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https://github.com/TREX-CoE/Sherman-Morrison.git
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d81777e347
Since the dataset must be accessible from the CI runner, the best solution is probably to commit a small dataset containing only the required cycles. It's included in this commit, and can be generated by extract-from-h5.py using the same cycles list as the one used by vfc_test_h5.cpp. Moreover, the probes exported by vfc_test_h5.cpp are now 0-padded, which will result in a better sorting in the results.
73 lines
1.6 KiB
Python
73 lines
1.6 KiB
Python
#!/usr/bin/env python
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import h5py
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import sys
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# Helper script to extract a few cycles from a large dataset. This will be
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# especially useful for the Verificarlo CI, since vfc_ci_cycles.txt can be
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# used to both extract (with this script), and read the small dataset (in a CI
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# run).
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# Parse arguments
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if len(sys.argv) != 4:
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sys.stderr.write(
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"Error: Wrong number of arguments. Usage : extract_h5.py "\
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"<source_dataset.hdf5> <cycles_list.txt> <destination_dataset.hdf5>\n"
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)
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exit(1)
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source_dataset_path = sys.argv[1]
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cycles_list_path = sys.argv[2]
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destination_dataset_path = sys.argv[3]
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# Read the cycles list
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cycles_list = []
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try:
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f = open(cycles_list_path)
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for line in f:
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cycles_list.extend([cycle for cycle in line.split()])
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except IOError:
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sys.stderr.write("Error: Could not read " + cycles_list_path + "\n")
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exit(1)
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finally:
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f.close()
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# Read the source dataset, and extract the cycles to the destination dataset
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try:
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fs = h5py.File(source_dataset_path, "r")
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except IOError:
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sys.stderr.write("Error: Could not read " + source_dataset_path + "\n")
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exit(1)
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fd = h5py.File(destination_dataset_path, "w")
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# Copy cycles groups
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groups = [
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"slater_matrix_dim",
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"nupdates",
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"slater_matrix",
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"slater_inverse",
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"col_update_index",
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"updates"
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]
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for cycle in cycles_list:
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cycle_name = "cycle_" + cycle
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new_cycle = fd.create_group(cycle_name)
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# Copy all datasets
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for group_name in groups:
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fs.copy(cycle_name + "/" + group_name, new_cycle)
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print("Dataset successfully exported to %s" % source_dataset_path)
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