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139d95631f270b56b7da8fd69c22481616fb1a4e
giant/tests/test_analysis.py
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lars 139d95631f Add pdg energy/length contribution pie plots 
pdg_contribution_table_pl sums real/generated total deposited energy and
total step_length per pdg species over the whole file (pure lazy polars
group_by, no post_pos reconstruction needed for these scalars). Adds
plot_pdg_energy_share/plot_pdg_length_share, each rendering two pies
(real vs generated) so the per-species breakdown can be compared directly,
plus a matching notebook section.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-22 16:55:51 +02:00

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import matplotlib
matplotlib.use("Agg") # no display needed for plot smoke tests
import numpy as np
import polars as pl
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from giant.analysis import (
RAW_TARGET_NAMES,
SampleCollection,
compute_event_observables_pl,
constraint_report,
constraint_report_pl,
correlation_matrices,
direction_alignment,
load_predicted_local,
marginal_table,
marginal_table_pl,
pdg_contribution_table_pl,
plot_constraint_violations,
plot_correlation_matrices,
plot_direction_alignment,
plot_kl_bars,
plot_kl_bars_pl,
plot_longitudinal_profile,
plot_marginals,
plot_pairwise,
plot_pdg_energy_share,
plot_pdg_length_share,
plot_shower_max_depth,
plot_total_energy,
plot_total_length,
plot_transverse_profile,
)
from giant.constants import (
LOCAL_TARGET_NAMES,
PREDICT_COORD_METADATA_KEY,
PREDICT_SCHEMA_VERSION,
PREDICT_SCHEMA_VERSION_KEY,
)
from giant.data.transforms import inv_log_transform, log_transform, reconstruct_post_pos
def _unit_vectors(rng, n):
v = rng.standard_normal((n, 3)).astype(np.float32)
return v / np.linalg.norm(v, axis=1, keepdims=True)
def _make_collection(n=200, seed=0, gen_offset=0.0) -> SampleCollection:
rng = np.random.default_rng(seed)
real = np.column_stack(
[
rng.uniform(0.1, 5.0, n), # step_length
rng.uniform(0.1, 5.0, n), # delta_e
rng.uniform(0.1, 5.0, n), # edep
_unit_vectors(rng, n), # post_dir
_unit_vectors(rng, n), # travel_dir
]
).astype(np.float32)
gen = real + gen_offset
pre_E = rng.uniform(1.0, 100.0, n).astype(np.float32)
cond_cont_raw = np.column_stack(
[
rng.standard_normal((n, 3)),
pre_E,
rng.standard_normal((n, 3)),
rng.integers(0, 5, n),
rng.integers(0, 3, n),
]
).astype(np.float32)
return SampleCollection(
cond_cont_raw=cond_cont_raw,
pdg=rng.choice([11, -11, 22], size=n),
material=rng.choice(["W", "Pb"], size=n),
real_raw=real,
gen_raw=gen,
real_norm=real,
gen_norm=gen,
)
def test_marginal_table_aggregate_has_all_dims():
table = marginal_table(_make_collection())
assert set(table["dim"]) == set(RAW_TARGET_NAMES)
assert (table["group"] == "all").all()
@pytest.mark.parametrize("group_by", ["pdg", "material", "energy"])
def test_marginal_table_grouped_covers_all_rows(group_by):
collection = _make_collection()
table = marginal_table(collection, group_by=group_by)
assert table["n"].groupby(table["group"]).first().sum() == len(collection.pdg)
def test_marginal_table_identical_distributions_have_zero_kl():
collection = _make_collection(gen_offset=0.0)
table = marginal_table(collection)
np.testing.assert_allclose(table["kl_real_gen"], 0.0, atol=1e-6)
def test_marginal_table_shifted_distribution_has_positive_kl():
collection = _make_collection(gen_offset=3.0)
table = marginal_table(collection)
assert (table["kl_real_gen"] > 0).all()
def test_correlation_matrices_are_symmetric_unit_diagonal():
real_corr, gen_corr = correlation_matrices(_make_collection())
