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MoritzNeubergerMoritzNeuberger
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First attempt at reducing scope to refocus tests to validate data. Additionally, also a lot of uncluttering.
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‎python/remage/utils.py‎

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from collections.abc import Iterable
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2020

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def write_process_table(file_path: str, proc_name_to_id: dict[str, int]) -> None:
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"""Write an LH5 ``processes`` struct from a process-name to id mapping."""
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if not proc_name_to_id:
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return
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import numpy as np
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from lgdo import Scalar, Struct, lh5
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process_struct = Struct(
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{name: Scalar(np.int64(pid)) for name, pid in proc_name_to_id.items()}
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)
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lh5.write(process_struct, "processes", file_path, wo_mode="overwrite")
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def canonicalize_process_ids_for_concat(
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lh5_files: list[str],
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*,
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suffix: str = "-canon",
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) -> tuple[list[str], dict[str, int]]:
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"""Create remapped LH5 copies with canonical process ids for safe concatenation.
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This is useful when concatenating simulation outputs generated by different remage instances with different process-name to id mappings.
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Parameters
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----------
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lh5_files
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Input files to canonicalize.
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suffix
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Suffix appended to each copied filename stem.
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Returns
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-------
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remapped_files, canonical_proc_ids
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Paths to remapped copies and canonical process-name to id mapping.
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"""
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if not lh5_files:
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return [], {}
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import shutil
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from pathlib import Path
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from lgdo import Array, lh5
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def _read_process_map(file_path: str) -> dict[str, int]:
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if lh5.ls(file_path, "processes") == []:
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return {}
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procs = lh5.read("processes", file_path)
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return {name: int(proc.value) for name, proc in procs.items()}
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ordered_names = []
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shard_proc_maps = {}
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for lh5_file in lh5_files:
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proc_map = _read_process_map(lh5_file)
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shard_proc_maps[lh5_file] = proc_map
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for name in proc_map:
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if name not in ordered_names:
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ordered_names.append(name)
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canonical_proc_ids = {name: idx for idx, name in enumerate(ordered_names)}
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shard_id_maps = {
81-
lh5_file: {
82-
old_id: canonical_proc_ids[name] for name, old_id in proc_map.items()
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}
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for lh5_file, proc_map in shard_proc_maps.items()
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}
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remapped_files = []
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for lh5_file in lh5_files:
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src_path = Path(lh5_file)
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remapped_file = str(
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src_path.with_name(f"{src_path.stem}{suffix}{src_path.suffix}")
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)
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shutil.copy2(lh5_file, remapped_file)
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if lh5.ls(remapped_file, "tracks") != []:
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tracks = lh5.read("tracks", remapped_file)
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old_procid = tracks["procid"].view_as("np")
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new_procid = old_procid.copy()
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for old_id, new_id in shard_id_maps.get(lh5_file, {}).items():
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new_procid[old_procid == old_id] = new_id
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tracks["procid"] = Array(
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new_procid.astype(old_procid.dtype, copy=False),
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attrs=tracks["procid"].attrs,
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)
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lh5.write(tracks, "tracks", remapped_file, wo_mode="overwrite")
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write_process_table(remapped_file, canonical_proc_ids)
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remapped_files.append(remapped_file)
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return remapped_files, canonical_proc_ids
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def _to_list(thing):
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if not isinstance(thing, tuple | list):
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return [thing]

‎tests/cosmogenics/muon/misc/densities.yaml‎

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lar: 1.396
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water: 1.0
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rock: 2.65

