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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

tp_data = trgtools.TPReader(hdf5_file_name)

# Get all the available paths for TPs in this file.
frag_paths = tp_data.get_fragment_paths()

# Read all fragment paths. Appends results to tp_data.tp_data.
tp_data.read_all_fragments()

# Read only one fragment. Return result and append to tp_data.tp_data.
frag0_tps = tp_data.read_fragment(frag_paths[0])

# Reset tp_data.tp_data. Keeps the current fragment paths.
tp_data.clear_data()

# Reset the fragment paths to the initalized state.
tp_data.reset_fragment_paths()

Data Accessing

tp_data = trgtools.TPReader(hdf5_file_name)
ta_data = trgtools.TAReader(hdf5_file_name)
tc_data = trgtools.TCReader(hdf5_file_name)

tp_data.read_all_fragments()
ta_data.read_all_fragments()
tc_data.read_all_fragments()

# Primary contents of the fragments
# np.ndarray with each index as one T*
tp_data.tp_data
ta_data.ta_data
tc_data.tc_data

# Secondary contents of the fragments
# List with each index as the TPs/TAs in the TA/TC
ta_data.tp_data
tc_data.ta_data

ta0_contents = ta_data.tp_data[0]
tc0_contents = tc_data.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, 
ta_data.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 tp_data.tp_dt, ta_data.ta_dt and ta_data.tp_dt, and tc_data.tc_dt and tc_data.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(tp_data) and help(tp_data.read_fragment) for documentation on their usage (and similarly for the other readers and plotters).

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

tp_data = 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(tp_data['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.

Last git commit to the markdown source of this page:

Author: aeoranday

Date: Tue Feb 27 17:46:11 2024 +0100

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