Python trgtools Module

Reading a DUNE-DAQ HDF5 file for the TP, TA, and TC contents can be easily done using the trgtools Python module.

Example

Common Methods

import trgtools
 
tpr = trgtools.TPReader(hdf5_file_name)
 
# Get all the available paths for TPs in this file.
frag_paths = tpr.get_fragment_paths()
 
# Read all fragment paths. Appends results to tpr.tp_data.
tpr.read_all_fragments()
 
# Read only one fragment. Return result and append to tpr.tp_data.
frag0_tps = tpr.read_fragment(frag_paths[0])
 
# Reset tpr.tp_data. Keeps the current fragment paths.
tpr.clear_data()
 
# Set a different list of fragment paths.
tpr.set_fragment_paths(tpr.get_fragment_paths()[:4])
 
# Reset the fragment paths to the initalized state.
tpr.reset_fragment_paths()

Data Accessing

tpr = trgtools.TPReader(hdf5_file_name)
tar = trgtools.TAReader(hdf5_file_name)
tcr = trgtools.TCReader(hdf5_file_name)
 
tpr.read_all_fragments()
tar.read_all_fragments()
tcr.read_all_fragments()
 
# Primary contents of the fragments
# np.ndarray with each index as one T*
print(tpr.tp_data)
print(tar.ta_data)
print(tcr.tc_data)
 
# Secondary contents of the fragments
# List with each index as the TPs/TAs in the TA/TC
print(tar.tp_data)
print(tcr.ta_data)
 
ta0_contents = tar.tp_data[0]
tc0_contents = tcr.ta_data[0]

Data accessing follows a very similar procedure between the different readers. The TAReader and TCReader also contain the secondary information about the TPs and TAs that formed the TAs and TCs, respectively. For the np.ndarray objects, one can also specify the member data they want to access. For example,

tar.ta_data['time_start']  # Returns a np.ndarray of the time_starts for all read TAs

The available data members for each reader can be used (and shown) with tpr.tp_dt, tar.ta_dt and tar.tp_dt, and tcr.tc_dt and tcr.ta_dt.

Look at the contents of *_dump.py for more detailed examples of data member usage.

While using interactive Python, one can do help(tpr) and help(tpr.read_fragment) for documentation on their usage (and similarly for the other readers and plotters).

One can also access the primary data by indexing the reader class itself. For example,

tar[0]             # Returns the 0-th TA that was read.
tar['time_start']  # Returns the `time_start` values for all TAs read.
len(tar)           # Returns the number of TAs read.

It is still necessary to specify the secondary data, e.g., tar.tp_data[0] for TPs in the 0-th TA or ‘tcr.ta_data[10]['adc_integral’]` for the TA ADC integrals in the 10-th TC.

Plotting

There is also a submodule trgtools.plot that features a class PDFPlotter. This class contains common plotting that was repeated between the *_dump.py. Loading this class requires matplotlib to be installed, but simply doing import trgtools does not have this requirement.

Example

import trgtools
from trgtools.plot import PDFPlotter
 
tpr = trgtools.TPReader(file_to_read)
 
pdf_save_name = 'example.pdf'
pdf_plotter = PDFPlotter(pdf_save_name)
 
plot_style_dict = dict(title="ADC Peak Histogram", xlabel="ADC Counts", ylabel="Count")
pdf_plotter.plot_histogram(tpr['adc_peak'], plot_style_dict)

By design, the plot_style_dict requires the keys title, xlabel, and ylabel at a minimum. More options are available to further change the style of the plot, and examples of this are available in the *_dump.py.

Development

The common plots available in PDFPlotter is rather limited right now. At this moment, these plots are sufficient, but more common plotting functions can be added.