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Encode edep/secondary/post energy as a conservation-constrained simplex

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Replaces the independent log_delta_e/log_edep targets with 2 additive-log-ratio
coordinates over the deposit/secondary/post-energy simplex (fractions of pre_E
summing to 1), so edep + e_sec + post_E == pre_E holds by construction after
decoding (softmax) rather than being learned approximately. Requires e_sec
(secondary energy) as a new conditioning input and a steps_to_parquet.py pass
to derive it from child track first-step energies.
This commit is contained in:
lars
2026-06-25 16:01:13 +02:00
parent 64c6bd1cef
commit 8475199609
13 changed files with 415 additions and 83 deletions
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tests/test_analysis.py
+50 -20
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@@ -41,7 +41,12 @@ from giant.constants import (
PREDICT_SCHEMA_VERSION,
PREDICT_SCHEMA_VERSION_KEY,
)
from giant.data.transforms import inv_log_transform, log_transform, reconstruct_post_pos
from giant.data.transforms import (
energy_simplex_decode,
inv_log_transform,
log_transform,
reconstruct_post_pos,
)
def _unit_vectors(rng, n):
@@ -186,13 +191,17 @@ def test_plot_kl_bars_caps_groups_by_pdg():
def _write_predicted_local_parquet(path, n=50, metadata=None, rng=None):
"""Mimic `giant predict --coord local`'s output schema for the loader tests."""
"""Mimic `giant predict --coord local`'s output schema for the loader tests.
Column 0 is a log-scaled step_length; columns 1–2 are the deposit/secondary
ALR energy logits (unconstrained reals, decoded against pre_E); columns 3–8
are direction components.
"""
rng = rng or np.random.default_rng(0)
true_log_local = rng.standard_normal((n, 9)).astype(np.float32)
true_log_local[:, :3] = log_transform(
rng.uniform(0.1, 5.0, (n, 3)).astype(np.float32)
)
true_log_local[:, 0] = log_transform(rng.uniform(0.1, 5.0, n).astype(np.float32))
pred_log_local = true_log_local + rng.normal(0, 0.01, (n, 9)).astype(np.float32)
pre_E = rng.uniform(1.0, 100.0, n).astype(np.float32)
table = pa.table(
{
@@ -201,7 +210,7 @@ def _write_predicted_local_parquet(path, n=50, metadata=None, rng=None):
"pre_x": rng.standard_normal(n).astype(np.float32),
"pre_y": rng.standard_normal(n).astype(np.float32),
"pre_z": rng.standard_normal(n).astype(np.float32),
"pre_E": rng.uniform(1.0, 100.0, n).astype(np.float32),
"pre_E": pre_E,
"pre_dx": rng.standard_normal(n).astype(np.float32),
"pre_dy": rng.standard_normal(n).astype(np.float32),
"pre_dz": rng.standard_normal(n).astype(np.float32),
@@ -221,12 +230,12 @@ def _write_predicted_local_parquet(path, n=50, metadata=None, rng=None):
if metadata is not None:
table = table.replace_schema_metadata(metadata)
pq.write_table(table, path)
return true_log_local, pred_log_local
return true_log_local, pred_log_local, pre_E
def test_load_predicted_local_round_trips_values(tmp_path):
path = tmp_path / "predicted_local.parquet"
true_log_local, pred_log_local = _write_predicted_local_parquet(
true_log_local, pred_log_local, pre_E = _write_predicted_local_parquet(
path,
metadata={
PREDICT_COORD_METADATA_KEY: "local",
@@ -236,12 +245,20 @@ def test_load_predicted_local_round_trips_values(tmp_path):
collection = load_predicted_local(path)
expected_real = true_log_local.copy()
expected_real[:, :3] = np.exp(expected_real[:, :3]) - 1e-8
expected_gen = pred_log_local.copy()
expected_gen[:, :3] = np.exp(expected_gen[:, :3]) - 1e-8
np.testing.assert_allclose(collection.real_raw, expected_real, atol=1e-4)
np.testing.assert_allclose(collection.gen_raw, expected_gen, atol=1e-4)
def expected_raw(log_local):
raw = log_local.copy()
raw[:, 0] = np.exp(log_local[:, 0]) - 1e-8
edep, _e_sec, _post_E, delta_e = energy_simplex_decode(log_local[:, 1:3], pre_E)
raw[:, 1] = delta_e
raw[:, 2] = edep
return raw
np.testing.assert_allclose(
collection.real_raw, expected_raw(true_log_local), atol=1e-4
)
np.testing.assert_allclose(
collection.gen_raw, expected_raw(pred_log_local), atol=1e-4
)
assert collection.real_norm is None
assert collection.gen_norm is None
@@ -321,13 +338,21 @@ def test_marginal_table_pl_matches_numpy_version(tmp_path, group_by):
assert list(expected["group"]) == list(actual["group"])
assert list(expected["n"]) == list(actual["n"])
for col in ["real_mean", "gen_mean", "real_std", "gen_std", "kl_real_gen"]:
for col in ["real_mean", "gen_mean", "real_std", "gen_std"]:
np.testing.assert_allclose(
expected[col].to_numpy(),
actual[col].to_numpy(),
atol=1e-4,
rtol=1e-4,
)
# KL uses np.histogram (numpy path) vs polars Series.hist (lazy path); the two
# backends bin the boundary (min/max) sample differently, so allow a small
# absolute discrepancy rather than requiring bit-identical estimates.
np.testing.assert_allclose(
expected["kl_real_gen"].to_numpy(),
actual["kl_real_gen"].to_numpy(),
atol=2e-2,
)
@pytest.mark.parametrize("group_by", [None, "pdg", "material", "energy"])
@@ -387,7 +412,8 @@ def _make_event_level_arrays(rng):
def _local_block():
block = rng.standard_normal((n, 9)).astype(np.float32)
block[:, :3] = log_transform(rng.uniform(0.1, 5.0, (n, 3)).astype(np.float32))
block[:, 0] = log_transform(rng.uniform(0.1, 5.0, n).astype(np.float32))
# cols 1–2 stay as random ALR energy logits (decoded against pre_E)
block[:, 3:6] = _unit_vectors(rng, n)
block[:, 6:9] = _unit_vectors(rng, n)
return block
@@ -451,7 +477,9 @@ def _expected_event_table(
def agg(log_local):
step_length = inv_log_transform(log_local[mask, 0])
edep = inv_log_transform(log_local[mask, 2])
edep, _e_sec, _post_E, _delta_e = energy_simplex_decode(
log_local[mask, 1:3], pre_E[mask]
)
travel_dir_local = log_local[mask, 6:9]
post_pos = reconstruct_post_pos(
pre_pos[mask], pre_dir[mask], step_length, travel_dir_local
@@ -574,10 +602,12 @@ def test_event_level_plots_run_without_error(tmp_path):
def test_pdg_contribution_table_pl_matches_manual_sums(tmp_path):
path = tmp_path / "event_level.parquet"
_, _, _, _, true_log_local, pred_log_local, pdg = _write_event_level_parquet(path)
_, _, _, pre_E, true_log_local, pred_log_local, pdg = _write_event_level_parquet(
path
)
real_edep = inv_log_transform(true_log_local[:, 2])
gen_edep = inv_log_transform(pred_log_local[:, 2])
real_edep = energy_simplex_decode(true_log_local[:, 1:3], pre_E)[0]
gen_edep = energy_simplex_decode(pred_log_local[:, 1:3], pre_E)[0]
real_length = inv_log_transform(true_log_local[:, 0])
gen_length = inv_log_transform(pred_log_local[:, 0])