‎tests/cosmogenics/muon/misc/energy_loss.yaml‎

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# taken from https://doi.org/10.1006/adnd.2001.0861
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energy_points:
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- 10
4+
- 14
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- 20
6+
- 30
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- 40
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- 80
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- 100
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- 140
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- 200
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- 300
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- 400
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- 800
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- 1000
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- 1400
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- 2000
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- 3000
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- 4000
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- 8000
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- 10000
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- 14000
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- 20000
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- 30000
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- 40000
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- 80000
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- 100000
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- 140000
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- 200000
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- 300000
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- 400000
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- 800000
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- 1000000
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- 1400000
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- 2000000
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- 3000000
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- 4000000
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- 8000000
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ionization:
40+
water:
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- 7.902
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- 6.166
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- 4.817
44+
- 3.738
45+
- 3.192
46+
- 2.398
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- 2.256
48+
- 2.103
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- 2.015
50+
- 1.981
51+
- 1.987
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- 2.064
53+
- 2.098
54+
- 2.154
55+
- 2.216
56+
- 2.287
57+
- 2.336
58+
- 2.448
59+
- 2.482
60+
- 2.531
61+
- 2.581
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- 2.635
63+
- 2.671
64+
- 2.755
65+
- 2.781
66+
- 2.82
67+
- 2.861
68+
- 2.907
69+
- 2.94
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- 3.019
71+
- 3.045
72+
- 3.084
73+
- 3.127
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- 3.175
75+
- 3.21
76+
- 3.296
77+
lar:
78+
- 5.687
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- 4.461
80+
- 3.502
81+
- 2.731
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- 2.34
83+
- 1.771
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- 1.669
85+
- 1.57
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- 1.518
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- 1.508
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- 1.509
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- 1.526
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- 1.61
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- 1.644
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- 1.699
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- 1.758
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- 1.825
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- 1.87
96+
- 1.973
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- 2.003
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- 2.047
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- 2.091
100+
- 2.137
101+
- 2.167
102+
- 2.236
103+
- 2.258
104+
- 2.289
105+
- 2.322
106+
- 2.36
107+
- 2.386
108+
- 2.404
109+
- 2.451
110+
- 2.472
111+
- 2.503
112+
- 2.538
113+
- 2.577
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- 2.606
115+
- 2.676
116+
total:
117+
water:
118+
- 7.902
119+
- 6.166
120+
- 4.817
121+
- 3.738
122+
- 3.192
123+
- 2.398
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- 2.256
125+
- 2.104
126+
- 2.015
127+
- 1.981
128+
- 1.988
129+
- 2.064
130+
- 2.099
131+
- 2.156
132+
- 2.218
133+
- 2.29
134+
- 2.341
135+
- 2.459
136+
- 2.497
137+
- 2.553
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- 2.615
139+
- 2.69
140+
- 2.749
141+
- 2.931
142+
- 3.009
143+
- 3.156
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- 3.365
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- 3.698
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- 4.028
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- 5.341
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- 6.003
149+
- 7.318
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- 9.315
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- 12.64
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- 16.003
153+
- 29.583
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lar:
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- 5.687
156+
- 4.461
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- 3.502
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- 2.731
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- 2.34
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- 1.771
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- 1.67
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- 1.57
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- 1.519
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- 1.508
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- 1.51
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- 1.526
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- 1.61
168+
- 1.645
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- 1.7
170+
- 1.761
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- 1.829
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- 1.877
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- 1.991
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- 2.028
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- 2.084
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- 2.149
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- 2.232
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- 2.304
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- 2.548
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- 2.663
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- 2.888
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- 3.224
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- 3.774
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- 4.332
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- 4.808
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- 6.585
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- 7.73
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- 10.015
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- 13.494
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- 19.288
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- 25.147
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- 48.769

‎tests/cosmogenics/muon/misc/neutron_yield.yaml‎

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# simulation data digitized from https://doi.org/10.1134/S106377881603011X
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Y_gen:
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lar:
4+
100: 0.0006141876797088565
5+
280: 0.001367709572991316
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water:
7+
100: 0.00012277499957428353
8+
280: 0.00026574153569584104
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rock:
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100: 0.00015301408425936515
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280: 0.00035309281896991977
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energy_dependence: # parameters of the power law fit Y = b * E^n_eff
13+
lar:
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b: 1.71071819e-05
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n_eff: 7.77561314e-01
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water:
17+
b: 3.88324714e-06
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n_eff: 7.49957455e-01
19+
rock:
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b: 3.63446076e-06
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n_eff: 8.12145712e-01

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