for corr in (real_corr, gen_corr):
np.testing.assert_allclose(np.diag(corr), 1.0, atol=1e-5)
np.testing.assert_allclose(corr, corr.T, atol=1e-5)
def test_direction_alignment_real_data_is_unit_norm_dot_product():
real_cos, gen_cos = direction_alignment(_make_collection())
assert np.all(real_cos >= -1.0 - 1e-5) and np.all(real_cos <= 1.0 + 1e-5)
assert np.all(gen_cos >= -1.0 - 1e-5) and np.all(gen_cos <= 1.0 + 1e-5)
def test_constraint_report_clean_data_has_no_violations():
report = constraint_report(_make_collection(gen_offset=0.0))
assert (report["violation_rate"] == 0.0).all()
def test_constraint_report_flags_negative_log_dims_and_bad_norms():
collection = _make_collection(gen_offset=0.0)
collection.gen_raw[:, 0] = -1.0 # negative step_length
collection.gen_raw[:, 3:6] *= 2.0 # post_dir no longer unit norm
report = constraint_report(collection)
violations = dict(zip(report["check"], report["violation_rate"]))
assert violations["step_length >= 0"] == 1.0
assert violations["post_dir unit norm"] == 1.0
def test_plot_marginals_runs_without_error():
fig = plot_marginals(_make_collection())
assert fig is not None
def test_plot_marginals_grouped_runs_without_error():
fig = plot_marginals(_make_collection(), group_by="material")
assert fig is not None
def test_plot_correlation_matrices_runs_without_error():
fig = plot_correlation_matrices(_make_collection())
assert fig is not None
def test_plot_pairwise_runs_without_error():
fig = plot_pairwise(_make_collection())
assert fig is not None
def test_plot_direction_alignment_runs_without_error():
fig = plot_direction_alignment(_make_collection())
assert fig is not None
def test_plot_constraint_violations_runs_without_error():
fig = plot_constraint_violations(_make_collection())
assert fig is not None
def test_plot_kl_bars_runs_without_error():
fig = plot_kl_bars(_make_collection())
assert fig is not None
def test_plot_kl_bars_grouped_runs_without_error():
fig = plot_kl_bars(_make_collection(), group_by="material")
assert fig is not None
def test_plot_kl_bars_caps_groups_by_pdg():
collection = _make_collection(n=600)
collection.pdg = np.arange(600) % 8 # 8 distinct pdg values, > max_groups
fig = plot_kl_bars(collection, group_by="pdg", max_groups=3)
ax = fig.axes[0]
assert len({line.get_label() for line in ax.containers}) <= 3
def _write_predicted_local_parquet(path, n=50, metadata=None, rng=None):
"""Mimic `giant predict --coord local`'s output schema for the loader tests."""
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)
)
pred_log_local = true_log_local + rng.normal(0, 0.01, (n, 9)).astype(np.float32)
table = pa.table(
{
"event_id": rng.integers(0, 10, n),
"pdg": rng.choice([11, -11, 22], n),
"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_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),
"material": rng.choice(["W", "Pb"], n),
"layer_id": rng.integers(0, 10, n).astype(np.int32),
"n_sec": rng.integers(0, 3, n).astype(np.int32),
**{
f"pred_{name}": pred_log_local[:, j]
for j, name in enumerate(LOCAL_TARGET_NAMES)
},
**{
f"true_{name}": true_log_local[:, j]
for j, name in enumerate(LOCAL_TARGET_NAMES)
},
}
)
if metadata is not None:
table = table.replace_schema_metadata(metadata)
pq.write_table(table, path)
return true_log_local, pred_log_local
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(
path,
metadata={
PREDICT_COORD_METADATA_KEY: "local",
PREDICT_SCHEMA_VERSION_KEY: PREDICT_SCHEMA_VERSION,
},
)
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)
assert collection.real_norm is None
assert collection.gen_norm is None
def test_load_predicted_local_usable_by_downstream_plots(tmp_path):
