, so that readout.data_file $PWD/frames.bin is equvalent to "readout": { "data_file": "$PWD/frames.bin" }.

1) In order to get the set of configuration options that can be overridden from the command line and their help , run : fddaqconf_gen -h Command-line options override any options set in the configuration file, which in turn override any default values.

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 readout.data_file $PWD/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 dataflow.apps[n].output_dirs can be used to specify the directory where output data files are written. If more than one path is provided in the array, the DataWriter will use those directories in rotation for writting TriggerRecords.

3) The default trigger rate generated is 1 Hz per readout unit (RU). This can be changed with the option trigger.trigger_rate_hz FLOAT (default 1.0 Hz).

4) Use option readout.data_rate_slowdown_factor 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.

5) Use options dataflow.apps[n].host_df TEXT , trigger.host_trigger TEXT , hsi.host_hsi TEXT to specify different hosts for the different applications (processes). The default for any unspecified host options will be localhost

6) As described in the nanorc documentation, running nanorc can be done in interactively or in batch mode. For batch mode you can specify a sequence of commands to drive MiniDAQ app, for example run :

nanorc daq_fake03 <partition_name> boot conf start_run 103 wait 60 stop_run shutdown

The start_run <run_number> command specifies the run_number value to be used. Any meaningful combination of commands is allowed. Note that the start_run command includes an automatically-generated enable_triggers command to start the flow of triggers, and the stop_run command includes an automatically-generated disable_triggers command to stop the flow of triggers.

7) Examine the contents of the HDf5 file with commands like the following:

  • h5dump-shared -H -A swtest_run000103_0000_*.hdf5

  • and

  • hdf5_dump.py -p both -f swtest_run000103_0000_*.hdf5

For more on hdf5_dump.py, run hdf5_dump.py -h.

8) Some examples:

slowdown_conf.json (Applying the data slowdown factor from point 4): 

{
"readout": {
  "data_rate_slowdown_factor": 10
}
}
fddaqconf_gen --config slowdown_conf.json daq_fake01

multi_df.json (Running two dataflow apps): 

{
"dataflow":{
"apps": [
{"app_name": "dataflow0", "output_dir": "/data1", "host_df": 127.0.0.1 },
{"app_name": "dataflow1", "output_dir": "/data2", "host_df": 127.0.0.2 }
]
}
}
fddaqconf_gen --config multi_df.json daq_fake02

9) One of the key options is the --detector-readout-map-file, or readout.detector_readout_map_file, which points the configuration generators to a JSON file describing the readout links in the current configuration. It can be generated using this utility from daqconf and edited using this utility.

Last git commit to the markdown source of this page:

Author: jtenavidal

Date: Tue Sep 19 09:58:44 2023 -0400

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