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I just time-profiled LH5Store.read(), used to read skimmed files from p03-p08. Attached are the results.
As you can see reading data sets with h5py takes only 50% of the time, and I am wondering if we can improve this. Relevant excerpts from the profiling:
Line # Hits Time Per Hit % Time Line Contents
==============================================================
319 66 5295.0 80.2 4.2 if not h5f or name not in h5f:
320 msg = f"'{name}' not in {h5f.filename}"
321 raise KeyError(msg)
...
337 66 11320.5 171.5 9.0 datatype = h5f[name].attrs["datatype"]
...
338 66 9917.8 150.3 7.9 datatype, shape, elements = parse_datatype(datatype)
...
763 59 7936.4 134.5 6.3 ds_n_rows = h5f[name].shape[0]
...
818 58 62259.3 1073.4 49.4 nda = h5f[name][source_sel]
...
837 58 7681.7 132.4 6.1 attrs = h5f[name].attrs
...
840 58 7633.8 131.6 6.1 return Array(nda=nda, attrs=attrs), n_rows_to_read
I just time-profiled
LH5Store.read()
, used to read skimmed files from p03-p08. Attached are the results.As you can see reading data sets with
h5py
takes only 50% of the time, and I am wondering if we can improve this. Relevant excerpts from the profiling:To reproduce
Install
line_profiler
:test.py
:Run profiling:
Dump results:
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