path = tmp_path / "predicted_local.parquet"
_write_predicted_local_parquet(
path,
metadata={
PREDICT_COORD_METADATA_KEY: "local",
PREDICT_SCHEMA_VERSION_KEY: PREDICT_SCHEMA_VERSION,
},
)
collection = load_predicted_local(path)
assert marginal_table(collection) is not None
assert plot_marginals(collection) is not None
def test_load_predicted_local_rejects_missing_metadata(tmp_path):
path = tmp_path / "no_metadata.parquet"
_write_predicted_local_parquet(path, metadata=None)
with pytest.raises(ValueError, match="no '.*' parquet metadata"):
load_predicted_local(path)
def test_load_predicted_local_rejects_global_coord(tmp_path):
path = tmp_path / "global.parquet"
_write_predicted_local_parquet(
path,
metadata={
PREDICT_COORD_METADATA_KEY: "global",
PREDICT_SCHEMA_VERSION_KEY: PREDICT_SCHEMA_VERSION,
},
)
with pytest.raises(ValueError, match="coord=local"):
load_predicted_local(path)
def test_load_predicted_local_rejects_mismatched_schema_version(tmp_path):
path = tmp_path / "old_version.parquet"
_write_predicted_local_parquet(
path,
metadata={
PREDICT_COORD_METADATA_KEY: "local",
PREDICT_SCHEMA_VERSION_KEY: "999",
},
)
with pytest.raises(ValueError, match="schema version"):
load_predicted_local(path)
def _predicted_local_path(tmp_path, n=200, seed=0):
path = tmp_path / "predicted_local.parquet"
_write_predicted_local_parquet(
path,
n=n,
rng=np.random.default_rng(seed),
metadata={
PREDICT_COORD_METADATA_KEY: "local",
PREDICT_SCHEMA_VERSION_KEY: PREDICT_SCHEMA_VERSION,
},
)
return path
@pytest.mark.parametrize("group_by", [None, "pdg", "material", "energy"])
def test_marginal_table_pl_matches_numpy_version(tmp_path, group_by):
path = _predicted_local_path(tmp_path)
collection = load_predicted_local(path)
expected = marginal_table(collection, group_by=group_by).sort_values(
["group", "dim"]
)
actual = (
marginal_table_pl(path, group_by=group_by).sort(["group", "dim"]).to_pandas()
)
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"]:
np.testing.assert_allclose(
expected[col].to_numpy(),
actual[col].to_numpy(),
atol=1e-4,
rtol=1e-4,
)
@pytest.mark.parametrize("group_by", [None, "pdg", "material", "energy"])
def test_plot_kl_bars_pl_runs_without_error(tmp_path, group_by):
path = _predicted_local_path(tmp_path)
fig = plot_kl_bars_pl(path, group_by=group_by)
assert fig is not None
def test_marginal_table_pl_rejects_missing_metadata(tmp_path):
path = tmp_path / "no_metadata.parquet"
_write_predicted_local_parquet(path, metadata=None)
with pytest.raises(ValueError, match="no '.*' parquet metadata"):
marginal_table_pl(path)
def test_constraint_report_pl_matches_numpy_version(tmp_path):
path = _predicted_local_path(tmp_path)
collection = load_predicted_local(path)
expected = constraint_report(collection)
actual = constraint_report_pl(path).to_pandas()
assert list(expected["check"]) == list(actual["check"])
np.testing.assert_allclose(
expected["violation_rate"].to_numpy(),
actual["violation_rate"].to_numpy(),
atol=1e-6,
)
np.testing.assert_allclose(
expected["mean_abs_error"].to_numpy(),
actual["mean_abs_error"].to_numpy(),
atol=1e-4,
)
# ---------------------------------------------------------------------------
# Tier 4: event-level (shower) observables
# ---------------------------------------------------------------------------
def _make_event_level_arrays(rng):
"""3 events (3/2/4 steps), each with an unambiguous highest-pre_E row.
The forced max-pre_E rows (indices 1, 3, 7) fix a known shower axis/entry
point per event, so the expected event_table can be re-derived
independently in the test without depending on compute_event_observables_pl.
