A package for reading tpx3 files to Awkward Arrays
- Free software: 3-clause BSD license
- Documentation: (COMING SOON!) https://nsls-ii.github.io/tpx3awkward.
- TODO
- https://github.com/svihra/TimePix3/blob/master/dataprocess.cpp
- https://github.com/emx77/tpx3-to-root/blob/master/tpx3_to_root.cpp
This example validates against the CSV output
from pathlib import Path
import pandas as pd
from tpx3awkward._utils import raw_as_numpy, ingest_raw_data
data_dir = Path('/some/path/')
tmp = []
for j in range(16):
csv = data_dir / f'analysis/Test2-68mA-cont/frames_000000-{j}.csv'
tmp.append(pd.read_csv(csv))
ref = pd.concat(tmp).reset_index()
fname = data_dir / 'analysis/Test2-68mA-cont/frames_000000.tpx3'
d = raw_as_numpy(fname)
test = pd.DataFrame(ingest_raw_data(d)).reset_index()
test_sorted = test.sort_values(['timestamp', 'ToT', 'x', 'y'])
ref_sorted = ref.sort_values(['#ToA', '#ToT[arb]', '#Col', '#Row'])
assert (test_sorted['x'].values == ref_sorted['#Col'].values).all()
assert (test_sorted['y'].values == ref_sorted['#Row'].values).all()
assert (test_sorted['ToT'].values == ref_sorted['#ToT[arb]'].values).all()
assert (test_sorted['timestamp'].values == ref_sorted['#ToA'].values).all()The sorting step is needed to compare because the roll-over does not appear to be correctly handled by either code (which follows because this code has the exact same logic as the c++ code we were adapting). However, the c++ code sorts the photons by ToA in batches. This means that before and after the roll over the times are monotonic and the some of the photons in the sort batch will come out shuffled.
Recreating the "sort by batch" is not worth the trouble, so fully sort things by ToA, then ToT, then pixels location (to ensure that the sorting is stable, there are events with identical times).