"""
event_id = np.array([0, 0, 0, 1, 1, 2, 2, 2, 2], dtype=np.int64)
n = len(event_id)
pre_pos = rng.uniform(-5.0, 5.0, (n, 3)).astype(np.float32)
pre_dir = _unit_vectors(rng, n)
pre_E = rng.uniform(1.0, 50.0, n).astype(np.float32)
pre_E[1] = 100.0 # event 0's entry step
pre_E[3] = 100.0 # event 1's entry step
pre_E[7] = 100.0 # event 2's entry step
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[:, 3:6] = _unit_vectors(rng, n)
block[:, 6:9] = _unit_vectors(rng, n)
return block
true_log_local = _local_block()
pred_log_local = _local_block()
return event_id, pre_pos, pre_dir, pre_E, true_log_local, pred_log_local
def _write_event_level_parquet(path, rng=None):
rng = rng or np.random.default_rng(7)
event_id, pre_pos, pre_dir, pre_E, true_log_local, pred_log_local = (
_make_event_level_arrays(rng)
)
n = len(event_id)
pdg = rng.choice([11, -11, 22], n)
table = pa.table(
{
"event_id": event_id,
"pdg": pdg,
"pre_x": pre_pos[:, 0],
"pre_y": pre_pos[:, 1],
"pre_z": pre_pos[:, 2],
"pre_E": pre_E,
"pre_dx": pre_dir[:, 0],
"pre_dy": pre_dir[:, 1],
"pre_dz": pre_dir[:, 2],
"material": rng.choice(["W", "Pb"], n),
"layer_id": rng.integers(0, 10, n).astype(np.int32),
"n_sec": rng.integers(0, 3, n).astype(np.int32),
**{
f"pred_{name}": pred_log_local[:, j]
for j, name in enumerate(LOCAL_TARGET_NAMES)
},
**{
f"true_{name}": true_log_local[:, j]
for j, name in enumerate(LOCAL_TARGET_NAMES)
},
}
)
table = table.replace_schema_metadata(
{
PREDICT_COORD_METADATA_KEY: "local",
PREDICT_SCHEMA_VERSION_KEY: PREDICT_SCHEMA_VERSION,
}
)
pq.write_table(table, path)
return event_id, pre_pos, pre_dir, pre_E, true_log_local, pred_log_local, pdg
def _expected_event_table(
event_id, pre_pos, pre_dir, pre_E, true_log_local, pred_log_local
):
"""Independent re-derivation of total/centroid/RMS per event, for comparison."""
expected = {}
for e in sorted(np.unique(event_id).tolist()):
mask = event_id == e
entry_idx = np.where(mask)[0][np.argmax(pre_E[mask])]
entry_pos = pre_pos[entry_idx]
axis_dir = pre_dir[entry_idx]
def agg(log_local):
step_length = inv_log_transform(log_local[mask, 0])
edep = inv_log_transform(log_local[mask, 2])
travel_dir_local = log_local[mask, 6:9]
post_pos = reconstruct_post_pos(
pre_pos[mask], pre_dir[mask], step_length, travel_dir_local
)
disp = post_pos - entry_pos
depth = disp @ axis_dir
transverse = np.linalg.norm(disp - depth[:, None] * axis_dir, axis=1)
total_edep = float(edep.sum())
total_length = float(step_length.sum())
centroid = float((edep * depth).sum() / total_edep)
rms = float(np.sqrt((edep * transverse**2).sum() / total_edep))
return total_edep, total_length, centroid, rms
real_total_edep, real_total_length, real_centroid, real_rms = agg(
true_log_local
)
gen_total_edep, gen_total_length, gen_centroid, gen_rms = agg(pred_log_local)
expected[e] = (
real_total_edep,
gen_total_edep,
real_total_length,
gen_total_length,
real_centroid,
gen_centroid,
real_rms,
gen_rms,
)
return expected
def test_compute_event_observables_pl_matches_manual_reconstruction(tmp_path):
path = tmp_path / "event_level.parquet"
event_id, pre_pos, pre_dir, pre_E, true_log_local, pred_log_local, _pdg = (
_write_event_level_parquet(path)
)
expected = _expected_event_table(
event_id, pre_pos, pre_dir, pre_E, true_log_local, pred_log_local
)
obs = compute_event_observables_pl(path, depth_bins=5, transverse_bins=5)
table = obs.event_table.sort("event_id")
for i, eid in enumerate(table["event_id"].to_list()):
(
real_total_edep,
gen_total_edep,
real_total_length,
gen_total_length,
real_centroid,
gen_centroid,
real_rms,
gen_rms,
) = expected[eid]
np.testing.assert_allclose(
table["real_total_edep"][i], real_total_edep, rtol=1e-4
)
np.testing.assert_allclose(
table["gen_total_edep"][i], gen_total_edep, rtol=1e-4
)
np.testing.assert_allclose(
table["real_total_length"][i], real_total_length, rtol=1e-4
)
np.testing.assert_allclose(
table["gen_total_length"][i], gen_total_length, rtol=1e-4
)
np.testing.assert_allclose(
table["real_centroid_depth"][i], real_centroid, rtol=1e-3, atol=1e-4
)
np.testing.assert_allclose(
table["gen_centroid_depth"][i], gen_centroid, rtol=1e-3, atol=1e-4
)
np.testing.assert_allclose(
table["real_transverse_rms"][i], real_rms, rtol=1e-3, atol=1e-4
)
np.testing.assert_allclose(
table["gen_transverse_rms"][i], gen_rms, rtol=1e-3, atol=1e-4
)
def test_compute_event_observables_pl_profile_shapes(tmp_path):
path = tmp_path / "event_level.parquet"
_write_event_level_parquet(path)
obs = compute_event_observables_pl(path, depth_bins=7, transverse_bins=4)
assert obs.depth_edges.shape == (8,)
assert obs.transverse_edges.shape == (5,)
assert obs.real_depth_profile.shape == (7,)
assert obs.gen_depth_profile.shape == (7,)
assert obs.real_transverse_profile.shape == (4,)
assert obs.gen_transverse_profile.shape == (4,)
assert len(obs.event_table) == 3
def test_compute_event_observables_pl_accepts_lazyframe(tmp_path):
path = tmp_path / "event_level.parquet"
_write_event_level_parquet(path)
obs_from_path = compute_event_observables_pl(path, depth_bins=5, transverse_bins=5)
obs_from_lf = compute_event_observables_pl(
pl.scan_parquet(path), depth_bins=5, transverse_bins=5
)
np.testing.assert_allclose(
obs_from_lf.event_table.sort("event_id")["real_total_edep"].to_numpy(),
obs_from_path.event_table.sort("event_id")["real_total_edep"].to_numpy(),
)
def test_event_level_plots_run_without_error(tmp_path):
path = tmp_path / "event_level.parquet"
_write_event_level_parquet(path)
obs = compute_event_observables_pl(path, depth_bins=5, transverse_bins=5)
assert plot_total_energy(obs) is not None
assert plot_total_length(obs) is not None
assert plot_longitudinal_profile(obs) is not None
assert plot_transverse_profile(obs) is not None
assert plot_shower_max_depth(obs) is not None
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)
real_edep = inv_log_transform(true_log_local[:, 2])
gen_edep = inv_log_transform(pred_log_local[:, 2])
real_length = inv_log_transform(true_log_local[:, 0])
gen_length = inv_log_transform(pred_log_local[:, 0])
expected = {}
for p in np.unique(pdg):
mask = pdg == p
expected[int(p)] = (
float(real_edep[mask].sum()),
float(gen_edep[mask].sum()),
float(real_length[mask].sum()),
float(gen_length[mask].sum()),
)
table = pdg_contribution_table_pl(path).sort("pdg")
for i, p in enumerate(table["pdg"].to_list()):
real_e, gen_e, real_l, gen_l = expected[int(p)]
np.testing.assert_allclose(table["real_total_edep"][i], real_e, rtol=1e-4)
np.testing.assert_allclose(table["gen_total_edep"][i], gen_e, rtol=1e-4)
np.testing.assert_allclose(table["real_total_length"][i], real_l, rtol=1e-4)
np.testing.assert_allclose(table["gen_total_length"][i], gen_l, rtol=1e-4)
def test_pdg_contribution_table_pl_accepts_lazyframe(tmp_path):
path = tmp_path / "event_level.parquet"
_write_event_level_parquet(path)
from_path = pdg_contribution_table_pl(path).sort("pdg")
from_lf = pdg_contribution_table_pl(pl.scan_parquet(path)).sort("pdg")
np.testing.assert_allclose(
from_lf["real_total_edep"].to_numpy(), from_path["real_total_edep"].to_numpy()
)
def test_pdg_pie_plots_run_without_error(tmp_path):
path = tmp_path / "event_level.parquet"
_write_event_level_parquet(path)
table = pdg_contribution_table_pl(path)
assert plot_pdg_energy_share(table) is not None
assert plot_pdg_length_share(table) is not None
def test_plot_pdg_energy_share_caps_slices():
table = pl.DataFrame(
{
"pdg": list(range(10)),
"real_total_edep": [float(10 - i) for i in range(10)],
"gen_total_edep": [float(10 - i) for i in range(10)],
"real_total_length": [float(10 - i) for i in range(10)],
"gen_total_length": [float(10 - i) for i in range(10)],
}
)
fig = plot_pdg_energy_share(table, max_slices=4)
for ax in fig.axes:
assert len(ax.patches) == 4
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